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teamalphabmsit commited on Dec 29, 2024

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.gitignore +16 -0
LICENSE +21 -0
README.md +131 -8
app.py +196 -0
cache/cache.py +30 -0
cache/oss.py +71 -0
config.yaml +40 -0
conversation.py +453 -0
front_end/css.css +3 -0
front_end/javascript.js +49 -0
front_end/lambda.jpg +0 -0
front_end/user.jpg +0 -0
function_calling.py +697 -0
inspector.py +44 -0
kernel.py +337 -0
knowledge_integration/N_correlation_matrix.py +510 -0
knowledge_integration/knowledge/nn_networks/models.py +130 -0
knowledge_integration/knowledge/nn_networks/train.py +52 -0
knowledge_integration/knowledge/nn_networks/utils.py +76 -0
knowledge_integration/knw.py +96 -0
knowledge_integration/nearest_correlation_matrix.py +528 -0
knowledge_integration/nn_network.py +261 -0
knowledge_integration/pami.py +32 -0
knowledge_integration/test.yaml +20 -0
knw.py +96 -0
knw_in.py +87 -0
lambda_utils.py +320 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/AUTHORS.txt +738 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/INSTALLER +1 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/LICENSE.txt +20 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/METADATA +90 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/RECORD +1003 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/REQUESTED +0 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/WHEEL +5 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/entry_points.txt +4 -0
myenv/Lib/site-packages/pip-23.2.1.dist-info/top_level.txt +1 -0
myenv/Lib/site-packages/pip/__init__.py +13 -0
myenv/Lib/site-packages/pip/__main__.py +24 -0
myenv/Lib/site-packages/pip/__pip-runner__.py +50 -0
myenv/Lib/site-packages/pip/_internal/__init__.py +19 -0
myenv/Lib/site-packages/pip/_internal/build_env.py +311 -0
myenv/Lib/site-packages/pip/_internal/cache.py +292 -0
myenv/Lib/site-packages/pip/_internal/cli/__init__.py +4 -0
myenv/Lib/site-packages/pip/_internal/cli/autocompletion.py +171 -0
myenv/Lib/site-packages/pip/_internal/cli/base_command.py +236 -0
myenv/Lib/site-packages/pip/_internal/cli/cmdoptions.py +1074 -0
myenv/Lib/site-packages/pip/_internal/cli/command_context.py +27 -0
myenv/Lib/site-packages/pip/_internal/cli/main.py +79 -0
myenv/Lib/site-packages/pip/_internal/cli/main_parser.py +134 -0
myenv/Lib/site-packages/pip/_internal/cli/parser.py +294 -0

.gitignore ADDED Viewed

	@@ -0,0 +1,16 @@

+.DS_Store
+.idea
+__pycache__/
+application
+back
+cache/conv_cache
+experiments/
+exported/
+function_calling/
+MNIST/
+utils/
+others/
+test/
+example_config.yaml

LICENSE ADDED Viewed

	@@ -0,0 +1,21 @@

+MIT License
+Copyright (c) 2024 MJ.Sun
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md CHANGED Viewed

@@ -1,12 +1,135 @@
 ---
-title: Project1
-emoji: ⚡
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 5.9.1
 app_file: app.py
-pinned: false
 ---
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

 ---
+title: project1
 app_file: app.py
+sdk: gradio
+sdk_version: 4.43.0
+---
+# [LAMBDA](https://www.polyu.edu.hk/ama/cmfai/lambda.html) - Multi-Agent Data Analysis System
+<body>
+<!-- <img src="https://github.com/user-attachments/assets/df454158-79e4-4da4-ae03-eb687fe02f16" style="width: 80%"> -->
+<!-- <p align="center">
+  <img src="https://github.com/user-attachments/assets/6f6d49ef-40b7-46f2-88ae-b8f6d9719c3a" style="width: 600px;">
+</p> -->
+![lambda_mix](https://github.com/user-attachments/assets/db5574aa-9441-4c9d-b44d-3b225d11e0cc)
+We introduce **LAMBDA**, a novel open-source, code-free multi-agent data analysis system that harnesses the power of large models. LAMBDA is designed to address data analysis challenges in complex data-driven applications through the use of innovatively designed data agents that operate iteratively and generatively using natural language.
+## Key Features
+- **Code-Free Data Analysis**: Perform complex data analysis tasks through human language instruction.
+- **Multi-Agent System**: Utilizes two key agent roles, the programmer and the inspector, to generate and debug code seamlessly.
+- **User Interface**: This includes a robust user interface that allows direct user intervention in the operational loop.
+- **Model Integration**: Flexibly integrates external models and algorithms to cater to customized data analysis needs.
+- **Automatic Report Generation**: Concentrate on high-value tasks, rather than spending time and resources on report writing and formatting.
+## Getting Started
+### Installation
+First, clone the repository.
+```bash
+git clone https://github.com/Stephen-SMJ/LAMBDA.git
+cd LAMBDA
+```
+Then, we recommend creating a [Conda](https://docs.conda.io/en/latest/) environment for this project and install the dependencies by following commands:
+```bash
+conda create -n lambda python=3.10
+conda activate lambda
+```
+Next, you should install the Jupyter kernel to create a local Code Interpreter:
+```bash
+ipython kernel install --name lambda --user
+```
+### Configuration
+1. To use the Large Language Model, you should have an API key from [OpenAI](https://platform.openai.com/docs/guides/authentication) or other companies. Also, you can call your local LLMs once deployed by frameworks such as [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
+2. **We used Aliyun Cloud Server to store the caches (like showing figures, models and so on). Currently, you should buy a [OSS（Object Storage Service](https://cn.aliyun.com/product/oss?from_alibabacloud=) from Aliyun to use it. But we will release a new version without the cloud server for easier use soon.**
+3. Set your API key, models, working path, and OSS-related items in the config.yaml:
+```bash
+#================================================================================================
+#                                       Config of the LLMs
+#================================================================================================
+conv_model : "gpt-4o-mini" # the conversation model
+programmer_model : "gpt-4o-mini"
+inspector_model : "gpt-4o-mini"
+api_key : "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"
+base_url_conv_model : 'https://api.openai.com/v1'
+base_url_programmer : 'https://api.openai.com/v1'
+base_url_inspector : 'htts://api.openai.com/v1'
+max_token_conv_model: 4096 # the max token of the conversation model, this will determine the maximum length of the report.
+#================================================================================================
+#                                       Config of the system
+#================================================================================================
+streaming : True
+#cache_related
+oss_endpoint: ""
+oss_access_key_id: ""
+oss_access_secret: ""
+oss_bucket_name: ""
+expired_time: 36000 # The expired time of the link in cache
+cache_dir : "" # local cache dir
+max_attempts : 5 # The max attempts of self-correcting
+max_exe_time: 18000 # max time for the execution
+#knowledge integration
+retrieval : False # whether to start a knowledge retrieval. If you don't create your knowledge base, you should set it to False
+mode : "full" # the mode of the #knowledge integration
+```
+Finally, Run the following command to start the demo with GUI:
+```bash
+python app.py
+```
+## Demonstration Videos
+The performance of LAMBDA in solving data science problems is demonstrated in several case studies including:
+- **[Data Analysis](https://www.polyu.edu.hk/ama/cmfai/files/lambda/lambda.mp4)**
+- **[Integrating Human Intelligence](https://www.polyu.edu.hk/ama/cmfai/files/lambda/knw.mp4)**
+- **[Education](https://www.polyu.edu.hk/ama/cmfai/files/lambda/LAMBDA_education.mp4)**
+## Planning works
+- Code refactoring.
+- Remove the cloud cache module for easier use.
+- Add a Docker image for easier use.
+- Documentation writing.
+- Replace Gradio with other GUI frameworks.
+## License
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+## Acknowledgements
+Thank the contributors and the communities for their support and feedback.
 ---
+> If you find our work useful in your research, consider citing our paper by:
+```bash
+ @article{sun2024lambda,
+          title={LAMBDA: A Large Model Based Data Agent},
+          author={Sun, Maojun and Han, Ruijian and Jiang, Binyan and Qi, Houduo and Sun, Defeng and Yuan, Yancheng and Huang, Jian},
+          journal={arXiv preprint arXiv:2407.17535},
+          year={2024}
+}
+```
+## Star History
+[![Star History Chart](https://api.star-history.com/svg?repos=Stephen-SMJ/LAMBDA&type=Timeline)](https://star-history.com/#Stephen-SMJ/LAMBDA&Timeline)
+</body>

app.py ADDED Viewed

	@@ -0,0 +1,196 @@

+import os.path
+import shutil
+import sys
+import traceback
+import gradio as gr
+import json
+import random
+import time
+import uuid
+import pandas as pd
+from conversation import Conversation
+from cache.oss import init_dir, init_oss, upload_oss_file, get_download_link
+from cache.cache import data_cache
+from prompt_engineering.prompts import *
+from kernel import CodeKernel
+import yaml
+# from config import conv_model, streaming
+import atexit
+import signal
+class app():
+    def __init__(self, config_path='config.yaml'):
+        print("Load config: ", config_path)
+        self.config = yaml.load(open(config_path, 'r'), Loader=yaml.FullLoader)
+        self.conv = Conversation(self.config)
+        self.conv.programmer.messages = [
+            {
+                "role": "system",
+                "content": SYSTEM_PROMPT
+            }
+        ]
+    def open_board(self):
+        return self.conv.show_data()
+    def cache_file(self,file):
+        try:
+            self.oss_file_dir, self.local_cache_dir = init_dir(self.config['cache_dir'])
+            init_oss(self.config['oss_endpoint'], self.config['oss_access_key_id'], self.config['oss_access_secret'], self.config['oss_bucket_name'], self.config['expired_time'])
+            self.conv.oss_dir, self.conv.local_cache_dir = self.oss_file_dir, self.local_cache_dir
+            file_info = upload_oss_file(self.oss_file_dir, file)
+            local_file_path = os.path.join(self.local_cache_dir, file_info['file_name'])
+            shutil.copy2(file, local_file_path)
+            self.conv.add_data(file)
+            gen_info = self.conv.my_data_cache.get_description()
+            self.conv.programmer.messages[0]["content"] = (PROGRAMMER_PROMPT.format(working_path=self.local_cache_dir) +
+                                                f"\nNow, user uploads the datasets in {local_file_path}\n, and here is the general information of the dataset:\n {gen_info}")
+            if self.conv.retrieval:
+                self.conv.programmer.messages[0]["content"] += KNOWLEDGE_INTEGRATION_SYSTEM
+            print(self.conv.programmer.messages[0]["content"])
+            # self.conv.run(function_lib)
+            # self.conv.messages.append({"role": "assistant", "content": response})
+            # chat_history.append((message, response))
+        except Exception as e:
+            traceback.print_exc()
+            print(f"Uploaded file error: {e}")
+    # def show_data(self):
+    #     return self.conv.show_data()
+    def rendering_code(self):
+        return self.conv.rendering_code()
+    # def report_test(self):
+    #     with open("/Users/stephensun/Desktop/pypro/dsagent_ci/cache/conv_cache/2c23cf4b-31a8-4aeb-8689-13b9fbdfb7fc-2024-04-29/programmer_msg.json", "r") as f:
+    #         msg = json.load(f)
+    #         print(msg)
+    #     self.conv.programmer.messages = msg
+    #     down_path = self.conv.document_generation()
+    #     return [gr.Button(visible=False), gr.DownloadButton(label=f"Download Report", value=down_path, visible=True)]
+    def generate_report(self):
+        down_path = self.conv.document_generation()
+        return [gr.Button(visible=False), gr.DownloadButton(label=f"Download Report", value=down_path, visible=True)]
+    def export_code(self):
+        down_path = self.conv.export_code()
+        return [gr.Button(visible=False), gr.DownloadButton(label=f"Download Notebook", value=down_path, visible=True)]
+    def down_report(self):
+        return [gr.Button(visible=True), gr.DownloadButton(visible=False)]
+    def down_notebook(self):
+        return [gr.Button(visible=True), gr.DownloadButton(visible=False)]
+    # def human_loop(self, code):
+    #     result = self.conv.run_workflow(code)
+    #     self.conv.programmer.messages.append({"role": "assistant", "content": response})
+    #     return response
+    def call_llm(self, message, chat_history, code=None):
+        '''
+        :param message: input of user
+        :param chat_history: the history of whole dialogue. But this variable was only used to show in the UI (not record in LLM)
+        :return: chat_history in the Chatbot component
+        '''
+        if not code:
+            self.conv.programmer.messages.append({"role": "user", "content": message})
+        else:
+            message = code
+        response = self.conv.run_workflow(function_lib=None,code=code)
+        chat_history.append((message, response))
+        self.conv.chat_history = chat_history
+        # chat_history = self.conv.messages
+        # print(chat_history)
+        return "", chat_history  # return "" to let msg box be clear
+    # def load_chat_history(self):
+    #     # filtered_messages = [msg for msg in self.conv.programmer.messages if isinstance(msg, dict) and msg.get("role") in ["user", "assistant"]]
+    #     # # [{"role": "user", "content": "qqq"},{"role": "assistant", "content": "aaa"}]
+    #     # chat_history = [(filtered_messages[i].get('content'), filtered_messages[i + 1].get('content')) for i in range(0, len(filtered_messages), 2)]
+    #     return self.conv.chat_history
+    def save_dialogue(self, chat_history):
+        self.conv.save_conv()
+        with open(os.path.join(self.local_cache_dir,'system_dialogue.json'), 'w') as f:
+            json.dump(chat_history, f, indent=4)
+            f.close()
+        print(f"Dialogue saved in {os.path.join(self.local_cache_dir,'system_dialogue.json')}.")
+    def load_dialogue(self, json_file):
+        with open(json_file, 'r') as f:
+            chat_history = json.load(f)
+            f.close()
+        self.conv.chat_history = chat_history
+        return chat_history
+    def clear_all(self, message, chat_history):
+        self.conv.clear()
+        return "", []
+my_app = app()
+with gr.Blocks(theme=gr.themes.Soft(), css='front_end/css.css', js='front_end/javascript.js') as demo:
+    # get history for chatbot.
+    #history = my_app.load_dialogue("system_dialogue.json") # load history in previous conversation by indicate system_dialogue.json
+    chatbot = gr.Chatbot(value=my_app.conv.chat_history, height=600, label="LAMBDA", avatar_images=["front_end/user.jpg", "front_end/lambda.jpg"], show_copy_button=True)
+    with gr.Group():
+        with gr.Row():
+            upload_btn = gr.UploadButton(label="Upload Data", file_types=["csv", "xlsx"], scale=1)
+            # datasets = gr.File(file_types=["csv", "xlsx"], elem_id="file_upload",height=80)
+            msg = gr.Textbox(show_label=False, placeholder="Sent message to LLM", scale=6)
+            submit = gr.Button("Submit", scale=1)
+    with gr.Row():
+        board = gr.Button(value="Show/Update DataFrame", elem_id="df_btn", elem_classes="df_btn")
+        export_notebook = gr.Button(value="Notebook")
+        down_notebook = gr.DownloadButton("Download Notebook", visible=False)
+        generate_report = gr.Button(value="Generate Report")
+        down_report = gr.DownloadButton("Download Report",visible=False)
+        edit = gr.Button(value="Edit Code",elem_id="ed_btn", elem_classes="ed_btn")
+        save = gr.Button(value="Save Dialogue")
+        clear = gr.ClearButton(value="Clear All")
+    with gr.Group():
+        with gr.Row(visible=False, elem_id="ed",elem_classes="ed"):
+            code = gr.Code(label="Code",scale=6)
+            code_btn = gr.Button("Submit Code",scale=1)
+    code_btn.click(fn=my_app.call_llm, inputs=[msg, chatbot, code], outputs=[msg, chatbot])
+    df = gr.Dataframe(visible=False,elem_id="df",elem_classes="df")
+    upload_btn.upload(fn=my_app.cache_file, inputs=upload_btn)
+    if my_app.config['streaming']:
+        def chat_streaming(message, chat_history, code=None):
+            if not code:
+                my_app.conv.programmer.messages.append({"role": "user", "content": message})
+            else:
+                message = code
+            #my_app.conv.stream_workflow(function_lib=None, code=code)
+            return "", chat_history + [[message, None]]
+        msg.submit(chat_streaming, [msg, chatbot], [msg, chatbot], queue=False).then(
+            my_app.conv.stream_workflow, chatbot, chatbot
+        )
+        submit.click(chat_streaming, [msg, chatbot], [msg, chatbot], queue=False).then(
+            my_app.conv.stream_workflow, chatbot, chatbot
+        )
+    else:
+        msg.submit(my_app.call_llm, [msg, chatbot], [msg, chatbot])
+        submit.click(my_app.call_llm, [msg, chatbot], [msg, chatbot])
+    board.click(my_app.open_board, inputs=[], outputs=df)
+    edit.click(my_app.rendering_code, inputs=None, outputs=code)
+    export_notebook.click(my_app.export_code, inputs=None, outputs=[export_notebook, down_notebook])
+    down_notebook.click(my_app.down_notebook, inputs=None, outputs=[export_notebook, down_notebook])
+    generate_report.click(my_app.generate_report,inputs=None,outputs=[generate_report,down_report])
+    down_report.click(my_app.down_report,inputs=None,outputs=[generate_report,down_report])
+    save.click(my_app.save_dialogue, inputs=chatbot)
+    clear.click(fn=my_app.clear_all, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    #demo.load(fn=my_app.load_chat_history, inputs=None, outputs=chatbot)
+if __name__ == '__main__':
+    demo.launch(server_name="0.0.0.0",server_port=8000, debug=True, share=True)

cache/cache.py ADDED Viewed

	@@ -0,0 +1,30 @@

+import pandas as pd
+import os
+import shutil
+class data_cache:
+    def __init__(self, file_path) -> None:
+        self.file_path = file_path
+        self.data_cache_path = os.path.dirname(file_path)
+        self.data = pd.read_csv(self.file_path, encoding='gbk')
+        self.general_info = {}
+    def get_description(self) -> dict:
+        # self.general_info["info"] = get_info(self.data)
+        general_info = get_general_info(self.data)
+        self.general_info["num_rows"], self.general_info["num_features"], self.general_info["features"], \
+            self.general_info["col_type"], self.general_info["missing_val"] = general_info["num_rows"], \
+            general_info["num_features"], general_info["features"], general_info["col_type"], general_info[
+            "missing_val"]
+        self.general_info["describe"] = self.data.describe()
+        # self.general_info["label_counts"] = self.data["label"].value_counts()
+        return self.general_info
+def get_general_info(data: pd.DataFrame):
+    return {"num_rows": data.shape[0], "num_features": data.shape[1], "features": data.columns,
+            "col_type": data.dtypes, "missing_val": data.isnull().sum()}

cache/oss.py ADDED Viewed

	@@ -0,0 +1,71 @@

+import oss2
+import time
+import uuid
+import os
+from oss2.credentials import EnvironmentVariableCredentialsProvider
+import requests
+from joblib import load
+bucket = None
+expired_time = 3600
+def init_dir(cache_dir):
+    '''
+    initialize the folders for conversation
+    :return: the folder path of oss
+    '''
+    new_uuid = str(uuid.uuid4())
+    current_fold = time.strftime('%Y-%m-%d', time.localtime())
+    oss_file_dir = 'user_tmp/' + new_uuid + '-' + current_fold
+    # local_cache_dir = '/home/maojsun/proj/dsagent_ci/cache/conv_cache/' + new_uuid + '-' + current_fold
+    local_cache_dir = cache_dir + new_uuid + '-' + current_fold
+    os.makedirs(local_cache_dir)
+    return oss_file_dir, local_cache_dir
+def init_oss(endpoint, access_key_id, access_key_secret, bucket_name, expired):
+    auth = oss2.Auth(access_key_id, access_key_secret)
+    global bucket
+    bucket = oss2.Bucket(auth, endpoint, bucket_name)
+    global expired_time
+    expired_time = expired
+def upload_oss_file(oss_file_dir, local_file_path):
+    '''
+    upload a local file to oss
+    :param local_file_path: local file path
+    :return: file name and download link, which can directly download the file by clicking the link.
+    '''
+    file_name = os.path.basename(local_file_path)
+    object_name = os.path.join(oss_file_dir, file_name)
+    bucket.put_object_from_file(object_name, local_file_path)
+    download_link = get_download_link(object_name)
+    return {'file_name': file_name, 'download_link': download_link}
+def get_download_link(object_name):
+    # auth = oss2.ProviderAuth(EnvironmentVariableCredentialsProvider())
+    # 填写Object完整路径，例如exampledir/exampleobject.txt。Object完整路径中不能包含Bucket名称。 object_name = 'exampleobject.txt'
+    # 生成下载文件的签名URL，有效时间为3600秒。 # 设置slash_safe为True，OSS不会对Object完整路径中的正斜线（/）进行转义，此时生成的签名URL可以直接使用。
+    url = bucket.sign_url('GET', object_name, expired_time, slash_safe=True)
+    return url
+if __name__ == '__main__':
+    # local_file = "/Users/stephensun/Desktop/pypro/darob/models/LogisticRegression.pkl"
+    # model = load(local_file)
+    # print(model)
+    # upload_oss_file(local_file)
+    before_files = os.listdir(
+        '/Users/stephensun/Desktop/pypro/dsagent_ci/cache/conv_cache/6fa78267-4e0b-418e-ac47-c9d99b6bbe3b-2024-04-18')
+    # with open('/Users/stephensun/Desktop/pypro/dsagent_ci/cache/conv_cache/6fa78267-4e0b-418e-ac47-c9d99b6bbe3b-2024-04-18/test.txt', 'w') as f:
+    #     f.write("test")
+    after_files = os.listdir(
+        '/Users/stephensun/Desktop/pypro/dsagent_ci/cache/conv_cache/6fa78267-4e0b-418e-ac47-c9d99b6bbe3b-2024-04-18')
+    # print(check_folder(before_files, after_files))

config.yaml ADDED Viewed

	@@ -0,0 +1,40 @@

+#================================================================================================
+#                                       Config of the LLMs
+#================================================================================================
+conv_model : "gpt-4o-mini" # the conversation model
+programmer_model : "gpt-4o-mini"
+inspector_model : "gpt-4o-mini"
+api_key : ""
+base_url_conv_model : 'https://api.openai.com/v1'
+base_url_programmer : 'https://api.openai.com/v1'
+base_url_inspector : 'htts://api.openai.com/v1'
+max_token_conv_model: 4096 # the max token of the conversation model, this will determine the maximum length of the report.
+# conv_model: "gemini-1.5-flash" # the conversation model for high-quality responses
+# programmer_model: "gemini-1.5-flash" # model for code-related tasks
+# inspector_model: "gemini-1.5-flash" # model for error-checking and debugging
+# api_key: "AIzaSyA_BrRio97V_i7-sQ41EzhNqNqyPE5Ny9E" # replace with your API key
+# base_url_conv_model: 'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash'
+# base_url_programmer: 'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash'
+# base_url_inspector: 'https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash'
+# max_token_conv_model: 4096 # the maximum token limit for generating comprehensive reports
+#================================================================================================
+#                                       Config of the system
+#================================================================================================
+streaming : True
+#cache_related
+oss_endpoint: ""
+oss_access_key_id: ""
+oss_access_secret: ""
+oss_bucket_name: ""
+expired_time: 36000 # The expired time of the link in cache
+cache_dir : "" # local cache dir
+max_attempts : 5 # The max attempts of self-correcting
+max_exe_time: 18000 # max time for the execution
+#knowledge integration
+retrieval : False
+mode : "full"

conversation.py ADDED Viewed

	@@ -0,0 +1,453 @@

+import os
+import sys
+import openai
+import json
+# sys.path.append(r"D:\python\Lib\site-packages")
+import sys
+if 'D:\\python\\Lib\\site-packages' in sys.path:
+    sys.path.remove('D:\\python\\Lib\\site-packages')
+from programmer import Programmer
+from inspector import Inspector
+from function_calling import *
+from cache.cache import *
+from prompt_engineering.prompts import *
+import warnings
+import traceback
+# import pdfkit
+import zipfile
+from kernel import *
+from lambda_utils import *
+from cache.oss import upload_oss_file
+# from utilities import function_calling
+warnings.filterwarnings("ignore")
+class Conversation():
+    def __init__(self, config) -> None:
+        self.config = config
+        self.model = config['conv_model']
+        self.client = openai.OpenAI(api_key=config['api_key'], base_url=config['base_url_conv_model'])
+        self.programmer = Programmer(api_key=config['api_key'], model=config['programmer_model'], base_url=config['base_url_programmer'])
+        self.inspector = Inspector(api_key=config['api_key'], model=config['inspector_model'], base_url=config['base_url_inspector'])
+        self.messages = []
+        self.chat_history = []
+        self.retrieval = self.config['retrieval']
+        self.kernel = CodeKernel(config['max_exe_time'])
+        self.function_repository = {}
+        self.my_data_cache = None
+        self.max_attempts = config['max_attempts'] # maximum attempts of self-correcting
+        self.error_count = 0 # error count of self-correcting
+        self.repair_count = 0 # repair count of self-correcting
+        self.oss_dir = None
+        self.local_cache_dir = None
+        self.run_code(IMPORT)
+    def add_functions(self, function_lib: dict) -> None:
+        self.function_repository = function_lib
+    def add_data(self, data_path) -> None:
+        self.my_data_cache = data_cache(data_path)
+    def check_folder(self, before_files, after_files):
+        new_files = set(after_files) - set(before_files)
+        cloud_cache_info = []
+        if new_files:
+            for file in new_files:
+                cache_info = upload_oss_file(self.oss_dir, os.path.join(self.local_cache_dir, file))
+                cloud_cache_info.append(cache_info)
+        return cloud_cache_info
+    def save_conv(self):
+        with open(os.path.join(self.local_cache_dir,'programmer_msg.json'), 'w') as f:
+            json.dump(self.programmer.messages, f, indent=4)
+            f.close()
+        with open(os.path.join(self.local_cache_dir,'inspector_msg.json'), 'w') as f:
+            json.dump(self.inspector.messages, f, indent=4)
+            f.close()
+        print(f"Conversation saved in {os.path.join(self.local_cache_dir,'programmer_msg.json')}")
+        print(f"Conversation saved in {os.path.join(self.local_cache_dir, 'inspector_msg.json')}")
+    def add_programmer_msg(self, message: dict):
+        self.programmer.messages.append(message)
+    def add_programmer_repair_msg(self, bug_code:str, error_msg:str, fix_method:str, role="user"):
+        message = {"role": role, "content": CODE_FIX.format(bug_code=bug_code, error_message=error_msg, fix_method=fix_method)}
+        self.programmer.messages.append(message)
+    def add_inspector_msg(self, bug_code:str, error_msg:str, role="user"):
+        message = {"role": role, "content": CODE_INSPECT.format(bug_code=bug_code,error_message=error_msg)}
+        self.inspector.messages.append(message)
+    def run_code(self,code):
+        try:
+            res_type, res = execute(code, self.kernel)  # error in code of kernel will result in res_type:None
+        except Exception as e:
+            print(f'Error when executing code in kernel: {e}')
+            res_type, res = 'error', str(e)
+            self.add_inspector_msg(code, str(e))
+        return res_type, res
+    def rendering_code(self):
+        for i in range(len(self.programmer.messages)-1, 0, -1):
+            if self.programmer.messages[i]["role"] == "assistant":
+                is_python, code = extract_code(self.programmer.messages[i]["content"])
+                if is_python:
+                    return code
+        return None
+    # def human_loop(self, code):
+    #     self.programmer.messages.append({"role": "user", "content": HUMAN_LOOP.format(code=code)})
+    #
+    #     result = self.run_code(code)
+    #     exec_result = f"Execution result of user's code:\n{result}."
+    #     self.programmer.messages.append({"role": "system", "content": exec_result})
+    #     return response
+    def show_data(self) -> pd.DataFrame:
+        return self.my_data_cache.data
+    def document_generation(self):  # use conv model to generate report.
+        print("Report generating...")
+        self.messages = self.programmer.messages + [{"role": "user", "content": Academic_Report}]
+        # self.programmer.messages.append({"role": "user", "content": DOCUMENT})
+        report = self.call_chat_model(max_tokens=self.config["max_token_conv_model"]).choices[0].message.content
+        self.messages.append({"role": "assistant", "content": report})
+        #self.local_cache_dir = 'xxx'
+        mkd_path = os.path.join(self.local_cache_dir,'report.md')
+        # pdf_path = os.path.join(self.local_cache_dir,'report.pdf')
+        # zip_path = os.path.join(self.local_cache_dir,'report.zip')
+        with open(mkd_path, "w") as f:
+            f.write(report)
+            f.close()
+        # pdfkit.from_file(mkd_path, pdf_path)
+        # with zipfile.ZipFile(zip_path, 'w') as zipf:
+        #     zipf.write('report.md')
+        #     zipf.write('report.pdf')
+        return mkd_path
+    def export_code(self):
+        print("Exporting notebook...")
+        notebook_path = os.path.join(self.local_cache_dir, 'notebook.ipynb')
+        try:
+            self.kernel.export(notebook_path)
+        except Exception as e:
+            print(f"An error occurred when exporting notebook: {e}")
+        return notebook_path
+    def call_chat_model(self, max_tokens, functions=None, include_functions=False, ) -> dict:
+        params = {
+            "model": self.model,
+            "messages": self.messages,
+            "max_tokens": None if max_tokens is None else max_tokens
+        }
+        if include_functions:
+            params["functions"] = functions
+            #params["function_call"] = "auto"
+        try:
+            return self.client.chat.completions.create(**params)
+        except Exception as e:
+            self.messages = [self.messages[0], self.messages[-1]]
+            params = {
+                "model": self.model,
+                "messages": self.messages
+            }
+            print(f"Occurs error of calling chat model: {e}")
+            return self.client.chat.completions.create(**params)
+    def clear(self):
+        self.messages = []
+        self.programmer.clear()
+        self.inspector.clear()
+        self.kernel.shutdown()
+        del self.kernel
+        self.kernel = CodeKernel()
+        self.my_data_cache = None
+        self.my_data_description = None
+        self.oss_dir = None
+        self.local_cache_dir = None
+    def stream_workflow(self, chat_history, function_lib: dict=None, code=None) -> object:
+        try:
+            chat_history[-1][1] = ""
+            if not self.my_data_cache and code is None:  #todo: remove this restriction
+                for message in self.programmer._call_chat_model_streaming():
+                    chat_history[-1][1] += message
+                    yield chat_history
+                #chat_history += "\nNote, no data found. Please upload the data manually to start your task."
+                yield chat_history
+                final_response = chat_history[-1][1] #''.join(chat_history[-1][1])
+                self.add_programmer_msg({"role": "assistant", "content": final_response})
+            else:
+                if not code:
+                    #_, _ = execute(IMPORT, self.kernel) #todo install packages
+                    prog_response1_content = ''
+                    for message in self.programmer._call_chat_model_streaming(retrieval=self.retrieval, kernel=self.kernel):
+                        chat_history[-1][1] += message
+                        prog_response1_content += message
+                        yield chat_history
+                    self.add_programmer_msg({"role": "assistant", "content": prog_response1_content})
+                else:
+                    prog_response1_content = HUMAN_LOOP.format(code=code)
+                    self.add_programmer_msg({"role": "user", "content": prog_response1_content})
+                is_python, code = extract_code(prog_response1_content)
+                print("is_python:",is_python)
+                if is_python:
+                    chat_history[-1][1] += '\n🖥️ Execute code...\n'
+                    yield chat_history
+                    before_files = os.listdir(self.local_cache_dir)
+                    res_type, res = self.run_code(code)
+                    if res_type and res_type != 'error' or not res:  # no error in code   not res = res is None
+                        after_files = os.listdir(self.local_cache_dir)
+                        cloud_cache_info = self.check_folder(before_files, after_files)
+                        link_info = '\n'.join([f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                        ('.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})" for info in
+                                   cloud_cache_info])
+                        print("res:", res)
+                        chat_history[-1][1] += f"\n✅ Execution result:\n{res}\n\n"
+                        yield chat_history
+                        self.add_programmer_msg({"role": "user", "content": RESULT_PROMPT.format(res)})
+                        prog_response2 = ''
+                        for message in self.programmer._call_chat_model_streaming():
+                            chat_history[-1][1] += message
+                            prog_response2 += message
+                            yield chat_history
+                        self.add_programmer_msg({"role": "assistant", "content": prog_response2})
+                        chat_history[-1][1] += f"\n{link_info}"
+                        yield chat_history
+                    #elif not res_type and res:  #todo: problems related the image output in the result.
+                    # self-correcting
+                    else:
+                        self.error_count += 1
+                        round = 0
+                        while res and round < self.max_attempts: #max 5 round
+                            chat_history[-1][1] = f'⭕ Execution error, try to repair the code, attempts: {round+1}....\n'
+                            yield chat_history
+                            self.add_inspector_msg(code, res)
+                            if round == 3:
+                                insp_response1_content = "Try other packages or methods."
+                            else:
+                                insp_response1 = self.inspector._call_chat_model()
+                                insp_response1_content = insp_response1.choices[0].message.content
+                            self.inspector.messages.append({"role": "assistant", "content": insp_response1_content})
+                            self.add_programmer_repair_msg(code, res, insp_response1_content)
+                            prog_response1_content = ''
+                            for message in self.programmer._call_chat_model_streaming():
+                                chat_history[-1][1] += message
+                                prog_response1_content += message
+                                yield chat_history
+                            chat_history[-1][1] += '\n🖥️ Execute code...\n'
+                            yield chat_history
+                            self.add_programmer_msg({"role": "assistant", "content": prog_response1_content})
+                            is_python, code = extract_code(prog_response1_content)
+                            if is_python:
+                                before_files = os.listdir(self.local_cache_dir)
+                                res_type, res = self.run_code(code)
+                                if res_type and res_type != 'error' or not res: # execute successfully
+                                    self.repair_count += 1
+                                    break
+                            round += 1
+                        if round == self.max_attempts: # maximum retry
+                            return prog_response1_content + "\nSorry, I can't fix the code, can you help me to modified it or give some suggestions?"
+                            #self.add_programmer_msg({"role": "user", "content": "You can not tackle the error, you should ask me to give suggestions or modify the code."})
+                        # revision successful
+                        after_files = os.listdir(self.local_cache_dir)
+                        cloud_cache_info = self.check_folder(before_files, after_files)
+                        link_info = '\n'.join([f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                            ('.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})" for info in
+                                               cloud_cache_info])
+                        print("res:", res)
+                        chat_history[-1][1] += f"\n✅ Execution result:\n{res}\n\n"
+                        yield chat_history
+                        self.add_programmer_msg({"role": "user", "content": RESULT_PROMPT.format(res)})
+                        prog_response2 = ''
+                        for message in self.programmer._call_chat_model_streaming():
+                            chat_history[-1][1] += message
+                            prog_response2 += message
+                            yield chat_history
+                        self.add_programmer_msg({"role": "assistant", "content": prog_response2})
+                        chat_history[-1][1] += f"\n{link_info}"
+                        yield chat_history
+                    # else: #save file, figure..
+                    #     after_files = os.listdir(self.local_cache_dir)
+                    #     cloud_cache_info = self.check_folder(before_files, after_files)
+                    #     link_info = '\n'.join(
+                    #         [f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                    #             (
+                    #             '.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})"
+                    #          for info in
+                    #          cloud_cache_info])
+                    #     print("res:", res)
+                    #     self.add_programmer_msg({"role": "system", "content": FIG_PROMPT})
+                    #     prog_response2 = self.programmer._call_chat_model().choices[0].message.content
+                    #     final_response = prog_response1_content + "\n" + prog_response2
+                    #     final_response += f"\n{link_info}"
+                else:
+                    chat_history[-1][1] += "\nNo code detected or code is not python code." #todo : delete printing this
+                    yield chat_history
+                    final_response = prog_response1_content + "\nNo code detected or code is not python code."
+                    if self.programmer.messages[-1]["role"] == "assistant":
+                        self.programmer.messages[-1]["content"] = final_response
+        except Exception as e:
+            chat_history[-1][1] += "\nSorry, there is an error in the program, please try again."
+            print(f"An error occurred: {e}")
+            traceback.print_exc()
+            if self.programmer.messages[-1]["role"] == "user":
+                self.programmer.messages.append({"role": "assistant", "content": f"An error occurred in program: {e}"})
+            #return "Sorry, there is an error in the program, please try again."
+        #return final_response
+    # a previous code without streaming
+    def run_workflow(self,function_lib: dict=None, code=None) -> object:
+        try:
+            if not self.my_data_cache and code is None:
+                final_response = self.programmer._call_chat_model().choices[0].message.content
+                self.add_programmer_msg({"role": "assistant", "content": final_response})
+            else:
+                if not code:
+                    #_, _ = execute(IMPORT, self.kernel) #todo prompt of install packages
+                    prog_response1 = self.programmer._call_chat_model()
+                    prog_response1_content = prog_response1.choices[0].message.content
+                    self.add_programmer_msg({"role": "assistant", "content": prog_response1_content})
+                else:
+                    prog_response1_content = HUMAN_LOOP.format(code=code)
+                    self.add_programmer_msg({"role": "user", "content": prog_response1_content})
+                    self.add_programmer_msg({"role": "assistant", "content": "I got the code, let me execute it."})
+                is_python, code = extract_code(prog_response1_content)
+                print("is_python:",is_python)
+                if is_python:
+                    before_files = os.listdir(self.local_cache_dir)
+                    res_type, res = self.run_code(code)
+                    if res_type and res_type != 'error' or not res: # no error in code   not res = res is None
+                        after_files = os.listdir(self.local_cache_dir)
+                        cloud_cache_info = self.check_folder(before_files, after_files)
+                        link_info = '\n'.join([f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                        ('.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})" for info in
+                                   cloud_cache_info])
+                        print("res:", res)
+                        self.add_programmer_msg({"role": "user", "content": RESULT_PROMPT.format(res)})
+                        prog_response2 = self.programmer._call_chat_model().choices[0].message.content
+                        final_response = prog_response1_content + "\n" + f"Execution result:\n{res}\n\n{prog_response2}"
+                        final_response += f"\n{link_info}"
+                    #elif not res_type and res: #error occurs in code, only image
+                    else:
+                        self.error_count += 1
+                        round = 0
+                        while res and round < self.max_attempts: #max 5 round
+                            self.add_inspector_msg(code, res)
+                            if round == 3:
+                                insp_response1_content = "Try other packages or methods."
+                            else:
+                                insp_response1 = self.inspector._call_chat_model()
+                                insp_response1_content = insp_response1.choices[0].message.content
+                            self.inspector.messages.append({"role": "assistant", "content": insp_response1_content})
+                            #self.add_programmer_msg(modify_msg)
+                            self.add_programmer_repair_msg(code, res, insp_response1_content)
+                            prog_response1 = self.programmer._call_chat_model()
+                            prog_response1_content = prog_response1.choices[0].message.content
+                            self.add_programmer_msg({"role": "assistant", "content": prog_response1_content})
+                            is_python, code = extract_code(prog_response1_content)
+                            if is_python:
+                                before_files = os.listdir(self.local_cache_dir)
+                                res_type, res = self.run_code(code)
+                                if res_type or not res: # execute successfully
+                                    self.repair_count += 1
+                                    break
+                            round += 1
+                        if round == self.max_attempts: # max retry
+                            # self.add_programmer_msg({"role": "assistant", "content": "Sorry, I can't fix the code, can you help me to modified it or give some suggestions?"})
+                            return prog_response1_content + "\nSorry, I can't fix the code, can you help me to modified it or give some suggestions?"
+                        # revision successful
+                        after_files = os.listdir(self.local_cache_dir)
+                        cloud_cache_info = self.check_folder(before_files, after_files)
+                        link_info = '\n'.join([f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                            ('.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})" for info in
+                                               cloud_cache_info])
+                        print("res:", res)
+                        if 'head()' in code:
+                            self.messages.append({"role": "user", "content": MKD_PROMPT.format(res)})
+                            mkd_res = self.call_chat_model(max_tokens=512).choices[0].message.content
+                            res = mkd_res
+                            self.messages.pop()
+                        self.add_programmer_msg({"role": "user", "content": RESULT_PROMPT.format(res)})
+                        prog_response2 = self.programmer._call_chat_model().choices[0].message.content
+                        # todo: delete code in second response
+                        # prog_response2 = remove_code_blocks(prog_response2)
+                        self.add_programmer_msg({"role": "assistant", "content": prog_response2})
+                        # final_response = prog_response1_content + "\n" + prog_response2
+                        final_response = prog_response1_content + "\n" + f"Execution result:\n{res}\n\n{prog_response2}"
+                        final_response += f"\n{link_info}"
+                    # else: #save file, figure..
+                    #     after_files = os.listdir(self.local_cache_dir)
+                    #     cloud_cache_info = self.check_folder(before_files, after_files)
+                    #     link_info = '\n'.join(
+                    #         [f"![{info['file_name']}]({info['download_link']})" if info['file_name'].endswith(
+                    #             (
+                    #             '.jpg', '.jpeg', '.png', '.gif')) else f"[{info['file_name']}]({info['download_link']})"
+                    #          for info in
+                    #          cloud_cache_info])
+                    #     print("res:", res)
+                    #     self.add_programmer_msg({"role": "system", "content": FIG_PROMPT})
+                    #     prog_response2 = self.programmer._call_chat_model().choices[0].message.content
+                    #     final_response = prog_response1_content + "\n" + prog_response2
+                    #     final_response += f"\n{link_info}"
+                else:
+                    final_response = prog_response1_content + "\n" + "No code detected or code is not python code."
+                    if self.programmer.messages[-1]["role"] == "assistant":
+                        self.programmer.messages[-1]["content"] = final_response
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            traceback.print_exc()
+            if self.programmer.messages[-1]["role"] == "user":
+                self.programmer.messages.append({"role": "assistant", "content": f"An error occurred in program: {e}"})
+            return "Sorry, there is an error in the program, please try again."
+        return final_response
+# function_lib = {
+#
+# }

front_end/css.css ADDED Viewed

	@@ -0,0 +1,3 @@

+h1 {margin: 20px auto; text-align: center;}
+img {max-height: 400px!important; max-width: 50vw;!important;}
+/*footer {display: none !important}*/

front_end/javascript.js ADDED Viewed

	@@ -0,0 +1,49 @@

+function createGradioAnimation() {
+    var container = document.createElement('div');
+    container.id = 'gradio-animation';
+    container.style.fontSize = '2em';
+    container.style.fontWeight = 'bold';
+    container.style.textAlign = 'center';
+    container.style.marginBottom = '20px';
+    var text = 'Welcome to LAMBDA! Easy Data Analytics!';
+    for (var i = 0; i < text.length; i++) {
+        (function(i){
+            setTimeout(function(){
+                var letter = document.createElement('span');
+                letter.style.opacity = '0';
+                letter.style.transition = 'opacity 0.5s';
+                letter.innerText = text[i];
+                container.appendChild(letter);
+                setTimeout(function() {
+                    letter.style.opacity = '1';
+                }, 50);
+            }, i * 250);
+        })(i);
+    }
+    var gradioContainer = document.querySelector('.gradio-container');
+    gradioContainer.insertBefore(container, gradioContainer.firstChild);
+    var ed_btn = document.querySelector('.ed_btn');
+    var ed  = document.querySelector('.ed');
+    //console.log(button)
+    ed_btn.addEventListener('click', function() {
+        ed.style.display = 'block';
+    });
+    var button = document.querySelector('.df_btn');
+    var df = document.querySelector('.df');
+    //console.log(button)
+    button.addEventListener('click', function() {
+        df.style.display = 'block';
+    });
+    return 'Animation created';
+}

front_end/lambda.jpg ADDED Viewed

front_end/user.jpg ADDED Viewed

function_calling.py ADDED Viewed

	@@ -0,0 +1,697 @@

+"""Methods for creating function specs in the style of OpenAI Functions"""
+from __future__ import annotations
+import collections
+import inspect
+import logging
+import types
+import typing
+import uuid
+from typing import (
+    TYPE_CHECKING,
+    Annotated,
+    Any,
+    Callable,
+    Literal,
+    Optional,
+    Union,
+    cast,
+)
+from pydantic import BaseModel
+from typing_extensions import TypedDict, get_args, get_origin, is_typeddict
+from langchain_core._api import beta, deprecated
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
+from langchain_core.utils.json_schema import dereference_refs
+from langchain_core.utils.pydantic import is_basemodel_subclass
+if TYPE_CHECKING:
+    from langchain_core.tools import BaseTool
+logger = logging.getLogger(__name__)
+PYTHON_TO_JSON_TYPES = {
+    "str": "string",
+    "int": "integer",
+    "float": "number",
+    "bool": "boolean",
+}
+class FunctionDescription(TypedDict):
+    """Representation of a callable function to send to an LLM."""
+    name: str
+    """The name of the function."""
+    description: str
+    """A description of the function."""
+    parameters: dict
+    """The parameters of the function."""
+class ToolDescription(TypedDict):
+    """Representation of a callable function to the OpenAI API."""
+    type: Literal["function"]
+    """The type of the tool."""
+    function: FunctionDescription
+    """The function description."""
+def _rm_titles(kv: dict, prev_key: str = "") -> dict:
+    new_kv = {}
+    for k, v in kv.items():
+        if k == "title":
+            if isinstance(v, dict) and prev_key == "properties" and "title" in v:
+                new_kv[k] = _rm_titles(v, k)
+            else:
+                continue
+        elif isinstance(v, dict):
+            new_kv[k] = _rm_titles(v, k)
+        else:
+            new_kv[k] = v
+    return new_kv
+@deprecated(
+    "0.1.16",
+    alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+    removal="1.0",
+)
+def convert_pydantic_to_openai_function(
+    model: type,
+    *,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+    rm_titles: bool = True,
+) -> FunctionDescription:
+    """Converts a Pydantic model to a function description for the OpenAI API.
+    Args:
+        model: The Pydantic model to convert.
+        name: The name of the function. If not provided, the title of the schema will be
+            used.
+        description: The description of the function. If not provided, the description
+            of the schema will be used.
+        rm_titles: Whether to remove titles from the schema. Defaults to True.
+    Returns:
+        The function description.
+    """
+    if hasattr(model, "model_json_schema"):
+        schema = model.model_json_schema()  # Pydantic 2
+    elif hasattr(model, "schema"):
+        schema = model.schema()  # Pydantic 1
+    else:
+        msg = "Model must be a Pydantic model."
+        raise TypeError(msg)
+    schema = dereference_refs(schema)
+    if "definitions" in schema:  # pydantic 1
+        schema.pop("definitions", None)
+    if "$defs" in schema:  # pydantic 2
+        schema.pop("$defs", None)
+    title = schema.pop("title", "")
+    default_description = schema.pop("description", "")
+    return {
+        "name": name or title,
+        "description": description or default_description,
+        "parameters": _rm_titles(schema) if rm_titles else schema,
+    }
+@deprecated(
+    "0.1.16",
+    alternative="langchain_core.utils.function_calling.convert_to_openai_tool()",
+    removal="1.0",
+)
+def convert_pydantic_to_openai_tool(
+    model: type[BaseModel],
+    *,
+    name: Optional[str] = None,
+    description: Optional[str] = None,
+) -> ToolDescription:
+    """Converts a Pydantic model to a function description for the OpenAI API.
+    Args:
+        model: The Pydantic model to convert.
+        name: The name of the function. If not provided, the title of the schema will be
+            used.
+        description: The description of the function. If not provided, the description
+            of the schema will be used.
+    Returns:
+        The tool description.
+    """
+    function = convert_pydantic_to_openai_function(
+        model, name=name, description=description
+    )
+    return {"type": "function", "function": function}
+def _get_python_function_name(function: Callable) -> str:
+    """Get the name of a Python function."""
+    return function.__name__
+@deprecated(
+    "0.1.16",
+    alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+    removal="1.0",
+)
+def convert_python_function_to_openai_function(
+    function: Callable,
+) -> FunctionDescription:
+    """Convert a Python function to an OpenAI function-calling API compatible dict.
+    Assumes the Python function has type hints and a docstring with a description. If
+        the docstring has Google Python style argument descriptions, these will be
+        included as well.
+    Args:
+        function: The Python function to convert.
+    Returns:
+        The OpenAI function description.
+    """
+    from langchain_core.tools.base import create_schema_from_function
+    func_name = _get_python_function_name(function)
+    model = create_schema_from_function(
+        func_name,
+        function,
+        filter_args=(),
+        parse_docstring=True,
+        error_on_invalid_docstring=False,
+        include_injected=False,
+    )
+    return convert_pydantic_to_openai_function(
+        model,
+        name=func_name,
+        description=model.__doc__,
+    )
+def _convert_typed_dict_to_openai_function(typed_dict: type) -> FunctionDescription:
+    visited: dict = {}
+    from pydantic.v1 import BaseModel
+    model = cast(
+        type[BaseModel],
+        _convert_any_typed_dicts_to_pydantic(typed_dict, visited=visited),
+    )
+    return convert_pydantic_to_openai_function(model)  # type: ignore
+_MAX_TYPED_DICT_RECURSION = 25
+def _convert_any_typed_dicts_to_pydantic(
+    type_: type,
+    *,
+    visited: dict,
+    depth: int = 0,
+) -> type:
+    from pydantic.v1 import Field as Field_v1
+    from pydantic.v1 import create_model as create_model_v1
+    if type_ in visited:
+        return visited[type_]
+    elif depth >= _MAX_TYPED_DICT_RECURSION:
+        return type_
+    elif is_typeddict(type_):
+        typed_dict = type_
+        docstring = inspect.getdoc(typed_dict)
+        annotations_ = typed_dict.__annotations__
+        description, arg_descriptions = _parse_google_docstring(
+            docstring, list(annotations_)
+        )
+        fields: dict = {}
+        for arg, arg_type in annotations_.items():
+            if get_origin(arg_type) is Annotated:
+                annotated_args = get_args(arg_type)
+                new_arg_type = _convert_any_typed_dicts_to_pydantic(
+                    annotated_args[0], depth=depth + 1, visited=visited
+                )
+                field_kwargs = dict(zip(("default", "description"), annotated_args[1:]))
+                if (field_desc := field_kwargs.get("description")) and not isinstance(
+                    field_desc, str
+                ):
+                    msg = (
+                        f"Invalid annotation for field {arg}. Third argument to "
+                        f"Annotated must be a string description, received value of "
+                        f"type {type(field_desc)}."
+                    )
+                    raise ValueError(msg)
+                elif arg_desc := arg_descriptions.get(arg):
+                    field_kwargs["description"] = arg_desc
+                else:
+                    pass
+                fields[arg] = (new_arg_type, Field_v1(**field_kwargs))
+            else:
+                new_arg_type = _convert_any_typed_dicts_to_pydantic(
+                    arg_type, depth=depth + 1, visited=visited
+                )
+                field_kwargs = {"default": ...}
+                if arg_desc := arg_descriptions.get(arg):
+                    field_kwargs["description"] = arg_desc
+                fields[arg] = (new_arg_type, Field_v1(**field_kwargs))
+        model = create_model_v1(typed_dict.__name__, **fields)
+        model.__doc__ = description
+        visited[typed_dict] = model
+        return model
+    elif (origin := get_origin(type_)) and (type_args := get_args(type_)):
+        subscriptable_origin = _py_38_safe_origin(origin)
+        type_args = tuple(
+            _convert_any_typed_dicts_to_pydantic(arg, depth=depth + 1, visited=visited)
+            for arg in type_args  # type: ignore[index]
+        )
+        return subscriptable_origin[type_args]  # type: ignore[index]
+    else:
+        return type_
+@deprecated(
+    "0.1.16",
+    alternative="langchain_core.utils.function_calling.convert_to_openai_function()",
+    removal="1.0",
+)
+def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
+    """Format tool into the OpenAI function API.
+    Args:
+        tool: The tool to format.
+    Returns:
+        The function description.
+    """
+    from langchain_core.tools import simple
+    is_simple_oai_tool = isinstance(tool, simple.Tool) and not tool.args_schema
+    if tool.tool_call_schema and not is_simple_oai_tool:
+        return convert_pydantic_to_openai_function(
+            tool.tool_call_schema, name=tool.name, description=tool.description
+        )
+    else:
+        return {
+            "name": tool.name,
+            "description": tool.description,
+            "parameters": {
+                # This is a hack to get around the fact that some tools
+                # do not expose an args_schema, and expect an argument
+                # which is a string.
+                # And Open AI does not support an array type for the
+                # parameters.
+                "properties": {
+                    "__arg1": {"title": "__arg1", "type": "string"},
+                },
+                "required": ["__arg1"],
+                "type": "object",
+            },
+        }
+@deprecated(
+    "0.1.16",
+    alternative="langchain_core.utils.function_calling.convert_to_openai_tool()",
+    removal="1.0",
+)
+def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription:
+    """Format tool into the OpenAI function API.
+    Args:
+        tool: The tool to format.
+    Returns:
+        The tool description.
+    """
+    function = format_tool_to_openai_function(tool)
+    return {"type": "function", "function": function}
+def convert_to_openai_function(
+    function: Union[dict[str, Any], type, Callable, BaseTool],
+    *,
+    strict: Optional[bool] = None,
+) -> dict[str, Any]:
+    """Convert a raw function/class to an OpenAI function.
+    Args:
+        function:
+            A dictionary, Pydantic BaseModel class, TypedDict class, a LangChain
+            Tool object, or a Python function. If a dictionary is passed in, it is
+            assumed to already be a valid OpenAI function, a JSON schema with
+            top-level 'title' key specified, an Anthropic format
+            tool, or an Amazon Bedrock Converse format tool.
+        strict:
+            If True, model output is guaranteed to exactly match the JSON Schema
+            provided in the function definition. If None, ``strict`` argument will not
+            be included in function definition.
+    Returns:
+        A dict version of the passed in function which is compatible with the OpenAI
+        function-calling API.
+    Raises:
+        ValueError: If function is not in a supported format.
+    .. versionchanged:: 0.2.29
+        ``strict`` arg added.
+    .. versionchanged:: 0.3.13
+        Support for Anthropic format tools added.
+    .. versionchanged:: 0.3.14
+        Support for Amazon Bedrock Converse format tools added.
+    .. versionchanged:: 0.3.16
+        'description' and 'parameters' keys are now optional. Only 'name' is
+        required and guaranteed to be part of the output.
+    """
+    from langchain_core.tools import BaseTool
+    # an Anthropic format tool
+    if isinstance(function, dict) and all(
+        k in function for k in ("name", "input_schema")
+    ):
+        oai_function = {
+            "name": function["name"],
+            "parameters": function["input_schema"],
+        }
+        if "description" in function:
+            oai_function["description"] = function["description"]
+    # an Amazon Bedrock Converse format tool
+    elif isinstance(function, dict) and "toolSpec" in function:
+        oai_function = {
+            "name": function["toolSpec"]["name"],
+            "parameters": function["toolSpec"]["inputSchema"]["json"],
+        }
+        if "description" in function["toolSpec"]:
+            oai_function["description"] = function["toolSpec"]["description"]
+    # already in OpenAI function format
+    elif isinstance(function, dict) and "name" in function:
+        oai_function = {
+            k: v
+            for k, v in function.items()
+            if k in ("name", "description", "parameters", "strict")
+        }
+    # a JSON schema with title and description
+    elif isinstance(function, dict) and "title" in function:
+        function_copy = function.copy()
+        oai_function = {"name": function_copy.pop("title")}
+        if "description" in function_copy:
+            oai_function["description"] = function_copy.pop("description")
+        if function_copy and "properties" in function_copy:
+            oai_function["parameters"] = function_copy
+    elif isinstance(function, type) and is_basemodel_subclass(function):
+        oai_function = cast(dict, convert_pydantic_to_openai_function(function))
+    elif is_typeddict(function):
+        oai_function = cast(
+            dict, _convert_typed_dict_to_openai_function(cast(type, function))
+        )
+    elif isinstance(function, BaseTool):
+        oai_function = cast(dict, format_tool_to_openai_function(function))
+    elif callable(function):
+        oai_function = cast(dict, convert_python_function_to_openai_function(function))
+    else:
+        msg = (
+            f"Unsupported function\n\n{function}\n\nFunctions must be passed in"
+            " as Dict, pydantic.BaseModel, or Callable. If they're a dict they must"
+            " either be in OpenAI function format or valid JSON schema with top-level"
+            " 'title' and 'description' keys."
+        )
+        raise ValueError(msg)
+    if strict is not None:
+        if "strict" in oai_function and oai_function["strict"] != strict:
+            msg = (
+                f"Tool/function already has a 'strict' key wth value "
+                f"{oai_function['strict']} which is different from the explicit "
+                f"`strict` arg received {strict=}."
+            )
+            raise ValueError(msg)
+        oai_function["strict"] = strict
+        if strict:
+            # As of 08/06/24, OpenAI requires that additionalProperties be supplied and
+            # set to False if strict is True.
+            # All properties layer needs 'additionalProperties=False'
+            oai_function["parameters"] = _recursive_set_additional_properties_false(
+                oai_function["parameters"]
+            )
+    return oai_function
+def convert_to_openai_tool(
+    tool: Union[dict[str, Any], type[BaseModel], Callable, BaseTool],
+    *,
+    strict: Optional[bool] = None,
+) -> dict[str, Any]:
+    """Convert a tool-like object to an OpenAI tool schema.
+    OpenAI tool schema reference:
+    https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools
+    Args:
+        tool:
+            Either a dictionary, a pydantic.BaseModel class, Python function, or
+            BaseTool. If a dictionary is passed in, it is
+            assumed to already be a valid OpenAI function, a JSON schema with
+            top-level 'title' key specified, an Anthropic format
+            tool, or an Amazon Bedrock Converse format tool.
+        strict:
+            If True, model output is guaranteed to exactly match the JSON Schema
+            provided in the function definition. If None, ``strict`` argument will not
+            be included in tool definition.
+    Returns:
+        A dict version of the passed in tool which is compatible with the
+        OpenAI tool-calling API.
+    .. versionchanged:: 0.2.29
+        ``strict`` arg added.
+    .. versionchanged:: 0.3.13
+        Support for Anthropic format tools added.
+    .. versionchanged:: 0.3.14
+        Support for Amazon Bedrock Converse format tools added.
+    .. versionchanged:: 0.3.16
+        'description' and 'parameters' keys are now optional. Only 'name' is
+        required and guaranteed to be part of the output.
+    """
+    if isinstance(tool, dict) and tool.get("type") == "function" and "function" in tool:
+        return tool
+    oai_function = convert_to_openai_function(tool, strict=strict)
+    return {"type": "function", "function": oai_function}
+@beta()
+def tool_example_to_messages(
+    input: str,
+    tool_calls: list[BaseModel],
+    tool_outputs: Optional[list[str]] = None,
+    *,
+    ai_response: Optional[str] = None,
+) -> list[BaseMessage]:
+    """Convert an example into a list of messages that can be fed into an LLM.
+    This code is an adapter that converts a single example to a list of messages
+    that can be fed into a chat model.
+    The list of messages per example by default corresponds to:
+    1) HumanMessage: contains the content from which content should be extracted.
+    2) AIMessage: contains the extracted information from the model
+    3) ToolMessage: contains confirmation to the model that the model requested a tool
+        correctly.
+    If `ai_response` is specified, there will be a final AIMessage with that response.
+    The ToolMessage is required because some chat models are hyper-optimized for agents
+    rather than for an extraction use case.
+    Arguments:
+        input: string, the user input
+        tool_calls: List[BaseModel], a list of tool calls represented as Pydantic
+            BaseModels
+        tool_outputs: Optional[List[str]], a list of tool call outputs.
+            Does not need to be provided. If not provided, a placeholder value
+            will be inserted. Defaults to None.
+        ai_response: Optional[str], if provided, content for a final AIMessage.
+    Returns:
+        A list of messages
+    Examples:
+        .. code-block:: python
+            from typing import List, Optional
+            from pydantic import BaseModel, Field
+            from langchain_openai import ChatOpenAI
+            class Person(BaseModel):
+                '''Information about a person.'''
+                name: Optional[str] = Field(..., description="The name of the person")
+                hair_color: Optional[str] = Field(
+                    ..., description="The color of the person's hair if known"
+                )
+                height_in_meters: Optional[str] = Field(
+                    ..., description="Height in METERs"
+                )
+            examples = [
+                (
+                    "The ocean is vast and blue. It's more than 20,000 feet deep.",
+                    Person(name=None, height_in_meters=None, hair_color=None),
+                ),
+                (
+                    "Fiona traveled far from France to Spain.",
+                    Person(name="Fiona", height_in_meters=None, hair_color=None),
+                ),
+            ]
+            messages = []
+            for txt, tool_call in examples:
+                messages.extend(
+                    tool_example_to_messages(txt, [tool_call])
+                )
+    """
+    messages: list[BaseMessage] = [HumanMessage(content=input)]
+    openai_tool_calls = []
+    for tool_call in tool_calls:
+        openai_tool_calls.append(
+            {
+                "id": str(uuid.uuid4()),
+                "type": "function",
+                "function": {
+                    # The name of the function right now corresponds to the name
+                    # of the pydantic model. This is implicit in the API right now,
+                    # and will be improved over time.
+                    "name": tool_call.__class__.__name__,
+                    "arguments": tool_call.model_dump_json(),
+                },
+            }
+        )
+    messages.append(
+        AIMessage(content="", additional_kwargs={"tool_calls": openai_tool_calls})
+    )
+    tool_outputs = tool_outputs or ["You have correctly called this tool."] * len(
+        openai_tool_calls
+    )
+    for output, tool_call_dict in zip(tool_outputs, openai_tool_calls):
+        messages.append(ToolMessage(content=output, tool_call_id=tool_call_dict["id"]))  # type: ignore
+    if ai_response:
+        messages.append(AIMessage(content=ai_response))
+    return messages
+def _parse_google_docstring(
+    docstring: Optional[str],
+    args: list[str],
+    *,
+    error_on_invalid_docstring: bool = False,
+) -> tuple[str, dict]:
+    """Parse the function and argument descriptions from the docstring of a function.
+    Assumes the function docstring follows Google Python style guide.
+    """
+    if docstring:
+        docstring_blocks = docstring.split("\n\n")
+        if error_on_invalid_docstring:
+            filtered_annotations = {
+                arg for arg in args if arg not in ("run_manager", "callbacks", "return")
+            }
+            if filtered_annotations and (
+                len(docstring_blocks) < 2
+                or not any(block.startswith("Args:") for block in docstring_blocks[1:])
+            ):
+                msg = "Found invalid Google-Style docstring."
+                raise ValueError(msg)
+        descriptors = []
+        args_block = None
+        past_descriptors = False
+        for block in docstring_blocks:
+            if block.startswith("Args:"):
+                args_block = block
+                break
+            elif block.startswith(("Returns:", "Example:")):
+                # Don't break in case Args come after
+                past_descriptors = True
+            elif not past_descriptors:
+                descriptors.append(block)
+            else:
+                continue
+        description = " ".join(descriptors)
+    else:
+        if error_on_invalid_docstring:
+            msg = "Found invalid Google-Style docstring."
+            raise ValueError(msg)
+        description = ""
+        args_block = None
+    arg_descriptions = {}
+    if args_block:
+        arg = None
+        for line in args_block.split("\n")[1:]:
+            if ":" in line:
+                arg, desc = line.split(":", maxsplit=1)
+                arg = arg.strip()
+                arg_name, _, _annotations = arg.partition(" ")
+                if _annotations.startswith("(") and _annotations.endswith(")"):
+                    arg = arg_name
+                arg_descriptions[arg] = desc.strip()
+            elif arg:
+                arg_descriptions[arg] += " " + line.strip()
+    return description, arg_descriptions
+def _py_38_safe_origin(origin: type) -> type:
+    origin_union_type_map: dict[type, Any] = (
+        {types.UnionType: Union} if hasattr(types, "UnionType") else {}
+    )
+    origin_map: dict[type, Any] = {
+        dict: dict,
+        list: list,
+        tuple: tuple,
+        set: set,
+        collections.abc.Iterable: typing.Iterable,
+        collections.abc.Mapping: typing.Mapping,
+        collections.abc.Sequence: typing.Sequence,
+        collections.abc.MutableMapping: typing.MutableMapping,
+        **origin_union_type_map,
+    }
+    return cast(type, origin_map.get(origin, origin))
+def _recursive_set_additional_properties_false(
+    schema: dict[str, Any],
+) -> dict[str, Any]:
+    if isinstance(schema, dict):
+        # Check if 'required' is a key at the current level or if the schema is empty,
+        # in which case additionalProperties still needs to be specified.
+        if "required" in schema or (
+            "properties" in schema and not schema["properties"]
+        ):
+            schema["additionalProperties"] = False
+        # Recursively check 'properties' and 'items' if they exist
+        if "properties" in schema:
+            for value in schema["properties"].values():
+                _recursive_set_additional_properties_false(value)
+        if "items" in schema:
+            _recursive_set_additional_properties_false(schema["items"])
+    return schema

inspector.py ADDED Viewed

	@@ -0,0 +1,44 @@

+import openai
+class Inspector:
+    def __init__(self, api_key , model="gpt-3.5-turbo-1106", base_url=''):
+        self.client = openai.OpenAI(api_key=api_key, base_url=base_url)
+        self.model = model
+        self.messages = []
+        self.function_repository = {}
+    def add_functions(self, function_lib: dict) -> None:
+        self.function_repository = function_lib
+    def _call_chat_model(self, functions=None, include_functions=False):
+        params = {
+            "model": self.model,
+            "messages": self.messages,
+        }
+        if include_functions:
+            params['functions'] = functions
+            params['function_call'] = "auto"
+        try:
+            return self.client.chat.completions.create(**params)
+        except Exception as e:
+            print(f"Error calling chat model: {e}")
+            return None
+    def run(self, function_lib=None):
+        try:
+            if function_lib is None:
+                response = self._call_chat_model()
+                final_response = response.choices[0].message.content
+                return final_response
+        except Exception as e:
+            print(f"An error occurred: {e}")
+            return None
+    def clear(self):
+        self.messages = []
+        self.function_repository = {}

kernel.py ADDED Viewed

	@@ -0,0 +1,337 @@

+import queue
+# import streamlit as st
+import base64
+from io import BytesIO
+import re
+import os
+import jupyter_client
+from PIL import Image
+from subprocess import PIPE
+from pprint import pprint
+import nbformat as nbf
+IPYKERNEL = os.environ.get('IPYKERNEL', 'dsagent')
+# client = get_client()
+class CodeKernel(object):
+    def __init__(self,
+                 kernel_name='kernel',
+                 kernel_id=None,
+                 kernel_config_path="",
+                 python_path=None,
+                 ipython_path=None,
+                 init_file_path="./startup.py",
+                 verbose=1,
+                 max_exe_time=6000):
+        self.kernel_name = kernel_name
+        self.kernel_id = kernel_id
+        self.kernel_config_path = kernel_config_path
+        self.python_path = python_path
+        self.ipython_path = ipython_path
+        self.init_file_path = init_file_path
+        self.executed_cells = []
+        self.verbose = verbose
+        self.interrupt_signal = False
+        self.max_exe_time = max_exe_time
+        if python_path is None and ipython_path is None:
+            env = None
+        else:
+            env = {"PATH": self.python_path + ":$PATH", "PYTHONPATH": self.python_path}
+        # Initialize the backend kernel
+        self.kernel_manager = jupyter_client.KernelManager(kernel_name=IPYKERNEL,
+                                                           connection_file=self.kernel_config_path,
+                                                           exec_files=[self.init_file_path],
+                                                           env=env)
+        if self.kernel_config_path:
+            self.kernel_manager.load_connection_file()
+            self.kernel_manager.start_kernel(stdout=PIPE, stderr=PIPE)
+            print("Backend kernel started with the configuration: {}".format(
+                self.kernel_config_path))
+        else:
+            self.kernel_manager.start_kernel(stdout=PIPE, stderr=PIPE)
+            print("Backend kernel started with the configuration: {}".format(
+                self.kernel_manager.connection_file))
+        if verbose:
+            pprint(self.kernel_manager.get_connection_info())
+        # Initialize the code kernel
+        self.kernel = self.kernel_manager.blocking_client()
+        # self.kernel.load_connection_file()
+        self.kernel.start_channels()
+        print("Code kernel started.")
+    def execute(self, code):
+        self.kernel.execute(code)
+        try:
+            shell_msg = self.kernel.get_shell_msg(timeout=self.max_exe_time)
+            io_msg_content = self.kernel.get_iopub_msg(timeout=self.max_exe_time)['content']
+            msg_out_list = []
+            while True:
+                msg_out = io_msg_content
+                if 'name' in msg_out and msg_out['name'] != 'stderr':
+                    msg_out_list.append(msg_out)
+                elif 'data' in msg_out and 'image/png' in msg_out['data']:
+                    msg_out_list.append(msg_out)
+                ### Poll the message
+                try:
+                    io_msg_content = self.kernel.get_iopub_msg(timeout=self.max_exe_time)['content']
+                    if 'execution_state' in io_msg_content and io_msg_content['execution_state'] == 'idle':
+                        msg_out_list.append(msg_out)
+                        break
+                except queue.Empty:
+                    break
+            # msg_out = '\n'.join(msg_out_list)
+            return shell_msg, msg_out_list
+        except Exception as e:
+            print(e)
+            return None
+    def execute_code_(self, code):
+        msg_id = self.kernel.execute(code)
+        # Get the output of the code
+        msg_list = []
+        while True:
+            try:
+                iopub_msg = self.kernel.get_iopub_msg(timeout=self.max_exe_time)
+                msg_list.append(iopub_msg)
+                if iopub_msg['msg_type'] == 'status' and iopub_msg['content'].get('execution_state') == 'idle':
+                    break
+            except:
+                if self.interrupt_signal:
+                    self.kernel_manager.interrupt_kernel()
+                    self.interrupt_signal = False
+                continue
+        all_output = []
+        for iopub_msg in msg_list:
+            if iopub_msg['msg_type'] == 'stream':
+                if iopub_msg['content'].get('name') == 'stdout':
+                    output = iopub_msg['content']['text']
+                    all_output.append(('stdout', output))
+            elif iopub_msg['msg_type'] == 'execute_result':
+                if 'data' in iopub_msg['content']:
+                    if 'text/plain' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['text/plain']
+                        all_output.append(('execute_result_text', output))
+                    if 'text/html' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['text/html']
+                        all_output.append(('execute_result_html', output))
+                    if 'image/png' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['image/png']
+                        all_output.append(('execute_result_png', output))
+                    if 'image/jpeg' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['image/jpeg']
+                        all_output.append(('execute_result_jpeg', output))
+            elif iopub_msg['msg_type'] == 'display_data':
+                if 'data' in iopub_msg['content']:
+                    if 'text/plain' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['text/plain']
+                        all_output.append(('display_text', output))
+                    if 'text/html' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['text/html']
+                        all_output.append(('display_html', output))
+                    if 'image/png' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['image/png']
+                        all_output.append(('display_png', output))
+                    if 'image/jpeg' in iopub_msg['content']['data']:
+                        output = iopub_msg['content']['data']['image/jpeg']
+                        all_output.append(('display_jpeg', output))
+            elif iopub_msg['msg_type'] == 'error':
+                if 'traceback' in iopub_msg['content']:
+                    output = '\n'.join(iopub_msg['content']['traceback'])
+                    all_output.append(('error', output))
+        return all_output
+    def export(self, file_path):
+        nb = nbf.v4.new_notebook()
+        nb.cells = self.executed_cells
+        print("cell: ",nb.cells)
+        with open(file_path, 'w', encoding='utf-8') as f:
+            nbf.write(nb, f)
+        print(f"Notebook exported to {file_path}")
+    def execute_interactive(self, code, verbose=False):
+        shell_msg = self.kernel.execute_interactive(code)
+        if shell_msg is queue.Empty:
+            if verbose:
+                print("Timeout waiting for shell message.")
+        self.check_msg(shell_msg, verbose=verbose)
+        return shell_msg
+    def inspect(self, code, verbose=False):
+        msg_id = self.kernel.inspect(code)
+        shell_msg = self.kernel.get_shell_msg(timeout=30)
+        if shell_msg is queue.Empty:
+            if verbose:
+                print("Timeout waiting for shell message.")
+        self.check_msg(shell_msg, verbose=verbose)
+        return shell_msg
+    def get_error_msg(self, msg, verbose=False) -> str | None:
+        if msg['content']['status'] == 'error':
+            try:
+                error_msg = msg['content']['traceback']
+            except:
+                try:
+                    error_msg = msg['content']['traceback'][-1].strip()
+                except:
+                    error_msg = "Traceback Error"
+            if verbose:
+                print("Error: ", error_msg)
+            return error_msg
+        return None
+    def check_msg(self, msg, verbose=False):
+        status = msg['content']['status']
+        if status == 'ok':
+            if verbose:
+                print("Execution succeeded.")
+        elif status == 'error':
+            for line in msg['content']['traceback']:
+                if verbose:
+                    print(line)
+    def shutdown(self):
+        # Shutdown the backend kernel
+        self.kernel_manager.shutdown_kernel(now=True)
+        print("Backend kernel shutdown.")
+        # Shutdown the code kernel
+        self.kernel.shutdown()
+        print("Code kernel shutdown.")
+    def restart(self):
+        # Restart the backend kernel
+        self.kernel_manager.restart_kernel()
+        print("Backend kernel restarted.")
+    def start(self):
+        # Initialize the code kernel
+        self.kernel = self.kernel_manager.blocking_client()
+        # self.kernel.load_connection_file()
+        self.kernel.start_channels()
+        print("Code kernel started.")
+    def interrupt(self):
+        # Interrupt the backend kernel
+        self.kernel_manager.interrupt_kernel()
+        print("Backend kernel interrupted.")
+    def is_alive(self):
+        return self.kernel.is_alive()
+def b64_2_img(data):
+    buff = BytesIO(base64.b64decode(data))
+    return Image.open(buff)
+def clean_ansi_codes(input_string):
+    ansi_escape = re.compile(r'(\x9B|\x1B\[|\u001b\[)[0-?]*[ -/]*[@-~]')
+    return ansi_escape.sub('', input_string)
+def execute(code, kernel: CodeKernel) -> tuple[str, str | Image.Image]:
+    res = ""
+    res_type = None
+    # code = code.replace("<|observation|>", "")
+    # code = code.replace("<|assistant|>interpreter", "")
+    # code = code.replace("<|assistant|>", "")
+    # code = code.replace("<|user|>", "")
+    # code = code.replace("<|system|>", "")
+    msg, output_list = kernel.execute(code)
+    if msg['metadata']['status'] == "timeout":
+        return res_type, 'Timed out'
+    elif msg['metadata']['status'] == 'error':
+        return res_type, clean_ansi_codes('\n'.join(kernel.get_error_msg(msg, verbose=True))) #todo: change the res_type to error
+    if len(output_list) > 1:
+        res_list = []
+        for output in output_list:
+            if 'text' in output:
+                res_type = "text"
+                res = output['text']
+            elif 'data' in output:
+                for key in output['data']:
+                    if 'text/plain' in key:
+                        res_type = "text"
+                        res = output['data'][key]
+                    elif 'image/png' in key:
+                        res_type = "image"
+                        res = output['data'][key]
+                        break
+            if res_type == "image":
+                #return res_type, b64_2_img(res)
+                res_list.append(b64_2_img(res))
+            elif res_type == "text" or res_type == "traceback": #traceback is error?
+                #res = res
+                res_list.append(res)
+        if len(res_list) > 1 and res_list[-1] == res_list[-2]:
+            res_list.pop()
+        res = '\n'.join(res_list)
+        return res_type, res
+    else:
+        output = output_list[0]
+        if 'text' in output: #output is a dict, what situation is 'text' in output?
+            res_type = "text"
+            res = output['text']
+        elif 'data' in output:
+            for key in output['data']:
+                if 'text/plain' in key: #key = 'text/plain'
+                    res_type = "text"
+                    res = output['data'][key]
+                elif 'image/png' in key: # image will be present as html file?
+                    res_type = "image"
+                    res = output['data'][key]
+                    break
+        if res_type == "image":
+            return res_type, b64_2_img(res)
+        elif res_type == "text" or res_type == "traceback":  # traceback is error?
+            res = res
+        return res_type, res
+# @st.cache_resource
+def get_kernel():
+    kernel = CodeKernel()
+    return kernel
+if __name__ == '__main__':
+    kernel = CodeKernel()
+    table_code = """
+    import pandas as pd
+    data = pd.read_csv('/Users/stephensun/Desktop/pypro/LAMBDA/cache/conv_cache/c1829f26-f6b3-4cbc-96a7-88aef2c33df2-2024-08-13/wine.csv')
+    data.head()
+    """
+    img_code = """
+    import matplotlib.pyplot as plt
+    x = [1, 2, 3, 4, 5]
+    y = [2, 3, 5, 7, 6]
+    plt.plot(x, y)
+    plt.title('Simple Line Plot')
+    plt.xlabel('X-axis')
+    plt.ylabel('Y-axis')
+    plt.show()
+    """
+    res_type, res = execute(img_code, kernel)
+    print(res_type,res)
+    # kernel.export("my_notebook.ipynb")

knowledge_integration/N_correlation_matrix.py ADDED Viewed

	@@ -0,0 +1,510 @@

+#################   This Python code is designed by Yancheng Yuan at National University of Singapore to solve  ##
+#################                          min 0.5*<X-Sigma, X-Sigma>
+#################                          s.t. X_ii =b_i, i=1,2,...,n
+#################                               X>=tau*I (symmetric and positive semi-definite)  ########
+#################
+#################                           based on the algorithm  in                   #################
+#################                 ``A Quadratically Convergent Newton Method fo           r#################
+#################                    Computing the Nearest Correlation Matrix             #################
+#################                           By Houduo Qi and Defeng Sun                   #################
+#################                     SIAM J. Matrix Anal. Appl. 28 (2006) 360--385.
+#################
+#################                       Last modified date: March 19, 2016                #################
+#################                                                                       #################
+#################  The  input arguments  Sigma, b>0, tau>=0, and tol (tolerance error)     #################
+#################                                                                      #################
+#################                                                                        #################
+#################      For the correlation matrix problem, set b = np.ones((n,1))             #################
+#################                                                                       #################
+#################      For a positive definite matrix                                     #################
+#################        set tau = 1.0e-5 for example                                    #################
+#################        set tol = 1.0e-6 or lower if no very high accuracy required     #################
+#################        If the algorithm terminates with the given tol, then it means    ##################
+#################         the relative gap between the  objective function value of the obtained solution  ###
+#################         and the objective function value of the global solution is no more than tol or  ###
+#################         the violation of the feasibility is smaller than tol*(1+np.linalg.norm(b))    ############
+#################
+#################        The outputs include the optimal primal solution, the dual solution and others   ########
+#################                 Diagonal Preconditioner is used                                  #################
+#################
+#################      Send your comments to [email protected]  or [email protected]              #################
+#################
+################# Warning:  Though the code works extremely well, it is your call to use it or not. #################
+import numpy as np
+import scipy.io as sio
+import scipy
+import sys
+import time
+# Generate F(y) Compute the gradient
+def my_gradient(y_input, lamb, p_input, b_0, n):
+    f = 0.0
+    Fy = np.zeros((n, 1))
+    p_input_copy = (p_input.copy()).transpose()
+    for i in range(0, n):
+        p_input_copy[i, :] = ((np.maximum(lamb[i], 0).astype(float)) ** 0.5) * p_input_copy[i, :]
+    for i in range(0, n):
+        Fy[i] = np.sum(p_input_copy[:, i] * p_input_copy[:, i])
+    for i in range(0, n):
+        f = f + np.square((np.maximum(lamb[i], 0)))
+    f = 0.5 * f - np.dot(b_0.transpose(), y_input)
+    return f, Fy
+# use PCA to generate a primal feasible solution checked
+def my_pca(x_input, lamb, p_input, b_0, n):
+    x_pca = x_input
+    lamb = np.asarray(lamb)
+    lp = lamb > 0
+    r = lamb[lp].size
+    if r == 0:
+        x_pca = np.zeros((n, n))
+    elif r == n:
+        x_pca = x_input
+    elif r < (n / 2.0):
+        lamb1 = lamb[lp].copy()
+        lamb1 = lamb1.transpose()
+        lamb1 = np.sqrt(lamb1.astype(float))
+        P1 = p_input[:, 0:r].copy()
+        if r > 1:
+            P1 = np.dot(P1, np.diagflat(lamb1))
+            x_pca = np.dot(P1, P1.transpose())
+        else:
+            x_pca = np.dot(np.dot(np.square(lamb1), P1), P1.transpose())
+    else:
+        lamb2 = -lamb[r:n].copy()
+        lamb2 = np.sqrt(lamb2.astype(float))
+        p_2 = p_input[:, r:n]
+        p_2 = np.dot(p_2, np.diagflat(lamb2))
+        x_pca = x_pca + np.dot(p_2, p_2.transpose())
+    # To make x_pca positive semidefinite with diagonal elements exactly b0
+    d = x_pca.diagonal()
+    d = d.reshape((d.size, 1))
+    d = np.maximum(d, b_0.reshape(d.shape))
+    x_pca = x_pca - np.diagflat(x_pca.diagonal()) + np.diagflat(d)
+    d = d.astype(float) ** (-0.5)
+    d = d * ((np.sqrt(b_0.astype(float))).reshape(d.shape))
+    x_pca = x_pca * (np.dot(d, d.reshape(1, d.size)))
+    return x_pca
+# end of PCA
+# To generate the first order difference of lambda
+# To generate the first order essential part of d
+def my_omega_mat(p_input, lamb, n):
+    idx_idp = np.where(lamb > 0)
+    idx_idp = idx_idp[0]
+    idx_idm = np.setdiff1d(range(0, n), idx_idp)
+    n = lamb.size
+    r = idx_idp.size
+    if r > 0:
+        if r == n:
+            omega_12 = np.ones((n, n))
+        else:
+            s = n - r
+            dp = lamb[0:r].copy()
+            dp = dp.reshape(dp.size, 1)
+            dn = lamb[r:n].copy()
+            dn = dn.reshape((dn.size, 1))
+            omega_12 = np.dot(dp, np.ones((1, s)))
+            omega_12 = omega_12 / (np.dot(np.abs(dp), np.ones((1, s))) + np.dot(np.ones((r, 1)), abs(dn.transpose())))
+            omega_12 = omega_12.reshape((r, s))
+    else:
+        omega_12 = np.array([])
+    return omega_12
+# End of my_omega_mat
+# To generate Jacobian
+def my_jacobian_matrix(x, omega_12, p_input, n):
+    x_result = np.zeros((n, 1))
+    [r, s] = omega_12.shape
+    if r > 0:
+        hmat_1 = p_input[:, 0:r].copy()
+        if r < n / 2.0:
+            i = 0
+            while i < n:
+                hmat_1[i, :] = x[i] * hmat_1[i, :]
+                i = i + 1
+            omega_12 = omega_12 * (np.dot(hmat_1.transpose(), p_input[:, r:n]))
+            hmat = np.dot(hmat_1.transpose(), np.dot(p_input[:, 0:r], p_input[:, 0:r].transpose()))
+            hmat = hmat + np.dot(omega_12, p_input[:, r:n].transpose())
+            hmat = np.vstack((hmat, np.dot(omega_12.transpose(), p_input[:, 0:r].transpose())))
+            i = 0
+            while i < n:
+                x_result[i] = np.dot(p_input[i, :], hmat[:, i])
+                x_result[i] = x_result[i] + 1.0e-10 * x[i]
+                i = i + 1
+        else:
+            if r == n:
+                x_result = 1.0e-10 * x
+            else:
+                hmat_2 = p_input[:, r:n].copy()
+                i = 0
+                while i < n:
+                    hmat_2[i, :] = x[i] * hmat_2[i, :]
+                    i = i + 1
+                omega_12 = np.ones((r, s)) - omega_12
+                omega_12 = omega_12 * (np.dot(p_input[:, 0:r].transpose(), hmat_2))
+                hmat = np.dot(p_input[:, r:n].transpose(), hmat_2)
+                hmat = np.dot(hmat, p_input[:, r:n].transpose())
+                hmat = hmat + np.dot(omega_12.transpose(), p_input[:, 0:r].transpose())
+                hmat = np.vstack((np.dot(omega_12, p_input[:, r:n].transpose()), hmat))
+                i = 0
+                while i < n:
+                    x_result[i] = np.dot(-p_input[i, :], hmat[:, i])
+                    x_result[i] = x[i] + x_result[i] + 1.0e-10 * x[i]
+                    i = i + 1
+    return x_result
+# end of Jacobian
+# PCG Method
+def my_pre_cg(b, tol, maxit, c, omega_12, p_input, n):
+    # Initializations
+    r = b.copy()
+    r = r.reshape(r.size, 1)
+    c = c.reshape(c.size, 1)
+    n2b = np.linalg.norm(b)
+    tolb = tol * n2b
+    p = np.zeros((n, 1))
+    flag = 1
+    iterk = 0
+    relres = 1000
+    # Precondition
+    z = r / c
+    rz_1 = np.dot(r.transpose(), z)
+    rz_2 = 1
+    d = z.copy()
+    # d = d.reshape(z.shape)
+    # CG Iteration
+    for k in range(0, maxit):
+        if k > 0:
+            beta = rz_1 / rz_2
+            d = z + beta * d
+        w = my_jacobian_matrix(d, omega_12, p_input, n)
+        denom = np.dot(d.transpose(), w)
+        iterk = k + 1
+        relres = np.linalg.norm(r) / n2b
+        if denom <= 0:
+            ss = 0  # don't know the usage, check the paper
+            p = d / np.linalg.norm(d)
+            break
+        else:
+            alpha = rz_1 / denom
+            p = p + alpha * d
+            r = r - alpha * w
+        z = r / c
+        if np.linalg.norm(r) <= tolb:  # exit if hmat p = b solved in relative error tolerance
+            iterk = k + 1
+            relres = np.linalg.norm(r) / n2b
+            flag = 0
+            break
+        rz_2 = rz_1
+        rz_1 = np.dot(r.transpose(), z)
+    return p, flag, relres, iterk
+# end of pre_cg
+# to generate the diagonal preconditioner
+def my_precond_matrix(omega_12, p_input, n):
+    [r, s] = omega_12.shape
+    c = np.ones((n, 1))
+    if r > 0:
+        if r < n / 2.0:
+            hmat = (p_input.copy()).transpose()
+            hmat = hmat * hmat
+            hmat_12 = np.dot(hmat[0:r, :].transpose(), omega_12)
+            d = np.ones((r, 1))
+            for i in range(0, n):
+                c_temp = np.dot(d.transpose(), hmat[0:r, i])
+                c_temp = c_temp * hmat[0:r, i]
+                c[i] = np.sum(c_temp)
+                c[i] = c[i] + 2.0 * np.dot(hmat_12[i, :], hmat[r:n, i])
+                if c[i] < 1.0e-8:
+                    c[i] = 1.0e-8
+        else:
+            if r < n:
+                hmat = (p_input.copy()).transpose()
+                hmat = hmat * hmat
+                omega_12 = np.ones((r, s)) - omega_12
+                hmat_12 = np.dot(omega_12, hmat[r:n, :])
+                d = np.ones((s, 1))
+                dd = np.ones((n, 1))
+                for i in range(0, n):
+                    c_temp = np.dot(d.transpose(), hmat[r:n, i])
+                    c[i] = np.sum(c_temp * hmat[r:n, i])
+                    c[i] = c[i] + 2.0 * np.dot(hmat[0:r, i].transpose(), hmat_12[:, i])
+                    alpha = np.sum(hmat[:, i])
+                    c[i] = alpha * np.dot(hmat[:, i].transpose(), dd) - c[i]
+                    if c[i] < 1.0e-8:
+                        c[i] = 1.0e-8
+    return c
+# end of precond_matrix
+# my_issorted()
+def my_issorted(x_input, flag):
+    n = x_input.size
+    tf_value = False
+    if n < 2:
+        tf_value = True
+    else:
+        if flag == 1:
+            i = 0
+            while i < n - 1:
+                if x_input[i] <= x_input[i + 1]:
+                    i = i + 1
+                else:
+                    break
+            if i == n - 1:
+                tf_value = True
+            elif i < n - 1:
+                tf_value = False
+        elif flag == -1:
+            i = n - 1
+            while i > 0:
+                if x_input[i] <= x_input[i - 1]:
+                    i = i - 1
+                else:
+                    break
+            if i == 0:
+                tf_value = True
+            elif i > 0:
+                tf_value = False
+    return tf_value
+# end of my_issorted()
+def my_mexeig(x_input):
+    [n, m] = x_input.shape
+    [lamb, p_x] = np.linalg.eigh(x_input)
+    # lamb = lamb.reshape((lamb.size, 1))
+    p_x = p_x.real
+    lamb = lamb.real
+    if my_issorted(lamb, 1):
+        lamb = lamb[::-1]
+        p_x = np.fliplr(p_x)
+    elif my_issorted(lamb, -1):
+        return p_x, lamb
+    else:
+        idx = np.argsort(-lamb)
+        # lamb_old = lamb   # add for debug
+        lamb = lamb[idx]
+        # p_x_old = p_x   add for debug
+        p_x = p_x[:, idx]
+    lamb = lamb.reshape((n, 1))
+    p_x = p_x.reshape((n, n))
+    return p_x, lamb
+# end of my_mymexeig()
+# begin of the main function
+def my_correlationmatrix(g_input, b_input=None, tau=None, tol=None):
+    print('-- Semismooth Newton-CG method starts -- \n')
+    [n, m] = g_input.shape
+    g_input = g_input.copy()
+    t0 = time.time()  # time start
+    g_input = (g_input + g_input.transpose()) / 2.0
+    b_g = np.ones((n, 1))
+    error_tol = 1.0e-6
+    if b_input is None:
+        tau = 0
+    elif tau is None:
+        b_g = b_input.copy()
+        tau = 0
+    elif tol is None:
+        b_g = b_input.copy() - tau * np.ones((n, 1))
+        g_input = g_input - tau * np.eye(n, n)
+    else:
+        b_g = b_input.copy() - tau * np.ones((n, 1))
+        g_input = g_input - tau * np.eye(n, n)
+        error_tol = np.maximum(1.0e-12, tol)
+    res_b = np.zeros((300, 1))
+    norm_b0 = np.linalg.norm(b_g)
+    y = np.zeros((n, 1))
+    f_y = np.zeros((n, 1))
+    k = 0
+    f_eval = 0
+    iter_whole = 200
+    iter_inner = 20  # maximum number of line search in Newton method
+    maxit = 200  # maximum number of iterations in PCG
+    iterk = 0
+    inner = 0
+    tol_cg = 1.0e-2  # relative accuracy for CGs
+    sigma_1 = 1.0e-4
+    x0 = y.copy()
+    prec_time = 0
+    pcg_time = 0
+    eig_time = 0
+    c = np.ones((n, 1))
+    d = np.zeros((n, 1))
+    val_g = np.sum((g_input.astype(float)) * (g_input.astype(float)))
+    val_g = val_g * 0.5
+    x_result = g_input + np.diagflat(y)
+    x_result = (x_result + x_result.transpose()) / 2.0
+    eig_time0 = time.time()
+    [p_x, lamb] = my_mexeig(x_result)
+    eig_time = eig_time + (time.time() - eig_time0)
+    [f_0, f_y] = my_gradient(y, lamb, p_x, b_g, n)
+    initial_f = val_g - f_0
+    x_result = my_pca(x_result, lamb, p_x, b_g, n)
+    val_obj = np.sum(((x_result - g_input) * (x_result - g_input))) / 2.0
+    gap = (val_obj - initial_f) / (1.0 + np.abs(initial_f) + np.abs(val_obj))
+    f = f_0.copy()
+    f_eval = f_eval + 1
+    b_input = b_g - f_y
+    norm_b = np.linalg.norm(b_input)
+    time_used = time.time() - t0
+    print('Newton-CG: Initial Dual objective function value: %s \n' % initial_f)
+    print('Newton-CG: Initial Primal objective function value: %s \n' % val_obj)
+    print('Newton-CG: Norm of Gradient: %s \n' % norm_b)
+    print('Newton-CG: computing time used so far: %s \n' % time_used)
+    omega_12 = my_omega_mat(p_x, lamb, n)
+    x0 = y.copy()
+    while np.abs(gap) > error_tol and norm_b / (1 + norm_b0) > error_tol and k < iter_whole:
+        prec_time0 = time.time()
+        c = my_precond_matrix(omega_12, p_x, n)
+        prec_time = prec_time + (time.time() - prec_time0)
+        pcg_time0 = time.time()
+        [d, flag, relres, iterk] = my_pre_cg(b_input, tol_cg, maxit, c, omega_12, p_x, n)
+        pcg_time = pcg_time + (time.time() - pcg_time0)
+        print('Newton-CG: Number of CG Iterations=== %s \n' % iterk)
+        if flag == 1:
+            print('=== Not a completed Newton-CG step === \n')
+        slope = np.dot((f_y - b_g).transpose(), d)
+        y = (x0 + d).copy()
+        x_result = g_input + np.diagflat(y)
+        x_result = (x_result + x_result.transpose()) / 2.0
+        eig_time0 = time.time()
+        [p_x, lamb] = my_mexeig(x_result)
+        eig_time = eig_time + (time.time() - eig_time0)
+        [f, f_y] = my_gradient(y, lamb, p_x, b_g, n)
+        k_inner = 0
+        while k_inner <= iter_inner and f > f_0 + sigma_1 * (np.power(0.5, k_inner)) * slope + 1.0e-6:
+            k_inner = k_inner + 1
+            y = x0 + (np.power(0.5, k_inner)) * d
+            x_result = g_input + np.diagflat(y)
+            x_result = (x_result + x_result.transpose()) / 2.0
+            eig_time0 = time.time()
+            [p_x, lamb] = my_mexeig(x_result)
+            eig_time = eig_time + (time.time() - eig_time0)
+            [f, f_y] = my_gradient(y, lamb, p_x, b_g, n)
+        f_eval = f_eval + k_inner + 1
+        x0 = y.copy()
+        f_0 = f.copy()
+        val_dual = val_g - f_0
+        x_result = my_pca(x_result, lamb, p_x, b_g, n)
+        val_obj = np.sum((x_result - g_input) * (x_result - g_input)) / 2.0
+        gap = (val_obj - val_dual) / (1 + np.abs(val_dual) + np.abs(val_obj))
+        print('Newton-CG: The relative duality gap: %s \n' % gap)
+        print('Newton-CG: The Dual objective function value: %s \n' % val_dual)
+        print('Newton-CG: The Primal objective function value: %s \n' % val_obj)
+        b_input = b_g - f_y
+        norm_b = np.linalg.norm(b_input)
+        time_used = time.time() - t0
+        rel_norm_b = norm_b / (1 + norm_b0)
+        print('Newton-CG: Norm of Gradient: %s \n' % norm_b)
+        print('Newton-CG: Norm of Relative Gradient: %s \n' % rel_norm_b)
+        print('Newton-CG: Computing time used so for %s \n' % time_used)
+        res_b[k] = norm_b
+        k = k + 1
+        omega_12 = my_omega_mat(p_x, lamb, n)
+    position_rank = np.maximum(lamb, 0) > 0
+    rank_x = (np.maximum(lamb, 0)[position_rank]).size
+    final_f = val_g - f
+    x_result = x_result + tau * (np.eye(n))
+    time_used = time.time() - t0
+    print('\n')
+    print('Newton-CG: Number of iterations: %s \n' % k)
+    print('Newton-CG: Number of Funtion Evaluation:  =========== %s\n' % f_eval)
+    print('Newton-CG: Final Dual Objective Function value: ========= %s\n' % final_f)
+    print('Newton-CG: Final Primal Objective Function value: ======= %s \n' % val_obj)
+    print('Newton-CG: The final relative duality gap: %s \n' % gap)
+    print('Newton-CG: The rank of the Optimal Solution - tau*I: %s \n' % rank_x)
+    print('Newton-CG: computing time for computing preconditioners: %s \n' % prec_time)
+    print('Newton-CG: computing time for linear system solving (cgs time): %s \n' % pcg_time)
+    print('Newton-CG: computing time for eigenvalue decompositions: =============== %s \n' % eig_time)
+    print('Newton-CG: computing time used for equal weight calibration ============ %s \n' % time_used)
+    return x_result, y
+# end of the main function
+# test
+n = 3000
+data_g_test = scipy.randn(n, n)
+data_g_test = (data_g_test + data_g_test.transpose()) / 2.0
+data_g_test = data_g_test - np.diag(np.diag(data_g_test)) + np.eye(n)
+b = np.ones((n, 1))
+tau = 0
+tol = 1.0e-6
+[x_test_result, y_test_result] = my_correlationmatrix(data_g_test, b, tau, tol)
+print(x_test_result)
+print(y_test_result)

knowledge_integration/knowledge/nn_networks/models.py ADDED Viewed

	@@ -0,0 +1,130 @@

+import math
+from torch import nn
+from torch.nn.utils.parametrizations import spectral_norm
+def initialize_weights(tensor):
+    return tensor.uniform_() * math.sqrt(0.25 / (tensor.shape[0] + tensor.shape[1]))
+class _RRAutoencoder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear_1 = nn.Linear(784, 200)
+        self.linear_2 = nn.Linear(200, 784)
+        self.encoder = self.linear_1
+        self.decoder = self.linear_2
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.decoder(x)
+        return x
+    def clamp(self):
+        pass
+class _NNAutoencoder(_RRAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.linear_1.bias.data.zero_()
+        self.linear_2.bias.data.zero_()
+        self.linear_1.weight = nn.Parameter(
+            initialize_weights(self.linear_1.weight.data)
+        )
+        self.linear_2.weight = nn.Parameter(
+            initialize_weights(self.linear_2.weight.data)
+        )
+    def clamp(self):
+        self.linear_1.weight.data.clamp_(min=0)
+        self.linear_2.weight.data.clamp_(min=0)
+        self.linear_1.bias.data.clamp_(min=0)
+        self.linear_2.bias.data.clamp_(min=0)
+class _PNAutoencoder(_NNAutoencoder):
+    def clamp(self):
+        self.linear_1.weight.data.clamp_(min=1e-3)
+        self.linear_2.weight.data.clamp_(min=1e-3)
+        self.linear_1.bias.data.clamp_(min=0)
+        self.linear_2.bias.data.clamp_(min=0)
+class _NRAutoencoder(_NNAutoencoder):
+    def clamp(self):
+        self.linear_1.weight.data.clamp_(min=0)
+        self.linear_2.weight.data.clamp_(min=0)
+class SigmoidNNAutoencoder(_NNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.Sigmoid())
+        self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+class TanhNNAutoencoder(_NNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+        self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+class TanhPNAutoencoder(_PNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+        self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+class ReLUNNAutoencoder(_NNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.linear_1 = spectral_norm(self.linear_1)
+        self.linear_2 = spectral_norm(self.linear_2)
+        self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+        self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+    def clamp(self):
+        self.linear_1.parametrizations.weight.original.data.clamp_(min=0)
+        self.linear_2.parametrizations.weight.original.data.clamp_(min=0)
+        self.linear_1.bias.data.clamp_(min=0)
+        self.linear_2.bias.data.clamp_(min=0)
+class ReLUPNAutoencoder(_PNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.linear_1 = spectral_norm(self.linear_1)
+        self.linear_2 = spectral_norm(self.linear_2)
+        self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+        self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+    def clamp(self):
+        self.linear_1.parametrizations.weight.original.data.clamp_(min=1e-3)
+        self.linear_2.parametrizations.weight.original.data.clamp_(min=1e-3)
+        self.linear_1.bias.data.clamp_(min=0)
+        self.linear_2.bias.data.clamp_(min=0)
+class TanhSwishNNAutoencoder(_NNAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+        self.decoder = nn.Sequential(self.linear_2, nn.SiLU())
+class ReLUSigmoidNRAutoencoder(_NRAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+        self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+class ReLUSigmoidRRAutoencoder(_RRAutoencoder):
+    def __init__(self):
+        super().__init__()
+        self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+        self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())

knowledge_integration/knowledge/nn_networks/train.py ADDED Viewed

	@@ -0,0 +1,52 @@

+import argparse
+from pathlib import Path
+import torch
+from torch import nn
+from torch import optim
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision import datasets
+from utils import get_network, epoch
+torch.manual_seed(0)
+def train_nn_network(args):
+    p = Path(__file__)
+    weights_path = f"{p.parent}/weights"
+    Path(weights_path).mkdir(parents=True, exist_ok=True)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model = get_network(args.net)
+    model.to(device)
+    mnist_train = datasets.MNIST(
+        ".", train=True, download=True, transform=transforms.ToTensor()
+    )
+    mnist_test = datasets.MNIST(
+        ".", train=False, download=True, transform=transforms.ToTensor()
+    )
+    train_loader = DataLoader(
+        mnist_train, batch_size=args.b, shuffle=True, num_workers=4, pin_memory=True
+    )
+    test_loader = DataLoader(
+        mnist_test, batch_size=args.b, shuffle=False, num_workers=4, pin_memory=True
+    )
+    opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
+    criterion = nn.MSELoss()
+    best_loss = None
+    for i in range(1, args.epochs + 1):
+        train_loss = epoch(train_loader, model, device, criterion, opt)
+        test_loss = epoch(test_loader, model, device, criterion)
+        if best_loss is None or best_loss > test_loss:
+            best_loss = test_loss
+            torch.save(model.state_dict(), f"{weights_path}/{args.net}.pth")
+        print(f"Epoch: {i} | Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
+if __name__ == "__main__":
+    train_nn_network()

knowledge_integration/knowledge/nn_networks/utils.py ADDED Viewed

	@@ -0,0 +1,76 @@

+from models import (
+    SigmoidNNAutoencoder,
+    TanhNNAutoencoder,
+    TanhPNAutoencoder,
+    ReLUNNAutoencoder,
+    ReLUPNAutoencoder,
+    TanhSwishNNAutoencoder,
+    ReLUSigmoidNRAutoencoder,
+    ReLUSigmoidRRAutoencoder,
+)
+from tqdm import tqdm
+def get_network(name):
+    match name:
+        case "nn_sigmoid":
+            return SigmoidNNAutoencoder()
+        case "nn_tanh":
+            return TanhNNAutoencoder()
+        case "pn_tanh":
+            return TanhPNAutoencoder()
+        case "nn_relu":
+            return ReLUNNAutoencoder()
+        case "pn_relu":
+            return ReLUPNAutoencoder()
+        case "nn_tanh_swish":
+            return TanhSwishNNAutoencoder()
+        case "nr_relu_sigmoid":
+            return ReLUSigmoidNRAutoencoder()
+        case "rr_relu_sigmoid":
+            return ReLUSigmoidRRAutoencoder()
+        case _:
+            raise NotImplementedError(
+                f"Autoencoder of name '{name}' currently is not supported"
+            )
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+def epoch(loader, model, device, criterion, opt=None):
+    losses = AverageMeter()
+    if opt is None:
+        model.eval()
+    else:
+        model.train()
+    for inputs, _ in tqdm(loader, leave=False):
+        inputs = inputs.view(-1, 28 * 28).to(device)
+        outputs = model(inputs)
+        loss = criterion(outputs, inputs)
+        if opt:
+            opt.zero_grad(set_to_none=True)
+            loss.backward()
+            opt.step()
+            model.clamp()
+        losses.update(loss.item(), inputs.size(0))
+    return losses.avg

knowledge_integration/knw.py ADDED Viewed

	@@ -0,0 +1,96 @@

+import inspect
+import textwrap
+KNW_INJECTION = {}
+class knw:
+    def __init__(self):
+        self.name = 'knowledge_integration'
+        self.description = 'Integrate knowledge into the LLM.'
+        self.core_function = 'core_function'
+        self.test_case = 'test_case'
+        self.runnable_function = 'runnable_function'
+        self.mode = 'full'
+        self.method_code = {}
+    def get_core_function(self):
+        """
+        Core function of the knowledge integration.
+        """
+        function_name = self.core_function
+        core_function = getattr(self, function_name, None)
+        return textwrap.dedent(core_function())
+        # return self.method_code[self.core_function]
+    def get_runnable_function(self):
+        """
+        Runnable function of the knowledge integration.
+        """
+        function_name = self.runnable_function
+        runnable_function = getattr(self, function_name, None)
+        return textwrap.dedent(runnable_function())
+        #return self.method_code[self.runnable_function]
+    def get_all_code(self):
+        return self.get_core_function(), self.get_runnable_function()
+        #return "Core code:" + self.get_core_function() + "\nOther function code" + self.get_runnable_function()
+    def get_test_case(self):
+        """
+        Test case for the knowledge integration.
+        """
+        return self.method_code[self.test_case]
+    def get_internal_function(self):
+        """
+        All other functions of the core function.
+        """
+        internal_code = ""
+        for name, code in self.method_code.items():
+            if name not in [self.core_function, self.test_case]:
+                internal_code += f"{code}\n"
+        return internal_code
+    def get_function_code(self, function_name):
+        function = getattr(self, function_name, None)
+        if function is None:
+            logger.warning("Method not found.")
+        else:
+            inspect_function = inspect.getsource(function)
+            return inspect_function.replace('self,', '').replace('self.', '').replace('self','')
+    def get_all_function_code(self):
+        all_code = "```python"
+        for name, code in self.method_code.items():
+            all_code += f"\n{code}\n"
+        return all_code+"```"
+    def get_all_function(self):
+        methods = inspect.getmembers(self, predicate=inspect.ismethod)
+        self.method_code = {name: self.get_function_code(name) for name, _ in methods if name not in ['__init__', 'get_all_function', 'get_all_function_code', 'get_core_function', 'get_function_code', 'get_test_case', 'get_internal_function', 'get_fixed_function']}
+        return self.method_code
+    def get_fix_function(self):
+        return self.method_code
+    def remove_outer_indentation(code_str):  # delete the outer indentation
+        lines = code_str.splitlines()
+        non_empty_lines = [line for line in lines if line.strip()]
+        if not non_empty_lines:
+            return code_str
+        min_indent = min(len(line) - len(line.lstrip()) for line in non_empty_lines)
+        aligned_lines = [line[min_indent:] for line in lines]
+        return '\n'.join(aligned_lines)
+if __name__ == '__main__':
+    kwn = knw()
+    # print(kwn.get_entrance_function()) #todo : problem in indent
+    print(kwn.get_all_function_code())
+    # print(instantiate_subclasses(knw))

knowledge_integration/nearest_correlation_matrix.py ADDED Viewed

	@@ -0,0 +1,528 @@

+#################   This Python code is designed by Yancheng Yuan at National University of Singapore to solve  ##
+#################                          min 0.5*<X-Sigma, X-Sigma>
+#################                          s.t. X_ii =b_i, i=1,2,...,n
+#################                               X>=tau*I (symmetric and positive semi-definite)  ########
+#################
+#################                           based on the algorithm  in                   #################
+#################                 ``A Quadratically Convergent Newton Method fo           r#################
+#################                    Computing the Nearest Correlation Matrix             #################
+#################                           By Houduo Qi and Defeng Såun                   #################
+#################                     SIAM J. Matrix Anal. Appl. 28 (2006) 360--385.
+#################
+#################                       Last modified date: March 19, 2016                #################
+#################                                                                       #################
+#################  The  input arguments  Sigma, b>0, tau>=0, and tol (tolerance error)     #################
+#################                                                                      #################
+#################                                                                        #################
+#################      For the correlation matrix problem, set b = np.ones((n,1))             #################
+#################                                                                       #################
+#################      For a positive definite matrix                                     #################
+#################        set tau = 1.0e-5 for example                                    #################
+#################        set tol = 1.0e-6 or lower if no very high accuracy required     #################
+#################        If the algorithm terminates with the given tol, then it means    ##################
+#################         the relative gap between the  objective function value of the obtained solution  ###
+#################         and the objective function value of the global solution is no more than tol or  ###
+#################         the violation of the feasibility is smaller than tol*(1+np.linalg.norm(b))    ############
+#################
+#################        The outputs include the optimal primal solution, the dual solution and others   ########
+#################                 Diagonal Preconditioner is used                                  #################
+#################
+#################      Send your comments to [email protected]  or [email protected]              #################
+#################
+################# Warning:  Though the code works extremely well, it is your call to use it or not. #################
+# from knw import knw, knowledge_injection  # app use
+# import sys
+# sys.path.append('/Users/stephensun/Desktop/pypro/LAMBDA/knowledge_integration')
+from knw import knw  # debug use
+import numpy as np
+import scipy.io as sio
+import scipy
+import sys
+import time
+# @knowledge_injection('nearest_correlation_matrix')
+class nearest_correlation_matrix(knw):
+    # name = 'nearest_correlation_matrix'
+    # description = 'Calculate the nearest correlation matrix.'
+    # entrance_function = 'NearestCorrelationMatrix'
+    # test_case = 'test_ncm'
+    def __init__(self):
+        super().__init__()
+        self.name = 'nearest_correlation_matrix'
+        self.description = 'The function calculates the nearest correlation matrix using the quadratically convergent newton method. Acceptable parameters: Sigma, b>0, tau>=0, and tol (tolerance error) For the correlation matrix problem, set b = np.ones((n,1)).'
+        self.entrance_function = 'NearestCorrelationMatrix'
+        self.test_case = 'test_ncm'
+    def NearestCorrelationMatrix(self, g_input, b_input=None, tau=None, tol=None):
+        print('-- Semismooth Newton-CG method starts -- \n')
+        [n, m] = g_input.shape
+        g_input = g_input.copy()
+        t0 = time.time()  # time start
+        g_input = (g_input + g_input.transpose()) / 2.0
+        b_g = np.ones((n, 1))
+        error_tol = 1.0e-6
+        if b_input is None:
+            tau = 0
+        elif tau is None:
+            b_g = b_input.copy()
+            tau = 0
+        elif tol is None:
+            b_g = b_input.copy() - tau * np.ones((n, 1))
+            g_input = g_input - tau * np.eye(n, n)
+        else:
+            b_g = b_input.copy() - tau * np.ones((n, 1))
+            g_input = g_input - tau * np.eye(n, n)
+            error_tol = np.maximum(1.0e-12, tol)
+        res_b = np.zeros((300, 1))
+        norm_b0 = np.linalg.norm(b_g)
+        y = np.zeros((n, 1))
+        f_y = np.zeros((n, 1))
+        k = 0
+        f_eval = 0
+        iter_whole = 200
+        iter_inner = 20  # maximum number of line search in Newton method
+        maxit = 200  # maximum number of iterations in PCG
+        iterk = 0
+        inner = 0
+        tol_cg = 1.0e-2  # relative accuracy for CGs
+        sigma_1 = 1.0e-4
+        x0 = y.copy()
+        prec_time = 0
+        pcg_time = 0
+        eig_time = 0
+        c = np.ones((n, 1))
+        d = np.zeros((n, 1))
+        val_g = np.sum((g_input.astype(float)) * (g_input.astype(float)))
+        val_g = val_g * 0.5
+        x_result = g_input + np.diagflat(y)
+        x_result = (x_result + x_result.transpose()) / 2.0
+        eig_time0 = time.time()
+        [p_x, lamb] = self.my_mexeig(x_result)
+        eig_time = eig_time + (time.time() - eig_time0)
+        [f_0, f_y] = self.my_gradient(y, lamb, p_x, b_g, n)
+        initial_f = val_g - f_0
+        x_result = self.my_pca(x_result, lamb, p_x, b_g, n)
+        val_obj = np.sum(((x_result - g_input) * (x_result - g_input))) / 2.0
+        gap = (val_obj - initial_f) / (1.0 + np.abs(initial_f) + np.abs(val_obj))
+        f = f_0.copy()
+        f_eval = f_eval + 1
+        b_input = b_g - f_y
+        norm_b = np.linalg.norm(b_input)
+        time_used = time.time() - t0
+        print('Newton-CG: Initial Dual objective function value: %s \n' % initial_f)
+        print('Newton-CG: Initial Primal objective function value: %s \n' % val_obj)
+        print('Newton-CG: Norm of Gradient: %s \n' % norm_b)
+        print('Newton-CG: computing time used so far: %s \n' % time_used)
+        omega_12 = self.my_omega_mat(p_x, lamb, n)
+        x0 = y.copy()
+        while np.abs(gap) > error_tol and norm_b / (1 + norm_b0) > error_tol and k < iter_whole:
+            prec_time0 = time.time()
+            c = self.my_precond_matrix(omega_12, p_x, n)
+            prec_time = prec_time + (time.time() - prec_time0)
+            pcg_time0 = time.time()
+            [d, flag, relres, iterk] = self.my_pre_cg(b_input, tol_cg, maxit, c, omega_12, p_x, n)
+            pcg_time = pcg_time + (time.time() - pcg_time0)
+            print('Newton-CG: Number of CG Iterations=== %s \n' % iterk)
+            if flag == 1:
+                print('=== Not a completed Newton-CG step === \n')
+            slope = np.dot((f_y - b_g).transpose(), d)
+            y = (x0 + d).copy()
+            x_result = g_input + np.diagflat(y)
+            x_result = (x_result + x_result.transpose()) / 2.0
+            eig_time0 = time.time()
+            [p_x, lamb] = self.my_mexeig(x_result)
+            eig_time = eig_time + (time.time() - eig_time0)
+            [f, f_y] = self.my_gradient(y, lamb, p_x, b_g, n)
+            k_inner = 0
+            while k_inner <= iter_inner and f > f_0 + sigma_1 * (np.power(0.5, k_inner)) * slope + 1.0e-6:
+                k_inner = k_inner + 1
+                y = x0 + (np.power(0.5, k_inner)) * d
+                x_result = g_input + np.diagflat(y)
+                x_result = (x_result + x_result.transpose()) / 2.0
+                eig_time0 = time.time()
+                [p_x, lamb] = self.my_mexeig(x_result)
+                eig_time = eig_time + (time.time() - eig_time0)
+                [f, f_y] = self.my_gradient(y, lamb, p_x, b_g, n)
+            f_eval = f_eval + k_inner + 1
+            x0 = y.copy()
+            f_0 = f.copy()
+            val_dual = val_g - f_0
+            x_result = self.my_pca(x_result, lamb, p_x, b_g, n)
+            val_obj = np.sum((x_result - g_input) * (x_result - g_input)) / 2.0
+            gap = (val_obj - val_dual) / (1 + np.abs(val_dual) + np.abs(val_obj))
+            print('Newton-CG: The relative duality gap: %s \n' % gap)
+            print('Newton-CG: The Dual objective function value: %s \n' % val_dual)
+            print('Newton-CG: The Primal objective function value: %s \n' % val_obj)
+            b_input = b_g - f_y
+            norm_b = np.linalg.norm(b_input)
+            time_used = time.time() - t0
+            rel_norm_b = norm_b / (1 + norm_b0)
+            print('Newton-CG: Norm of Gradient: %s \n' % norm_b)
+            print('Newton-CG: Norm of Relative Gradient: %s \n' % rel_norm_b)
+            print('Newton-CG: Computing time used so for %s \n' % time_used)
+            res_b[k] = norm_b
+            k = k + 1
+            omega_12 = self.my_omega_mat(p_x, lamb, n)
+        position_rank = np.maximum(lamb, 0) > 0
+        rank_x = (np.maximum(lamb, 0)[position_rank]).size
+        final_f = val_g - f
+        x_result = x_result + tau * (np.eye(n))
+        time_used = time.time() - t0
+        print('\n')
+        print('Newton-CG: Number of iterations: %s \n' % k)
+        print('Newton-CG: Number of Funtion Evaluation:  =========== %s\n' % f_eval)
+        print('Newton-CG: Final Dual Objective Function value: ========= %s\n' % final_f)
+        print('Newton-CG: Final Primal Objective Function value: ======= %s \n' % val_obj)
+        print('Newton-CG: The final relative duality gap: %s \n' % gap)
+        print('Newton-CG: The rank of the Optimal Solution - tau*I: %s \n' % rank_x)
+        print('Newton-CG: computing time for computing preconditioners: %s \n' % prec_time)
+        print('Newton-CG: computing time for linear system solving (cgs time): %s \n' % pcg_time)
+        print('Newton-CG: computing time for eigenvalue decompositions: =============== %s \n' % eig_time)
+        print('Newton-CG: computing time used for equal weight calibration ============ %s \n' % time_used)
+        return x_result, y
+    def my_gradient(self, y_input, lamb, p_input, b_0, n):
+        f = 0.0
+        Fy = np.zeros((n, 1))
+        p_input_copy = (p_input.copy()).transpose()
+        for i in range(0, n):
+            p_input_copy[i, :] = ((np.maximum(lamb[i], 0).astype(float)) ** 0.5) * p_input_copy[i, :]
+        for i in range(0, n):
+            Fy[i] = np.sum(p_input_copy[:, i] * p_input_copy[:, i])
+        for i in range(0, n):
+            f = f + np.square((np.maximum(lamb[i], 0)))
+        f = 0.5 * f - np.dot(b_0.transpose(), y_input)
+        return f, Fy
+    # use PCA to generate a primal feasible solution checked
+    def my_pca(self, x_input, lamb, p_input, b_0, n):
+        x_pca = x_input
+        lamb = np.asarray(lamb)
+        lp = lamb > 0
+        r = lamb[lp].size
+        if r == 0:
+            x_pca = np.zeros((n, n))
+        elif r == n:
+            x_pca = x_input
+        elif r < (n / 2.0):
+            lamb1 = lamb[lp].copy()
+            lamb1 = lamb1.transpose()
+            lamb1 = np.sqrt(lamb1.astype(float))
+            P1 = p_input[:, 0:r].copy()
+            if r > 1:
+                P1 = np.dot(P1, np.diagflat(lamb1))
+                x_pca = np.dot(P1, P1.transpose())
+            else:
+                x_pca = np.dot(np.dot(np.square(lamb1), P1), P1.transpose())
+        else:
+            lamb2 = -lamb[r:n].copy()
+            lamb2 = np.sqrt(lamb2.astype(float))
+            p_2 = p_input[:, r:n]
+            p_2 = np.dot(p_2, np.diagflat(lamb2))
+            x_pca = x_pca + np.dot(p_2, p_2.transpose())
+        # To make x_pca positive semidefinite with diagonal elements exactly b0
+        d = x_pca.diagonal()
+        d = d.reshape((d.size, 1))
+        d = np.maximum(d, b_0.reshape(d.shape))
+        x_pca = x_pca - np.diagflat(x_pca.diagonal()) + np.diagflat(d)
+        d = d.astype(float) ** (-0.5)
+        d = d * ((np.sqrt(b_0.astype(float))).reshape(d.shape))
+        x_pca = x_pca * (np.dot(d, d.reshape(1, d.size)))
+        return x_pca
+    # end of PCA
+    # To generate the first order difference of lambda
+    # To generate the first order essential part of d
+    def my_omega_mat(self, p_input, lamb, n):
+        idx_idp = np.where(lamb > 0)
+        idx_idp = idx_idp[0]
+        idx_idm = np.setdiff1d(range(0, n), idx_idp)
+        n = lamb.size
+        r = idx_idp.size
+        if r > 0:
+            if r == n:
+                omega_12 = np.ones((n, n))
+            else:
+                s = n - r
+                dp = lamb[0:r].copy()
+                dp = dp.reshape(dp.size, 1)
+                dn = lamb[r:n].copy()
+                dn = dn.reshape((dn.size, 1))
+                omega_12 = np.dot(dp, np.ones((1, s)))
+                omega_12 = omega_12 / (np.dot(np.abs(dp), np.ones((1, s))) + np.dot(np.ones((r, 1)), abs(dn.transpose())))
+                omega_12 = omega_12.reshape((r, s))
+        else:
+            omega_12 = np.array([])
+        return omega_12
+    # End of my_omega_mat
+    # To generate Jacobian
+    def my_jacobian_matrix(self, x, omega_12, p_input, n):
+        x_result = np.zeros((n, 1))
+        [r, s] = omega_12.shape
+        if r > 0:
+            hmat_1 = p_input[:, 0:r].copy()
+            if r < n / 2.0:
+                i = 0
+                while i < n:
+                    hmat_1[i, :] = x[i] * hmat_1[i, :]
+                    i = i + 1
+                omega_12 = omega_12 * (np.dot(hmat_1.transpose(), p_input[:, r:n]))
+                hmat = np.dot(hmat_1.transpose(), np.dot(p_input[:, 0:r], p_input[:, 0:r].transpose()))
+                hmat = hmat + np.dot(omega_12, p_input[:, r:n].transpose())
+                hmat = np.vstack((hmat, np.dot(omega_12.transpose(), p_input[:, 0:r].transpose())))
+                i = 0
+                while i < n:
+                    x_result[i] = np.dot(p_input[i, :], hmat[:, i])
+                    x_result[i] = x_result[i] + 1.0e-10 * x[i]
+                    i = i + 1
+            else:
+                if r == n:
+                    x_result = 1.0e-10 * x
+                else:
+                    hmat_2 = p_input[:, r:n].copy()
+                    i = 0
+                    while i < n:
+                        hmat_2[i, :] = x[i] * hmat_2[i, :]
+                        i = i + 1
+                    omega_12 = np.ones((r, s)) - omega_12
+                    omega_12 = omega_12 * (np.dot(p_input[:, 0:r].transpose(), hmat_2))
+                    hmat = np.dot(p_input[:, r:n].transpose(), hmat_2)
+                    hmat = np.dot(hmat, p_input[:, r:n].transpose())
+                    hmat = hmat + np.dot(omega_12.transpose(), p_input[:, 0:r].transpose())
+                    hmat = np.vstack((np.dot(omega_12, p_input[:, r:n].transpose()), hmat))
+                    i = 0
+                    while i < n:
+                        x_result[i] = np.dot(-p_input[i, :], hmat[:, i])
+                        x_result[i] = x[i] + x_result[i] + 1.0e-10 * x[i]
+                        i = i + 1
+        return x_result
+    # end of Jacobian
+    # PCG Method
+    def my_pre_cg(self, b, tol, maxit, c, omega_12, p_input, n):
+        # Initializations
+        r = b.copy()
+        r = r.reshape(r.size, 1)
+        c = c.reshape(c.size, 1)
+        n2b = np.linalg.norm(b)
+        tolb = tol * n2b
+        p = np.zeros((n, 1))
+        flag = 1
+        iterk = 0
+        relres = 1000
+        # Precondition
+        z = r / c
+        rz_1 = np.dot(r.transpose(), z)
+        rz_2 = 1
+        d = z.copy()
+        # d = d.reshape(z.shape)
+        # CG Iteration
+        for k in range(0, maxit):
+            if k > 0:
+                beta = rz_1 / rz_2
+                d = z + beta * d
+            w = self.my_jacobian_matrix(d, omega_12, p_input, n)
+            denom = np.dot(d.transpose(), w)
+            iterk = k + 1
+            relres = np.linalg.norm(r) / n2b
+            if denom <= 0:
+                ss = 0  # don't know the usage, check the paper
+                p = d / np.linalg.norm(d)
+                break
+            else:
+                alpha = rz_1 / denom
+                p = p + alpha * d
+                r = r - alpha * w
+            z = r / c
+            if np.linalg.norm(r) <= tolb:  # exit if hmat p = b solved in relative error tolerance
+                iterk = k + 1
+                relres = np.linalg.norm(r) / n2b
+                flag = 0
+                break
+            rz_2 = rz_1
+            rz_1 = np.dot(r.transpose(), z)
+        return p, flag, relres, iterk
+    # end of pre_cg
+    # to generate the diagonal preconditioner
+    def my_precond_matrix(self, omega_12, p_input, n):
+        [r, s] = omega_12.shape
+        c = np.ones((n, 1))
+        if r > 0:
+            if r < n / 2.0:
+                hmat = (p_input.copy()).transpose()
+                hmat = hmat * hmat
+                hmat_12 = np.dot(hmat[0:r, :].transpose(), omega_12)
+                d = np.ones((r, 1))
+                for i in range(0, n):
+                    c_temp = np.dot(d.transpose(), hmat[0:r, i])
+                    c_temp = c_temp * hmat[0:r, i]
+                    c[i] = np.sum(c_temp)
+                    c[i] = c[i] + 2.0 * np.dot(hmat_12[i, :], hmat[r:n, i])
+                    if c[i] < 1.0e-8:
+                        c[i] = 1.0e-8
+            else:
+                if r < n:
+                    hmat = (p_input.copy()).transpose()
+                    hmat = hmat * hmat
+                    omega_12 = np.ones((r, s)) - omega_12
+                    hmat_12 = np.dot(omega_12, hmat[r:n, :])
+                    d = np.ones((s, 1))
+                    dd = np.ones((n, 1))
+                    for i in range(0, n):
+                        c_temp = np.dot(d.transpose(), hmat[r:n, i])
+                        c[i] = np.sum(c_temp * hmat[r:n, i])
+                        c[i] = c[i] + 2.0 * np.dot(hmat[0:r, i].transpose(), hmat_12[:, i])
+                        alpha = np.sum(hmat[:, i])
+                        c[i] = alpha * np.dot(hmat[:, i].transpose(), dd) - c[i]
+                        if c[i] < 1.0e-8:
+                            c[i] = 1.0e-8
+        return c
+    # end of precond_matrix
+    # my_issorted()
+    def my_issorted(self, x_input, flag):
+        n = x_input.size
+        tf_value = False
+        if n < 2:
+            tf_value = True
+        else:
+            if flag == 1:
+                i = 0
+                while i < n - 1:
+                    if x_input[i] <= x_input[i + 1]:
+                        i = i + 1
+                    else:
+                        break
+                if i == n - 1:
+                    tf_value = True
+                elif i < n - 1:
+                    tf_value = False
+            elif flag == -1:
+                i = n - 1
+                while i > 0:
+                    if x_input[i] <= x_input[i - 1]:
+                        i = i - 1
+                    else:
+                        break
+                if i == 0:
+                    tf_value = True
+                elif i > 0:
+                    tf_value = False
+        return tf_value
+    # end of my_issorted()
+    def my_mexeig(self, x_input):
+        [n, m] = x_input.shape
+        [lamb, p_x] = np.linalg.eigh(x_input)
+        # lamb = lamb.reshape((lamb.size, 1))
+        p_x = p_x.real
+        lamb = lamb.real
+        if self.my_issorted(lamb, 1):
+            lamb = lamb[::-1]
+            p_x = np.fliplr(p_x)
+        elif self.my_issorted(lamb, -1):
+            return p_x, lamb
+        else:
+            idx = np.argsort(-lamb)
+            # lamb_old = lamb   # add for debug
+            lamb = lamb[idx]
+            # p_x_old = p_x   add for debug
+            p_x = p_x[:, idx]
+        lamb = lamb.reshape((n, 1))
+        p_x = p_x.reshape((n, n))
+        return p_x, lamb
+    def test_ncm(self):
+        # test
+        n = 3000
+        data_g_test = scipy.randn(n, n)
+        data_g_test = (data_g_test + data_g_test.transpose()) / 2.0 #对称
+        data_g_test = data_g_test - np.diag(np.diag(data_g_test)) + np.eye(n) #对角元素都是1
+        b = np.ones((n, 1))
+        tau = 0
+        tol = 1.0e-6
+        [x_test_result, y_test_result] = self.NearestCorrelationMatrix(data_g_test, b, tau, tol)
+        print("The x_test_result: \n", x_test_result)
+        print()
+        print("The y_test_result: \n", y_test_result)
+if __name__ == '__main__':
+    ncm = nearest_correlation_matrix()
+    print(ncm.get_all_function())
+    print(ncm.get_core_function())
+    print(ncm.get_test_case())
+    print(ncm.get_internal_function())

knowledge_integration/nn_network.py ADDED Viewed

	@@ -0,0 +1,261 @@

+from knw import knw
+import textwrap
+class nn_networks(knw):
+    def __init__(self):
+        super().__init__()
+        self.name = 'Fixed_points_of_nonnegative_neural_networks'
+        self.description = 'This is fixed_points_of_nonnegative_neural_networks which used fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors. Variables: networks: nn_sigmoid, learning rate: 5e-3, epochs: 30, wd: 0, b: 64 '
+        self.core_function = 'core'
+        self.runnable_function = 'runnable'
+        self.test_case = 'case_nn_networks'
+        self.mode = 'core'
+    def core(self):
+        case = """
+        args = argparse.ArgumentParser()
+        args.net = 'nn_sigmoid'
+        args.lr = 5e-3
+        args.epochs = 30
+        args.wd = 0
+        args.b = 64
+        train_nn_network(args)
+        """
+        return case
+    def runnable(self):
+        code = """
+        import numpy as np
+        import scipy.io as sio
+        import scipy
+        import sys
+        import time
+        import argparse
+        import torch
+        import math
+        from torch import nn
+        from torch.nn.utils.parametrizations import spectral_norm
+        from pathlib import Path
+        from torch import optim
+        from torch.utils.data import DataLoader
+        from torchvision import transforms
+        from torchvision import datasets
+        from tqdm import tqdm
+        def initialize_weights(tensor):
+            return tensor.uniform_() * math.sqrt(0.25 / (tensor.shape[0] + tensor.shape[1]))
+        class _RRAutoencoder(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.linear_1 = nn.Linear(784, 200)
+                self.linear_2 = nn.Linear(200, 784)
+                self.encoder = self.linear_1
+                self.decoder = self.linear_2
+            def forward(self, x):
+                x = self.encoder(x)
+                x = self.decoder(x)
+                return x
+            def clamp(self):
+                pass
+        class _NNAutoencoder(_RRAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.linear_1.bias.data.zero_()
+                self.linear_2.bias.data.zero_()
+                self.linear_1.weight = nn.Parameter(
+                    initialize_weights(self.linear_1.weight.data)
+                )
+                self.linear_2.weight = nn.Parameter(
+                    initialize_weights(self.linear_2.weight.data)
+                )
+            def clamp(self):
+                self.linear_1.weight.data.clamp_(min=0)
+                self.linear_2.weight.data.clamp_(min=0)
+                self.linear_1.bias.data.clamp_(min=0)
+                self.linear_2.bias.data.clamp_(min=0)
+        class _PNAutoencoder(_NNAutoencoder):
+            def clamp(self):
+                self.linear_1.weight.data.clamp_(min=1e-3)
+                self.linear_2.weight.data.clamp_(min=1e-3)
+                self.linear_1.bias.data.clamp_(min=0)
+                self.linear_2.bias.data.clamp_(min=0)
+        class _NRAutoencoder(_NNAutoencoder):
+            def clamp(self):
+                self.linear_1.weight.data.clamp_(min=0)
+                self.linear_2.weight.data.clamp_(min=0)
+        class SigmoidNNAutoencoder(_NNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.Sigmoid())
+                self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+        class TanhNNAutoencoder(_NNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+                self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+        class TanhPNAutoencoder(_PNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+                self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+        class ReLUNNAutoencoder(_NNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.linear_1 = spectral_norm(self.linear_1)
+                self.linear_2 = spectral_norm(self.linear_2)
+                self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+                self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+            def clamp(self):
+                self.linear_1.parametrizations.weight.original.data.clamp_(min=0)
+                self.linear_2.parametrizations.weight.original.data.clamp_(min=0)
+                self.linear_1.bias.data.clamp_(min=0)
+                self.linear_2.bias.data.clamp_(min=0)
+        class ReLUPNAutoencoder(_PNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.linear_1 = spectral_norm(self.linear_1)
+                self.linear_2 = spectral_norm(self.linear_2)
+                self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+                self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+            def clamp(self):
+                self.linear_1.parametrizations.weight.original.data.clamp_(min=1e-3)
+                self.linear_2.parametrizations.weight.original.data.clamp_(min=1e-3)
+                self.linear_1.bias.data.clamp_(min=0)
+                self.linear_2.bias.data.clamp_(min=0)
+        class TanhSwishNNAutoencoder(_NNAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+                self.decoder = nn.Sequential(self.linear_2, nn.SiLU())
+        class ReLUSigmoidNRAutoencoder(_NRAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+                self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+        class ReLUSigmoidRRAutoencoder(_RRAutoencoder):
+            def __init__(self):
+                super().__init__()
+                self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+                self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+        def get_network(name):
+            match name:
+                case "nn_sigmoid":
+                    return SigmoidNNAutoencoder()
+                case "nn_tanh":
+                    return TanhNNAutoencoder()
+                case "pn_tanh":
+                    return TanhPNAutoencoder()
+                case "nn_relu":
+                    return ReLUNNAutoencoder()
+                case "pn_relu":
+                    return ReLUPNAutoencoder()
+                case "nn_tanh_swish":
+                    return TanhSwishNNAutoencoder()
+                case "nr_relu_sigmoid":
+                    return ReLUSigmoidNRAutoencoder()
+                case "rr_relu_sigmoid":
+                    return ReLUSigmoidRRAutoencoder()
+                case _:
+                    raise NotImplementedError(
+                        f"Autoencoder of name '{name}' currently is not supported"
+                    )
+        class AverageMeter(object):
+            def __init__(self):
+                self.reset()
+            def reset(self):
+                self.val = 0
+                self.avg = 0
+                self.sum = 0
+                self.count = 0
+            def update(self, val, n=1):
+                self.val = val
+                self.sum += val * n
+                self.count += n
+                self.avg = self.sum / self.count
+        def epoch(loader, model, device, criterion, opt=None):
+            losses = AverageMeter()
+            if opt is None:
+                model.eval()
+            else:
+                model.train()
+            for inputs, _ in tqdm(loader, leave=False):
+                inputs = inputs.view(-1, 28 * 28).to(device)
+                outputs = model(inputs)
+                loss = criterion(outputs, inputs)
+                if opt:
+                    opt.zero_grad(set_to_none=True)
+                    loss.backward()
+                    opt.step()
+                    model.clamp()
+                losses.update(loss.item(), inputs.size(0))
+            return losses.avg
+        def train_nn_network(args):
+            # p = Path(__file__)
+            # weights_path = f"{p.parent}/weights"
+            # Path(weights_path).mkdir(parents=True, exist_ok=True)
+            device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+            model = get_network(args.net)
+            model.to(device)
+            mnist_train = datasets.MNIST(
+                ".", train=True, download=True, transform=transforms.ToTensor()
+            )
+            mnist_test = datasets.MNIST(
+                ".", train=False, download=True, transform=transforms.ToTensor()
+            )
+            train_loader = DataLoader(
+                mnist_train, batch_size=args.b, shuffle=True, num_workers=4, pin_memory=True
+            )
+            test_loader = DataLoader(
+                mnist_test, batch_size=args.b, shuffle=False, num_workers=4, pin_memory=True
+            )
+            opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
+            criterion = nn.MSELoss()
+            best_loss = None
+            for i in range(1, args.epochs + 1):
+                train_loss = epoch(train_loader, model, device, criterion, opt)
+                test_loss = epoch(test_loader, model, device, criterion)
+                if best_loss is None or best_loss > test_loss:
+                    best_loss = test_loss
+                    # torch.save(model.state_dict(), f"{weights_path}/{args.net}.pth")
+                print(f"Epoch: {i} | Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
+        """
+        return code
+if __name__ == '__main__':
+    nnn = nn_networks()
+    print(nnn.get_core_function())
+    print(nnn.runnable())

knowledge_integration/pami.py ADDED Viewed

	@@ -0,0 +1,32 @@

+from knw import knw
+class pattern_mining(knw):
+    def __init__(self):
+        super().__init__()
+        self.name = "pami"
+        self.description = "The pami library is a Python library for pattern mining. User can choose many algorithms like FP-growth algorithm."
+        self.core_function = "pami"
+        self.mode = 'full'
+    def pami(self):
+        return """
+        !pip install PAMI
+        from PAMI.frequentPattern.basic import FPGrowth as alg
+        fileURL = "https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv"
+        minSup = 300
+        obj = alg.FPGrowth(iFile=fileURL, minSup=minSup, sep='\t') #here is sep='tab'
+        # obj.startMine()  #deprecated
+        obj.mine()
+        obj.save('frequentPatternsAtMinSupCount300.txt')
+        frequentPatternsDF = obj.getPatternsAsDataFrame()
+        print('Total No of patterns: ' + str(len(frequentPatternsDF)))  # print the total number of patterns
+        print('Runtime: ' + str(obj.getRuntime()))  # measure the runtime
+        print('Memory (RSS): ' + str(obj.getMemoryRSS()))
+        print('Memory (USS): ' + str(obj.getMemoryUSS()))
+        """
+# if __name__ == '__main__':
+#     pami = pami()
+#     print(pami.get_core_function())

knowledge_integration/test.yaml ADDED Viewed

	@@ -0,0 +1,20 @@

+case: "def epoch(loader, model, device, criterion, opt=None):
+            losses = AverageMeter()
+            if opt is None:
+                model.eval()
+            else:
+                model.train()
+            for inputs, _ in tqdm(loader, leave=False):
+                inputs = inputs.view(-1, 28 * 28).to(device)
+                outputs = model(inputs)
+                loss = criterion(outputs, inputs)
+                if opt:
+                    opt.zero_grad(set_to_none=True)
+                    loss.backward()
+                    opt.step()
+                    model.clamp()
+                losses.update(loss.item(), inputs.size(0))
+            return losses.avg"

knw.py ADDED Viewed

	@@ -0,0 +1,96 @@

+import inspect
+import textwrap
+KNW_INJECTION = {}
+class knw:
+    def __init__(self):
+        self.name = 'knowledge_integration'
+        self.description = 'Integrate knowledge into the LLM.'
+        self.core_function = 'core_function'
+        self.test_case = 'test_case'
+        self.runnable_function = 'runnable_function'
+        self.mode = 'full'
+        self.method_code = {}
+    def get_core_function(self):
+        """
+        Core function of the knowledge integration.
+        """
+        function_name = self.core_function
+        core_function = getattr(self, function_name, None)
+        return textwrap.dedent(core_function())
+        # return self.method_code[self.core_function]
+    def get_runnable_function(self):
+        """
+        Runnable function of the knowledge integration.
+        """
+        function_name = self.runnable_function
+        runnable_function = getattr(self, function_name, None)
+        return textwrap.dedent(runnable_function())
+        #return self.method_code[self.runnable_function]
+    def get_all_code(self):
+        return self.get_core_function(), self.get_runnable_function()
+        #return "Core code:" + self.get_core_function() + "\nOther function code" + self.get_runnable_function()
+    def get_test_case(self):
+        """
+        Test case for the knowledge integration.
+        """
+        return self.method_code[self.test_case]
+    def get_internal_function(self):
+        """
+        All other functions of the core function.
+        """
+        internal_code = ""
+        for name, code in self.method_code.items():
+            if name not in [self.core_function, self.test_case]:
+                internal_code += f"{code}\n"
+        return internal_code
+    def get_function_code(self, function_name):
+        function = getattr(self, function_name, None)
+        if function is None:
+            logger.warning("Method not found.")
+        else:
+            inspect_function = inspect.getsource(function)
+            return inspect_function.replace('self,', '').replace('self.', '').replace('self','')
+    def get_all_function_code(self):
+        all_code = "```python"
+        for name, code in self.method_code.items():
+            all_code += f"\n{code}\n"
+        return all_code+"```"
+    def get_all_function(self):
+        methods = inspect.getmembers(self, predicate=inspect.ismethod)
+        self.method_code = {name: self.get_function_code(name) for name, _ in methods if name not in ['__init__', 'get_all_function', 'get_all_function_code', 'get_core_function', 'get_function_code', 'get_test_case', 'get_internal_function', 'get_fixed_function']}
+        return self.method_code
+    def get_fix_function(self):
+        return self.method_code
+    def remove_outer_indentation(code_str):  # delete the outer indentation
+        lines = code_str.splitlines()
+        non_empty_lines = [line for line in lines if line.strip()]
+        if not non_empty_lines:
+            return code_str
+        min_indent = min(len(line) - len(line.lstrip()) for line in non_empty_lines)
+        aligned_lines = [line[min_indent:] for line in lines]
+        return '\n'.join(aligned_lines)
+if __name__ == '__main__':
+    kwn = knw()
+    # print(kwn.get_entrance_function()) #todo : problem in indent
+    print(kwn.get_all_function_code())
+    # print(instantiate_subclasses(knw))

knw_in.py ADDED Viewed

	@@ -0,0 +1,87 @@

+import torch
+import sys
+sys.path.append('/Users/stephensun/Desktop/pypro/LAMBDA/knowledge_integration')
+from sentence_transformers import SentenceTransformer, util
+import numpy as np
+# from knw import KNW_INJECTION, knowledge_injection
+from prompt_engineering.prompts import PMT_KNW_IN_CORE, PMT_KNW_IN_FULL
+from knowledge_integration.nearest_correlation_matrix import nearest_correlation_matrix
+from knowledge_integration.nn_network import nn_networks
+from knowledge_integration.pami import pattern_mining
+from kernel import execute
+KNW_INJECTION = {}
+def knowledge_register():
+    ncm = nearest_correlation_matrix()
+    ncm_key = ncm.name+ncm.description
+    KNW_INJECTION[ncm_key] = ncm
+    nnn = nn_networks()
+    nnn_key = nnn.name+nnn.description
+    KNW_INJECTION[nnn_key] = nnn
+    pami = pattern_mining()
+    pami_key = pami.name+pami.description
+    KNW_INJECTION[pami_key] = pami
+# 初始化句子嵌入模型
+model = SentenceTransformer('all-MiniLM-L6-v2')
+def search_knowledge(user_input, knowledge_embeddings, knowledge_keys):
+    input_embedding = model.encode(user_input, convert_to_tensor=True) #embeding
+    # similarity
+    similarities_list = util.pytorch_cos_sim(input_embedding, knowledge_embeddings)
+    if torch.max(similarities_list) > 0.3:
+        best_match_idx = np.argmax(similarities_list.cpu())
+        best_match_key = knowledge_keys[best_match_idx]
+    else:
+        best_match_key = False
+    return (best_match_key, KNW_INJECTION[best_match_key]) if best_match_key else (False, None)
+def format_code_snaps(knw, kernel):
+    if knw.mode == 'full':
+        core_code = knw.get_core_function()
+        return PMT_KNW_IN_FULL.format(code=core_code)
+    elif knw.mode == 'core':
+        core_code = knw.get_core_function()
+        runnable_code = knw.get_runnable_function()
+        print("Knowledge_integration: core mode, runnable result: ", execute(runnable_code,kernel))
+        retri_knw = PMT_KNW_IN_CORE.format(core=core_code, runnable=runnable_code)
+        return retri_knw
+    else:
+        raise ValueError(f"Invalid mode: {knw.mode}, please choose from ['full', 'core'].")
+        # test_case = knw.get_test_case()
+        # return KNOWLEDGE_INJECTION_PMT_FIXED.format(test_case=test_case)
+def retrieval_knowledge(instruction, kernel): # return code_snaps and mode: 'full' or runnable code in 'core'. Nothing retrieval, return None
+    knowledge_register()
+    knowledge_keys = list(KNW_INJECTION.keys())
+    knowledge_embeddings = model.encode(knowledge_keys, convert_to_tensor=True)
+    best_key, best_knw_object = search_knowledge(instruction, knowledge_embeddings, knowledge_keys)
+    if best_key:
+        return format_code_snaps(best_knw_object, kernel)
+    else:
+        return None
+# def execute_runnable(code):
+#     res_type, res = my_app.conv.run_code(code)
+if __name__ == '__main__':
+    # knowledge_register()
+    # knowledge_keys = list(KNW_INJECTION.keys())
+    # knowledge_embeddings = model.encode(knowledge_keys, convert_to_tensor=True)
+    # user_input = "calculate nearest correlation matrix"
+    # best_key, best_knw_object = search_knowledge(user_input)
+    # print(best_key,best_knw_object)
+    # print(f"Best match key: {best_key}")
+    # print(format_code_snaps(best_knw_object))
+    #print(retrieval_knowledge("calculate nearest correlation matrix", 'full'))
+    print(retrieval_knowledge("Train a fixed points of nonnegative neural networks. Set parameters: networks: nn_sigmoid, learning rate: 5e-3, epochs: 30, wd: 0, b: 64"))
+    #print(retrieval_knowledge("Use pattern mining to find frequent patterns in the dataset. Set parameters: fileURL: https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv, minSup: 300."))

lambda_utils.py ADDED Viewed

	@@ -0,0 +1,320 @@

+import re
+from typing import Tuple, Any
+def extract_code_in_one_cell(text: str, lang) -> tuple[bool, Any]:  #
+    pattern = r'```{lang}([^\n]*)(.*?)```'.format(lang=lang)
+    matches = re.findall(pattern, text, re.DOTALL)
+    if len(matches)>1:
+        code_blocks = ''
+        for match in matches:
+            code_block = match[1]
+            code_blocks += code_block
+        return True, code_blocks
+    elif len(matches):
+        code_block = matches[-1]
+        #if 'python' in code_block[0]:
+        return True, code_block[1]
+    else:
+        return False, ''
+def extract_code(text: str) -> tuple[bool, Any]:
+    pattern = r'```python([^\n]*)(.*?)```'
+    matches = re.findall(pattern, text, re.DOTALL)
+    if len(matches)>1:
+        code_blocks = ''
+        for match in matches:
+            code_block = match[1]
+            code_blocks += code_block
+        return True, code_blocks
+    elif len(matches):
+        code_block = matches[-1]
+        #if 'python' in code_block[0]:
+        return True, code_block[1]
+    else:
+        return False, ''
+def remove_code_blocks(text):
+    pattern = r'(```.*?```)'
+    text = re.sub(pattern, '', text, flags=re.DOTALL)
+    text = text.replace("Execution result:", '')
+    return text
+def check_msg_ua(msg):
+    corrected_lst = []
+    for item in msg:
+        if corrected_lst and corrected_lst[-1] == item:
+            continue
+        corrected_lst.append(item)
+    if corrected_lst and corrected_lst[-1] != 'assistant':
+        corrected_lst.append('assistant')
+    return corrected_lst
+if __name__ == '__main__':
+    pmt = """
+    Retrieval: The retriever found the following pieces of code cloud address the problem. All functions are well-defined and have been executed in the back-end. So, you should directly refer to the core code and modify it as appropriate instead of re-implement any function in runnable function.
+    Core code (All functions have been well-defined in the runnable function):
+    ```core_function
+    args = argparse.ArgumentParser()
+    args.net = 'nn_sigmoid'
+    args.lr = 5e-3
+    args.epochs = 30
+    args.wd = 0
+    args.b = 64
+    train_nn_network(args)
+    ```
+    Runnable function (Have been executed in back-end):
+    ```runnable
+    import numpy as np
+    import scipy.io as sio
+    import scipy
+    import sys
+    import time
+    import argparse
+    import torch
+    import math
+    from torch import nn
+    from torch.nn.utils.parametrizations import spectral_norm
+    from pathlib import Path
+    from torch import optim
+    from torch.utils.data import DataLoader
+    from torchvision import transforms
+    from torchvision import datasets
+    from tqdm import tqdm
+    def initialize_weights(tensor):
+        return tensor.uniform_() * math.sqrt(0.25 / (tensor.shape[0] + tensor.shape[1]))
+    class _RRAutoencoder(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.linear_1 = nn.Linear(784, 200)
+            self.linear_2 = nn.Linear(200, 784)
+            self.encoder = self.linear_1
+            self.decoder = self.linear_2
+        def forward(self, x):
+            x = self.encoder(x)
+            x = self.decoder(x)
+            return x
+        def clamp(self):
+            pass
+    class _NNAutoencoder(_RRAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.linear_1.bias.data.zero_()
+            self.linear_2.bias.data.zero_()
+            self.linear_1.weight = nn.Parameter(
+                initialize_weights(self.linear_1.weight.data)
+            )
+            self.linear_2.weight = nn.Parameter(
+                initialize_weights(self.linear_2.weight.data)
+            )
+        def clamp(self):
+            self.linear_1.weight.data.clamp_(min=0)
+            self.linear_2.weight.data.clamp_(min=0)
+            self.linear_1.bias.data.clamp_(min=0)
+            self.linear_2.bias.data.clamp_(min=0)
+    class _PNAutoencoder(_NNAutoencoder):
+        def clamp(self):
+            self.linear_1.weight.data.clamp_(min=1e-3)
+            self.linear_2.weight.data.clamp_(min=1e-3)
+            self.linear_1.bias.data.clamp_(min=0)
+            self.linear_2.bias.data.clamp_(min=0)
+    class _NRAutoencoder(_NNAutoencoder):
+        def clamp(self):
+            self.linear_1.weight.data.clamp_(min=0)
+            self.linear_2.weight.data.clamp_(min=0)
+    class SigmoidNNAutoencoder(_NNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.Sigmoid())
+            self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+    class TanhNNAutoencoder(_NNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+            self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+    class TanhPNAutoencoder(_PNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+            self.decoder = nn.Sequential(self.linear_2, nn.Tanh())
+    class ReLUNNAutoencoder(_NNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.linear_1 = spectral_norm(self.linear_1)
+            self.linear_2 = spectral_norm(self.linear_2)
+            self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+            self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+        def clamp(self):
+            self.linear_1.parametrizations.weight.original.data.clamp_(min=0)
+            self.linear_2.parametrizations.weight.original.data.clamp_(min=0)
+            self.linear_1.bias.data.clamp_(min=0)
+            self.linear_2.bias.data.clamp_(min=0)
+    class ReLUPNAutoencoder(_PNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.linear_1 = spectral_norm(self.linear_1)
+            self.linear_2 = spectral_norm(self.linear_2)
+            self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+            self.decoder = nn.Sequential(self.linear_2, nn.ReLU())
+        def clamp(self):
+            self.linear_1.parametrizations.weight.original.data.clamp_(min=1e-3)
+            self.linear_2.parametrizations.weight.original.data.clamp_(min=1e-3)
+            self.linear_1.bias.data.clamp_(min=0)
+            self.linear_2.bias.data.clamp_(min=0)
+    class TanhSwishNNAutoencoder(_NNAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.Tanh())
+            self.decoder = nn.Sequential(self.linear_2, nn.SiLU())
+    class ReLUSigmoidNRAutoencoder(_NRAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+            self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+    class ReLUSigmoidRRAutoencoder(_RRAutoencoder):
+        def __init__(self):
+            super().__init__()
+            self.encoder = nn.Sequential(self.linear_1, nn.ReLU())
+            self.decoder = nn.Sequential(self.linear_2, nn.Sigmoid())
+    def get_network(name):
+        match name:
+            case "nn_sigmoid":
+                return SigmoidNNAutoencoder()
+            case "nn_tanh":
+                return TanhNNAutoencoder()
+            case "pn_tanh":
+                return TanhPNAutoencoder()
+            case "nn_relu":
+                return ReLUNNAutoencoder()
+            case "pn_relu":
+                return ReLUPNAutoencoder()
+            case "nn_tanh_swish":
+                return TanhSwishNNAutoencoder()
+            case "nr_relu_sigmoid":
+                return ReLUSigmoidNRAutoencoder()
+            case "rr_relu_sigmoid":
+                return ReLUSigmoidRRAutoencoder()
+            case _:
+                raise NotImplementedError(
+                    f"Autoencoder of name '{name}' currently is not supported"
+                )
+    class AverageMeter(object):
+        def __init__(self):
+            self.reset()
+        def reset(self):
+            self.val = 0
+            self.avg = 0
+            self.sum = 0
+            self.count = 0
+        def update(self, val, n=1):
+            self.val = val
+            self.sum += val * n
+            self.count += n
+            self.avg = self.sum / self.count
+    def epoch(loader, model, device, criterion, opt=None):
+        losses = AverageMeter()
+        if opt is None:
+            model.eval()
+        else:
+            model.train()
+        for inputs, _ in tqdm(loader, leave=False):
+            inputs = inputs.view(-1, 28 * 28).to(device)
+            outputs = model(inputs)
+            loss = criterion(outputs, inputs)
+            if opt:
+                opt.zero_grad(set_to_none=True)
+                loss.backward()
+                opt.step()
+                model.clamp()
+            losses.update(loss.item(), inputs.size(0))
+        return losses.avg
+    def train_nn_network(args):
+        # p = Path(__file__)
+        # weights_path = f"{p.parent}/weights"
+        # Path(weights_path).mkdir(parents=True, exist_ok=True)
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        model = get_network(args.net)
+        model.to(device)
+        mnist_train = datasets.MNIST(
+            ".", train=True, download=True, transform=transforms.ToTensor()
+        )
+        mnist_test = datasets.MNIST(
+            ".", train=False, download=True, transform=transforms.ToTensor()
+        )
+        train_loader = DataLoader(
+            mnist_train, batch_size=args.b, shuffle=True, num_workers=4, pin_memory=True
+        )
+        test_loader = DataLoader(
+            mnist_test, batch_size=args.b, shuffle=False, num_workers=4, pin_memory=True
+        )
+        opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)
+        criterion = nn.MSELoss()
+        best_loss = None
+        for i in range(1, args.epochs + 1):
+            train_loss = epoch(train_loader, model, device, criterion, opt)
+            test_loss = epoch(test_loader, model, device, criterion)
+            if best_loss is None or best_loss > test_loss:
+                best_loss = test_loss
+                # torch.save(model.state_dict(), f"{weights_path}/{args.net}.pth")
+            print(f"Epoch: {i} | Train Loss: {train_loss:.4f} | Test Loss: {test_loss:.4f}")
+    ```
+    Your modified code:
+    ```python
+    import argparse
+    args = argparse.ArgumentParser()
+    args.net = 'nn_sigmoid'
+    args.lr = 5e-3
+    args.epochs = 5  # Changed epochs to 5 as per the request
+    args.wd = 0
+    args.b = 64
+    train_nn_network(args)
+    ```
+    """
+    print(extract_code(pmt))
+    print(extract_code_in_one_cell(pmt,'python'))

myenv/Lib/site-packages/pip-23.2.1.dist-info/AUTHORS.txt ADDED Viewed

	@@ -0,0 +1,738 @@

+@Switch01
+A_Rog
+Aakanksha Agrawal
+Abhinav Sagar
+ABHYUDAY PRATAP SINGH
+abs51295
+AceGentile
+Adam Chainz
+Adam Tse
+Adam Wentz
+admin
+Adrien Morison
+ahayrapetyan
+Ahilya
+AinsworthK
+Akash Srivastava
+Alan Yee
+Albert Tugushev
+Albert-Guan
+albertg
+Alberto Sottile
+Aleks Bunin
+Alethea Flowers
+Alex Gaynor
+Alex Grönholm
+Alex Hedges
+Alex Loosley
+Alex Morega
+Alex Stachowiak
+Alexander Shtyrov
+Alexandre Conrad
+Alexey Popravka
+Alli
+Ami Fischman
+Ananya Maiti
+Anatoly Techtonik
+Anders Kaseorg
+Andre Aguiar
+Andreas Lutro
+Andrei Geacar
+Andrew Gaul
+Andrew Shymanel
+Andrey Bienkowski
+Andrey Bulgakov
+Andrés Delfino
+Andy Freeland
+Andy Kluger
+Ani Hayrapetyan
+Aniruddha Basak
+Anish Tambe
+Anrs Hu
+Anthony Sottile
+Antoine Musso
+Anton Ovchinnikov
+Anton Patrushev
+Antonio Alvarado Hernandez
+Antony Lee
+Antti Kaihola
+Anubhav Patel
+Anudit Nagar
+Anuj Godase
+AQNOUCH Mohammed
+AraHaan
+Arindam Choudhury
+Armin Ronacher
+Artem
+Arun Babu Neelicattu
+Ashley Manton
+Ashwin Ramaswami
+atse
+Atsushi Odagiri
+Avinash Karhana
+Avner Cohen
+Awit (Ah-Wit) Ghirmai
+Baptiste Mispelon
+Barney Gale
+barneygale
+Bartek Ogryczak
+Bastian Venthur
+Ben Bodenmiller
+Ben Darnell
+Ben Hoyt
+Ben Mares
+Ben Rosser
+Bence Nagy
+Benjamin Peterson
+Benjamin VanEvery
+Benoit Pierre
+Berker Peksag
+Bernard
+Bernard Tyers
+Bernardo B. Marques
+Bernhard M. Wiedemann
+Bertil Hatt
+Bhavam Vidyarthi
+Blazej Michalik
+Bogdan Opanchuk
+BorisZZZ
+Brad Erickson
+Bradley Ayers
+Brandon L. Reiss
+Brandt Bucher
+Brett Randall
+Brett Rosen
+Brian Cristante
+Brian Rosner
+briantracy
+BrownTruck
+Bruno Oliveira
+Bruno Renié
+Bruno S
+Bstrdsmkr
+Buck Golemon
+burrows
+Bussonnier Matthias
+bwoodsend
+c22
+Caleb Martinez
+Calvin Smith
+Carl Meyer
+Carlos Liam
+Carol Willing
+Carter Thayer
+Cass
+Chandrasekhar Atina
+Chih-Hsuan Yen
+Chris Brinker
+Chris Hunt
+Chris Jerdonek
+Chris Kuehl
+Chris McDonough
+Chris Pawley
+Chris Pryer
+Chris Wolfe
+Christian Clauss
+Christian Heimes
+Christian Oudard
+Christoph Reiter
+Christopher Hunt
+Christopher Snyder
+cjc7373
+Clark Boylan
+Claudio Jolowicz
+Clay McClure
+Cody
+Cody Soyland
+Colin Watson
+Collin Anderson
+Connor Osborn
+Cooper Lees
+Cooper Ry Lees
+Cory Benfield
+Cory Wright
+Craig Kerstiens
+Cristian Sorinel
+Cristina
+Cristina Muñoz
+Curtis Doty
+cytolentino
+Daan De Meyer
+Damian
+Damian Quiroga
+Damian Shaw
+Dan Black
+Dan Savilonis
+Dan Sully
+Dane Hillard
+daniel
+Daniel Collins
+Daniel Hahler
+Daniel Holth
+Daniel Jost
+Daniel Katz
+Daniel Shaulov
+Daniele Esposti
+Daniele Nicolodi
+Daniele Procida
+Daniil Konovalenko
+Danny Hermes
+Danny McClanahan
+Darren Kavanagh
+Dav Clark
+Dave Abrahams
+Dave Jones
+David Aguilar
+David Black
+David Bordeynik
+David Caro
+David D Lowe
+David Evans
+David Hewitt
+David Linke
+David Poggi
+David Pursehouse
+David Runge
+David Tucker
+David Wales
+Davidovich
+Deepak Sharma
+Deepyaman Datta
+Denise Yu
+derwolfe
+Desetude
+Devesh Kumar Singh
+Diego Caraballo
+Diego Ramirez
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+Dimitri Merejkowsky
+Dimitri Papadopoulos
+Dirk Stolle
+Dmitry Gladkov
+Dmitry Volodin
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+Ed Morley
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+Eric Hanchrow
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+GOTO Hayato
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+Joe Michelini
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+Jon Parise
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+Jonathan Herbert
+Joonatan Partanen
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+Jorge Niedbalski
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+Josh Bronson
+Josh Hansen
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+Juan Luis Cano Rodríguez
+Juanjo Bazán
+Judah Rand
+Julian Berman
+Julian Gethmann
+Julien Demoor
+Jussi Kukkonen
+jwg4
+Jyrki Pulliainen
+Kai Chen
+Kai Mueller
+Kamal Bin Mustafa
+kasium
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+Kelsey Hightower
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+Kenneth Reitz
+Kevin Burke
+Kevin Carter
+Kevin Frommelt
+Kevin R Patterson
+Kexuan Sun
+Kit Randel
+Klaas van Schelven
+KOLANICH
+kpinc
+Krishna Oza
+Kumar McMillan
+Kyle Persohn
+lakshmanaram
+Laszlo Kiss-Kollar
+Laurent Bristiel
+Laurent LAPORTE
+Laurie O
+Laurie Opperman
+layday
+Leon Sasson
+Lev Givon
+Lincoln de Sousa
+Lipis
+lorddavidiii
+Loren Carvalho
+Lucas Cimon
+Ludovic Gasc
+Lukas Juhrich
+Luke Macken
+Luo Jiebin
+luojiebin
+luz.paz
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+Mike Hendricks
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+montefra
+Monty Taylor
+Muha Ajjan‮
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+Nahuel Ambrosini
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+Nate Prewitt
+Nathan Houghton
+Nathaniel J. Smith
+Nehal J Wani
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+Nicole Harris
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+Nikhil Ladha
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+Nikolay Korolev
+Nipunn Koorapati
+Nitesh Sharma
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+Noah Gorny
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+Oliver Mannion
+Oliver Tonnhofer
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+Olivier Grisel
+Ollie Rutherfurd
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+Omry Yadan
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+Oren Held
+Oscar Benjamin
+Oz N Tiram
+Pachwenko
+Patrick Dubroy
+Patrick Jenkins
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+Paul Nasrat
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+Paul van der Linden
+Paulus Schoutsen
+Pavel Safronov
+Pavithra Eswaramoorthy
+Pawel Jasinski
+Paweł Szramowski
+Pekka Klärck
+Peter Gessler
+Peter Lisák
+Peter Waller
+petr-tik
+Phaneendra Chiruvella
+Phil Elson
+Phil Freo
+Phil Pennock
+Phil Whelan
+Philip Jägenstedt
+Philip Molloy
+Philippe Ombredanne
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+Pierre-Yves Rofes
+Pieter Degroote
+pip
+Prabakaran Kumaresshan
+Prabhjyotsing Surjit Singh Sodhi
+Prabhu Marappan
+Pradyun Gedam
+Prashant Sharma
+Pratik Mallya
+pre-commit-ci[bot]
+Preet Thakkar
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+Przemek Wrzos
+Pulkit Goyal
+q0w
+Qiangning Hong
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+Quentin Pradet
+R. David Murray
+Rafael Caricio
+Ralf Schmitt
+Razzi Abuissa
+rdb
+Reece Dunham
+Remi Rampin
+Rene Dudfield
+Riccardo Magliocchetti
+Riccardo Schirone
+Richard Jones
+Richard Si
+Ricky Ng-Adam
+Rishi
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+Robert Collins
+Robert McGibbon
+Robert Pollak
+Robert T. McGibbon
+robin elisha robinson
+Roey Berman
+Rohan Jain
+Roman Bogorodskiy
+Roman Donchenko
+Romuald Brunet
+ronaudinho
+Ronny Pfannschmidt
+Rory McCann
+Ross Brattain
+Roy Wellington Ⅳ
+Ruairidh MacLeod
+Russell Keith-Magee
+Ryan Shepherd
+Ryan Wooden
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+Sachi King
+Salvatore Rinchiera
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+Savio Jomton
+schlamar
+Scott Kitterman
+Sean
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+Sebastian Jordan
+Sebastian Schaetz
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+SeongSoo Cho
+Sergey Vasilyev
+Seth Michael Larson
+Seth Woodworth
+Shantanu
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+Shivansh-007
+Shlomi Fish
+Shovan Maity
+Simeon Visser
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+Simon Pichugin
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+sinscary
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+socketubs
+Sorin Sbarnea
+Srinivas Nyayapati
+Stavros Korokithakis
+Stefan Scherfke
+Stefano Rivera
+Stephan Erb
+Stephen Rosen
+stepshal
+Steve (Gadget) Barnes
+Steve Barnes
+Steve Dower
+Steve Kowalik
+Steven Myint
+Steven Silvester
+stonebig
+Stéphane Bidoul
+Stéphane Bidoul (ACSONE)
+Stéphane Klein
+Sumana Harihareswara
+Surbhi Sharma
+Sviatoslav Sydorenko
+Swat009
+Sylvain
+Takayuki SHIMIZUKAWA
+Taneli Hukkinen
+tbeswick
+Thiago
+Thijs Triemstra
+Thomas Fenzl
+Thomas Grainger
+Thomas Guettler
+Thomas Johansson
+Thomas Kluyver
+Thomas Smith
+Thomas VINCENT
+Tim D. Smith
+Tim Gates
+Tim Harder
+Tim Heap
+tim smith
+tinruufu
+Tobias Hermann
+Tom Forbes
+Tom Freudenheim
+Tom V
+Tomas Hrnciar
+Tomas Orsava
+Tomer Chachamu
+Tommi Enenkel | AnB
+Tomáš Hrnčiar
+Tony Beswick
+Tony Narlock
+Tony Zhaocheng Tan
+TonyBeswick
+toonarmycaptain
+Toshio Kuratomi
+toxinu
+Travis Swicegood
+Tushar Sadhwani
+Tzu-ping Chung
+Valentin Haenel
+Victor Stinner
+victorvpaulo
+Vikram - Google
+Viktor Szépe
+Ville Skyttä
+Vinay Sajip
+Vincent Philippon
+Vinicyus Macedo
+Vipul Kumar
+Vitaly Babiy
+Vladimir Rutsky
+W. Trevor King
+Wil Tan
+Wilfred Hughes
+William Edwards
+William ML Leslie
+William T Olson
+William Woodruff
+Wilson Mo
+wim glenn
+Winson Luk
+Wolfgang Maier
+Wu Zhenyu
+XAMES3
+Xavier Fernandez
+xoviat
+xtreak
+YAMAMOTO Takashi
+Yen Chi Hsuan
+Yeray Diaz Diaz
+Yoval P
+Yu Jian
+Yuan Jing Vincent Yan
+Yusuke Hayashi
+Zearin
+Zhiping Deng
+ziebam
+Zvezdan Petkovic
+Łukasz Langa
+Роман Донченко
+Семён Марьясин
+‮rekcäH nitraM‮

myenv/Lib/site-packages/pip-23.2.1.dist-info/INSTALLER ADDED Viewed

	@@ -0,0 +1 @@


1	+ pip

myenv/Lib/site-packages/pip-23.2.1.dist-info/LICENSE.txt ADDED Viewed

	@@ -0,0 +1,20 @@

+Copyright (c) 2008-present The pip developers (see AUTHORS.txt file)
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

myenv/Lib/site-packages/pip-23.2.1.dist-info/METADATA ADDED Viewed

	@@ -0,0 +1,90 @@

+Metadata-Version: 2.1
+Name: pip
+Version: 23.2.1
+Summary: The PyPA recommended tool for installing Python packages.
+Home-page: https://pip.pypa.io/
+Author: The pip developers
+Author-email: [email protected]
+License: MIT
+Project-URL: Documentation, https://pip.pypa.io
+Project-URL: Source, https://github.com/pypa/pip
+Project-URL: Changelog, https://pip.pypa.io/en/stable/news/
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Topic :: Software Development :: Build Tools
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Requires-Python: >=3.7
+License-File: LICENSE.txt
+License-File: AUTHORS.txt
+pip - The Python Package Installer
+==================================
+.. image:: https://img.shields.io/pypi/v/pip.svg
+   :target: https://pypi.org/project/pip/
+.. image:: https://readthedocs.org/projects/pip/badge/?version=latest
+   :target: https://pip.pypa.io/en/latest
+pip is the `package installer`_ for Python. You can use pip to install packages from the `Python Package Index`_ and other indexes.
+Please take a look at our documentation for how to install and use pip:
+* `Installation`_
+* `Usage`_
+We release updates regularly, with a new version every 3 months. Find more details in our documentation:
+* `Release notes`_
+* `Release process`_
+In pip 20.3, we've `made a big improvement to the heart of pip`_; `learn more`_. We want your input, so `sign up for our user experience research studies`_ to help us do it right.
+**Note**: pip 21.0, in January 2021, removed Python 2 support, per pip's `Python 2 support policy`_. Please migrate to Python 3.
+If you find bugs, need help, or want to talk to the developers, please use our mailing lists or chat rooms:
+* `Issue tracking`_
+* `Discourse channel`_
+* `User IRC`_
+If you want to get involved head over to GitHub to get the source code, look at our development documentation and feel free to jump on the developer mailing lists and chat rooms:
+* `GitHub page`_
+* `Development documentation`_
+* `Development IRC`_
+Code of Conduct
+---------------
+Everyone interacting in the pip project's codebases, issue trackers, chat
+rooms, and mailing lists is expected to follow the `PSF Code of Conduct`_.
+.. _package installer: https://packaging.python.org/guides/tool-recommendations/
+.. _Python Package Index: https://pypi.org
+.. _Installation: https://pip.pypa.io/en/stable/installation/
+.. _Usage: https://pip.pypa.io/en/stable/
+.. _Release notes: https://pip.pypa.io/en/stable/news.html
+.. _Release process: https://pip.pypa.io/en/latest/development/release-process/
+.. _GitHub page: https://github.com/pypa/pip
+.. _Development documentation: https://pip.pypa.io/en/latest/development
+.. _made a big improvement to the heart of pip: https://pyfound.blogspot.com/2020/11/pip-20-3-new-resolver.html
+.. _learn more: https://pip.pypa.io/en/latest/user_guide/#changes-to-the-pip-dependency-resolver-in-20-3-2020
+.. _sign up for our user experience research studies: https://pyfound.blogspot.com/2020/03/new-pip-resolver-to-roll-out-this-year.html
+.. _Python 2 support policy: https://pip.pypa.io/en/latest/development/release-process/#python-2-support
+.. _Issue tracking: https://github.com/pypa/pip/issues
+.. _Discourse channel: https://discuss.python.org/c/packaging
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+.. _PSF Code of Conduct: https://github.com/pypa/.github/blob/main/CODE_OF_CONDUCT.md

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+pip/_vendor/webencodings/__pycache__/tests.cpython-312.pyc,,
+pip/_vendor/webencodings/__pycache__/x_user_defined.cpython-312.pyc,,
+pip/_vendor/webencodings/labels.py,sha256=4AO_KxTddqGtrL9ns7kAPjb0CcN6xsCIxbK37HY9r3E,8979
+pip/_vendor/webencodings/mklabels.py,sha256=GYIeywnpaLnP0GSic8LFWgd0UVvO_l1Nc6YoF-87R_4,1305
+pip/_vendor/webencodings/tests.py,sha256=OtGLyjhNY1fvkW1GvLJ_FV9ZoqC9Anyjr7q3kxTbzNs,6563
+pip/_vendor/webencodings/x_user_defined.py,sha256=yOqWSdmpytGfUgh_Z6JYgDNhoc-BAHyyeeT15Fr42tM,4307
+pip/py.typed,sha256=EBVvvPRTn_eIpz5e5QztSCdrMX7Qwd7VP93RSoIlZ2I,286

myenv/Lib/site-packages/pip-23.2.1.dist-info/REQUESTED ADDED Viewed

File without changes

myenv/Lib/site-packages/pip-23.2.1.dist-info/WHEEL ADDED Viewed

	@@ -0,0 +1,5 @@

+Wheel-Version: 1.0
+Generator: bdist_wheel (0.40.0)
+Root-Is-Purelib: true
+Tag: py3-none-any

myenv/Lib/site-packages/pip-23.2.1.dist-info/entry_points.txt ADDED Viewed

	@@ -0,0 +1,4 @@

+[console_scripts]
+pip = pip._internal.cli.main:main
+pip3 = pip._internal.cli.main:main
+pip3.11 = pip._internal.cli.main:main

myenv/Lib/site-packages/pip-23.2.1.dist-info/top_level.txt ADDED Viewed

	@@ -0,0 +1 @@


1	+ pip

myenv/Lib/site-packages/pip/__init__.py ADDED Viewed

	@@ -0,0 +1,13 @@

+from typing import List, Optional
+__version__ = "23.2.1"
+def main(args: Optional[List[str]] = None) -> int:
+    """This is an internal API only meant for use by pip's own console scripts.
+    For additional details, see https://github.com/pypa/pip/issues/7498.
+    """
+    from pip._internal.utils.entrypoints import _wrapper
+    return _wrapper(args)

myenv/Lib/site-packages/pip/__main__.py ADDED Viewed

	@@ -0,0 +1,24 @@

+import os
+import sys
+# Remove '' and current working directory from the first entry
+# of sys.path, if present to avoid using current directory
+# in pip commands check, freeze, install, list and show,
+# when invoked as python -m pip <command>
+if sys.path[0] in ("", os.getcwd()):
+    sys.path.pop(0)
+# If we are running from a wheel, add the wheel to sys.path
+# This allows the usage python pip-*.whl/pip install pip-*.whl
+if __package__ == "":
+    # __file__ is pip-*.whl/pip/__main__.py
+    # first dirname call strips of '/__main__.py', second strips off '/pip'
+    # Resulting path is the name of the wheel itself
+    # Add that to sys.path so we can import pip
+    path = os.path.dirname(os.path.dirname(__file__))
+    sys.path.insert(0, path)
+if __name__ == "__main__":
+    from pip._internal.cli.main import main as _main
+    sys.exit(_main())

myenv/Lib/site-packages/pip/__pip-runner__.py ADDED Viewed

	@@ -0,0 +1,50 @@

+"""Execute exactly this copy of pip, within a different environment.
+This file is named as it is, to ensure that this module can't be imported via
+an import statement.
+"""
+# /!\ This version compatibility check section must be Python 2 compatible. /!\
+import sys
+# Copied from setup.py
+PYTHON_REQUIRES = (3, 7)
+def version_str(version):  # type: ignore
+    return ".".join(str(v) for v in version)
+if sys.version_info[:2] < PYTHON_REQUIRES:
+    raise SystemExit(
+        "This version of pip does not support python {} (requires >={}).".format(
+            version_str(sys.version_info[:2]), version_str(PYTHON_REQUIRES)
+        )
+    )
+# From here on, we can use Python 3 features, but the syntax must remain
+# Python 2 compatible.
+import runpy  # noqa: E402
+from importlib.machinery import PathFinder  # noqa: E402
+from os.path import dirname  # noqa: E402
+PIP_SOURCES_ROOT = dirname(dirname(__file__))
+class PipImportRedirectingFinder:
+    @classmethod
+    def find_spec(self, fullname, path=None, target=None):  # type: ignore
+        if fullname != "pip":
+            return None
+        spec = PathFinder.find_spec(fullname, [PIP_SOURCES_ROOT], target)
+        assert spec, (PIP_SOURCES_ROOT, fullname)
+        return spec
+sys.meta_path.insert(0, PipImportRedirectingFinder())
+assert __name__ == "__main__", "Cannot run __pip-runner__.py as a non-main module"
+runpy.run_module("pip", run_name="__main__", alter_sys=True)

myenv/Lib/site-packages/pip/_internal/__init__.py ADDED Viewed

	@@ -0,0 +1,19 @@

+from typing import List, Optional
+import pip._internal.utils.inject_securetransport  # noqa
+from pip._internal.utils import _log
+# init_logging() must be called before any call to logging.getLogger()
+# which happens at import of most modules.
+_log.init_logging()
+def main(args: (Optional[List[str]]) = None) -> int:
+    """This is preserved for old console scripts that may still be referencing
+    it.
+    For additional details, see https://github.com/pypa/pip/issues/7498.
+    """
+    from pip._internal.utils.entrypoints import _wrapper
+    return _wrapper(args)

myenv/Lib/site-packages/pip/_internal/build_env.py ADDED Viewed

	@@ -0,0 +1,311 @@

+"""Build Environment used for isolation during sdist building
+"""
+import logging
+import os
+import pathlib
+import site
+import sys
+import textwrap
+from collections import OrderedDict
+from types import TracebackType
+from typing import TYPE_CHECKING, Iterable, List, Optional, Set, Tuple, Type, Union
+from pip._vendor.certifi import where
+from pip._vendor.packaging.requirements import Requirement
+from pip._vendor.packaging.version import Version
+from pip import __file__ as pip_location
+from pip._internal.cli.spinners import open_spinner
+from pip._internal.locations import get_platlib, get_purelib, get_scheme
+from pip._internal.metadata import get_default_environment, get_environment
+from pip._internal.utils.subprocess import call_subprocess
+from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
+if TYPE_CHECKING:
+    from pip._internal.index.package_finder import PackageFinder
+logger = logging.getLogger(__name__)
+def _dedup(a: str, b: str) -> Union[Tuple[str], Tuple[str, str]]:
+    return (a, b) if a != b else (a,)
+class _Prefix:
+    def __init__(self, path: str) -> None:
+        self.path = path
+        self.setup = False
+        scheme = get_scheme("", prefix=path)
+        self.bin_dir = scheme.scripts
+        self.lib_dirs = _dedup(scheme.purelib, scheme.platlib)
+def get_runnable_pip() -> str:
+    """Get a file to pass to a Python executable, to run the currently-running pip.
+    This is used to run a pip subprocess, for installing requirements into the build
+    environment.
+    """
+    source = pathlib.Path(pip_location).resolve().parent
+    if not source.is_dir():
+        # This would happen if someone is using pip from inside a zip file. In that
+        # case, we can use that directly.
+        return str(source)
+    return os.fsdecode(source / "__pip-runner__.py")
+def _get_system_sitepackages() -> Set[str]:
+    """Get system site packages
+    Usually from site.getsitepackages,
+    but fallback on `get_purelib()/get_platlib()` if unavailable
+    (e.g. in a virtualenv created by virtualenv<20)
+    Returns normalized set of strings.
+    """
+    if hasattr(site, "getsitepackages"):
+        system_sites = site.getsitepackages()
+    else:
+        # virtualenv < 20 overwrites site.py without getsitepackages
+        # fallback on get_purelib/get_platlib.
+        # this is known to miss things, but shouldn't in the cases
+        # where getsitepackages() has been removed (inside a virtualenv)
+        system_sites = [get_purelib(), get_platlib()]
+    return {os.path.normcase(path) for path in system_sites}
+class BuildEnvironment:
+    """Creates and manages an isolated environment to install build deps"""
+    def __init__(self) -> None:
+        temp_dir = TempDirectory(kind=tempdir_kinds.BUILD_ENV, globally_managed=True)
+        self._prefixes = OrderedDict(
+            (name, _Prefix(os.path.join(temp_dir.path, name)))
+            for name in ("normal", "overlay")
+        )
+        self._bin_dirs: List[str] = []
+        self._lib_dirs: List[str] = []
+        for prefix in reversed(list(self._prefixes.values())):
+            self._bin_dirs.append(prefix.bin_dir)
+            self._lib_dirs.extend(prefix.lib_dirs)
+        # Customize site to:
+        # - ensure .pth files are honored
+        # - prevent access to system site packages
+        system_sites = _get_system_sitepackages()
+        self._site_dir = os.path.join(temp_dir.path, "site")
+        if not os.path.exists(self._site_dir):
+            os.mkdir(self._site_dir)
+        with open(
+            os.path.join(self._site_dir, "sitecustomize.py"), "w", encoding="utf-8"
+        ) as fp:
+            fp.write(
+                textwrap.dedent(
+                    """
+                import os, site, sys
+                # First, drop system-sites related paths.
+                original_sys_path = sys.path[:]
+                known_paths = set()
+                for path in {system_sites!r}:
+                    site.addsitedir(path, known_paths=known_paths)
+                system_paths = set(
+                    os.path.normcase(path)
+                    for path in sys.path[len(original_sys_path):]
+                )
+                original_sys_path = [
+                    path for path in original_sys_path
+                    if os.path.normcase(path) not in system_paths
+                ]
+                sys.path = original_sys_path
+                # Second, add lib directories.
+                # ensuring .pth file are processed.
+                for path in {lib_dirs!r}:
+                    assert not path in sys.path
+                    site.addsitedir(path)
+                """
+                ).format(system_sites=system_sites, lib_dirs=self._lib_dirs)
+            )
+    def __enter__(self) -> None:
+        self._save_env = {
+            name: os.environ.get(name, None)
+            for name in ("PATH", "PYTHONNOUSERSITE", "PYTHONPATH")
+        }
+        path = self._bin_dirs[:]
+        old_path = self._save_env["PATH"]
+        if old_path:
+            path.extend(old_path.split(os.pathsep))
+        pythonpath = [self._site_dir]
+        os.environ.update(
+            {
+                "PATH": os.pathsep.join(path),
+                "PYTHONNOUSERSITE": "1",
+                "PYTHONPATH": os.pathsep.join(pythonpath),
+            }
+        )
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        for varname, old_value in self._save_env.items():
+            if old_value is None:
+                os.environ.pop(varname, None)
+            else:
+                os.environ[varname] = old_value
+    def check_requirements(
+        self, reqs: Iterable[str]
+    ) -> Tuple[Set[Tuple[str, str]], Set[str]]:
+        """Return 2 sets:
+        - conflicting requirements: set of (installed, wanted) reqs tuples
+        - missing requirements: set of reqs
+        """
+        missing = set()
+        conflicting = set()
+        if reqs:
+            env = (
+                get_environment(self._lib_dirs)
+                if hasattr(self, "_lib_dirs")
+                else get_default_environment()
+            )
+            for req_str in reqs:
+                req = Requirement(req_str)
+                # We're explicitly evaluating with an empty extra value, since build
+                # environments are not provided any mechanism to select specific extras.
+                if req.marker is not None and not req.marker.evaluate({"extra": ""}):
+                    continue
+                dist = env.get_distribution(req.name)
+                if not dist:
+                    missing.add(req_str)
+                    continue
+                if isinstance(dist.version, Version):
+                    installed_req_str = f"{req.name}=={dist.version}"
+                else:
+                    installed_req_str = f"{req.name}==={dist.version}"
+                if not req.specifier.contains(dist.version, prereleases=True):
+                    conflicting.add((installed_req_str, req_str))
+                # FIXME: Consider direct URL?
+        return conflicting, missing
+    def install_requirements(
+        self,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix_as_string: str,
+        *,
+        kind: str,
+    ) -> None:
+        prefix = self._prefixes[prefix_as_string]
+        assert not prefix.setup
+        prefix.setup = True
+        if not requirements:
+            return
+        self._install_requirements(
+            get_runnable_pip(),
+            finder,
+            requirements,
+            prefix,
+            kind=kind,
+        )
+    @staticmethod
+    def _install_requirements(
+        pip_runnable: str,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix: _Prefix,
+        *,
+        kind: str,
+    ) -> None:
+        args: List[str] = [
+            sys.executable,
+            pip_runnable,
+            "install",
+            "--ignore-installed",
+            "--no-user",
+            "--prefix",
+            prefix.path,
+            "--no-warn-script-location",
+        ]
+        if logger.getEffectiveLevel() <= logging.DEBUG:
+            args.append("-v")
+        for format_control in ("no_binary", "only_binary"):
+            formats = getattr(finder.format_control, format_control)
+            args.extend(
+                (
+                    "--" + format_control.replace("_", "-"),
+                    ",".join(sorted(formats or {":none:"})),
+                )
+            )
+        index_urls = finder.index_urls
+        if index_urls:
+            args.extend(["-i", index_urls[0]])
+            for extra_index in index_urls[1:]:
+                args.extend(["--extra-index-url", extra_index])
+        else:
+            args.append("--no-index")
+        for link in finder.find_links:
+            args.extend(["--find-links", link])
+        for host in finder.trusted_hosts:
+            args.extend(["--trusted-host", host])
+        if finder.allow_all_prereleases:
+            args.append("--pre")
+        if finder.prefer_binary:
+            args.append("--prefer-binary")
+        args.append("--")
+        args.extend(requirements)
+        extra_environ = {"_PIP_STANDALONE_CERT": where()}
+        with open_spinner(f"Installing {kind}") as spinner:
+            call_subprocess(
+                args,
+                command_desc=f"pip subprocess to install {kind}",
+                spinner=spinner,
+                extra_environ=extra_environ,
+            )
+class NoOpBuildEnvironment(BuildEnvironment):
+    """A no-op drop-in replacement for BuildEnvironment"""
+    def __init__(self) -> None:
+        pass
+    def __enter__(self) -> None:
+        pass
+    def __exit__(
+        self,
+        exc_type: Optional[Type[BaseException]],
+        exc_val: Optional[BaseException],
+        exc_tb: Optional[TracebackType],
+    ) -> None:
+        pass
+    def cleanup(self) -> None:
+        pass
+    def install_requirements(
+        self,
+        finder: "PackageFinder",
+        requirements: Iterable[str],
+        prefix_as_string: str,
+        *,
+        kind: str,
+    ) -> None:
+        raise NotImplementedError()

myenv/Lib/site-packages/pip/_internal/cache.py ADDED Viewed

	@@ -0,0 +1,292 @@

+"""Cache Management
+"""
+import hashlib
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+from pip._vendor.packaging.tags import Tag, interpreter_name, interpreter_version
+from pip._vendor.packaging.utils import canonicalize_name
+from pip._internal.exceptions import InvalidWheelFilename
+from pip._internal.models.direct_url import DirectUrl
+from pip._internal.models.link import Link
+from pip._internal.models.wheel import Wheel
+from pip._internal.utils.temp_dir import TempDirectory, tempdir_kinds
+from pip._internal.utils.urls import path_to_url
+logger = logging.getLogger(__name__)
+ORIGIN_JSON_NAME = "origin.json"
+def _hash_dict(d: Dict[str, str]) -> str:
+    """Return a stable sha224 of a dictionary."""
+    s = json.dumps(d, sort_keys=True, separators=(",", ":"), ensure_ascii=True)
+    return hashlib.sha224(s.encode("ascii")).hexdigest()
+class Cache:
+    """An abstract class - provides cache directories for data from links
+    :param cache_dir: The root of the cache.
+    """
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__()
+        assert not cache_dir or os.path.isabs(cache_dir)
+        self.cache_dir = cache_dir or None
+    def _get_cache_path_parts(self, link: Link) -> List[str]:
+        """Get parts of part that must be os.path.joined with cache_dir"""
+        # We want to generate an url to use as our cache key, we don't want to
+        # just re-use the URL because it might have other items in the fragment
+        # and we don't care about those.
+        key_parts = {"url": link.url_without_fragment}
+        if link.hash_name is not None and link.hash is not None:
+            key_parts[link.hash_name] = link.hash
+        if link.subdirectory_fragment:
+            key_parts["subdirectory"] = link.subdirectory_fragment
+        # Include interpreter name, major and minor version in cache key
+        # to cope with ill-behaved sdists that build a different wheel
+        # depending on the python version their setup.py is being run on,
+        # and don't encode the difference in compatibility tags.
+        # https://github.com/pypa/pip/issues/7296
+        key_parts["interpreter_name"] = interpreter_name()
+        key_parts["interpreter_version"] = interpreter_version()
+        # Encode our key url with sha224, we'll use this because it has similar
+        # security properties to sha256, but with a shorter total output (and
+        # thus less secure). However the differences don't make a lot of
+        # difference for our use case here.
+        hashed = _hash_dict(key_parts)
+        # We want to nest the directories some to prevent having a ton of top
+        # level directories where we might run out of sub directories on some
+        # FS.
+        parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]]
+        return parts
+    def _get_candidates(self, link: Link, canonical_package_name: str) -> List[Any]:
+        can_not_cache = not self.cache_dir or not canonical_package_name or not link
+        if can_not_cache:
+            return []
+        candidates = []
+        path = self.get_path_for_link(link)
+        if os.path.isdir(path):
+            for candidate in os.listdir(path):
+                candidates.append((candidate, path))
+        return candidates
+    def get_path_for_link(self, link: Link) -> str:
+        """Return a directory to store cached items in for link."""
+        raise NotImplementedError()
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        """Returns a link to a cached item if it exists, otherwise returns the
+        passed link.
+        """
+        raise NotImplementedError()
+class SimpleWheelCache(Cache):
+    """A cache of wheels for future installs."""
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__(cache_dir)
+    def get_path_for_link(self, link: Link) -> str:
+        """Return a directory to store cached wheels for link
+        Because there are M wheels for any one sdist, we provide a directory
+        to cache them in, and then consult that directory when looking up
+        cache hits.
+        We only insert things into the cache if they have plausible version
+        numbers, so that we don't contaminate the cache with things that were
+        not unique. E.g. ./package might have dozens of installs done for it
+        and build a version of 0.0...and if we built and cached a wheel, we'd
+        end up using the same wheel even if the source has been edited.
+        :param link: The link of the sdist for which this will cache wheels.
+        """
+        parts = self._get_cache_path_parts(link)
+        assert self.cache_dir
+        # Store wheels within the root cache_dir
+        return os.path.join(self.cache_dir, "wheels", *parts)
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        candidates = []
+        if not package_name:
+            return link
+        canonical_package_name = canonicalize_name(package_name)
+        for wheel_name, wheel_dir in self._get_candidates(link, canonical_package_name):
+            try:
+                wheel = Wheel(wheel_name)
+            except InvalidWheelFilename:
+                continue
+            if canonicalize_name(wheel.name) != canonical_package_name:
+                logger.debug(
+                    "Ignoring cached wheel %s for %s as it "
+                    "does not match the expected distribution name %s.",
+                    wheel_name,
+                    link,
+                    package_name,
+                )
+                continue
+            if not wheel.supported(supported_tags):
+                # Built for a different python/arch/etc
+                continue
+            candidates.append(
+                (
+                    wheel.support_index_min(supported_tags),
+                    wheel_name,
+                    wheel_dir,
+                )
+            )
+        if not candidates:
+            return link
+        _, wheel_name, wheel_dir = min(candidates)
+        return Link(path_to_url(os.path.join(wheel_dir, wheel_name)))
+class EphemWheelCache(SimpleWheelCache):
+    """A SimpleWheelCache that creates it's own temporary cache directory"""
+    def __init__(self) -> None:
+        self._temp_dir = TempDirectory(
+            kind=tempdir_kinds.EPHEM_WHEEL_CACHE,
+            globally_managed=True,
+        )
+        super().__init__(self._temp_dir.path)
+class CacheEntry:
+    def __init__(
+        self,
+        link: Link,
+        persistent: bool,
+    ):
+        self.link = link
+        self.persistent = persistent
+        self.origin: Optional[DirectUrl] = None
+        origin_direct_url_path = Path(self.link.file_path).parent / ORIGIN_JSON_NAME
+        if origin_direct_url_path.exists():
+            try:
+                self.origin = DirectUrl.from_json(
+                    origin_direct_url_path.read_text(encoding="utf-8")
+                )
+            except Exception as e:
+                logger.warning(
+                    "Ignoring invalid cache entry origin file %s for %s (%s)",
+                    origin_direct_url_path,
+                    link.filename,
+                    e,
+                )
+class WheelCache(Cache):
+    """Wraps EphemWheelCache and SimpleWheelCache into a single Cache
+    This Cache allows for gracefully degradation, using the ephem wheel cache
+    when a certain link is not found in the simple wheel cache first.
+    """
+    def __init__(self, cache_dir: str) -> None:
+        super().__init__(cache_dir)
+        self._wheel_cache = SimpleWheelCache(cache_dir)
+        self._ephem_cache = EphemWheelCache()
+    def get_path_for_link(self, link: Link) -> str:
+        return self._wheel_cache.get_path_for_link(link)
+    def get_ephem_path_for_link(self, link: Link) -> str:
+        return self._ephem_cache.get_path_for_link(link)
+    def get(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Link:
+        cache_entry = self.get_cache_entry(link, package_name, supported_tags)
+        if cache_entry is None:
+            return link
+        return cache_entry.link
+    def get_cache_entry(
+        self,
+        link: Link,
+        package_name: Optional[str],
+        supported_tags: List[Tag],
+    ) -> Optional[CacheEntry]:
+        """Returns a CacheEntry with a link to a cached item if it exists or
+        None. The cache entry indicates if the item was found in the persistent
+        or ephemeral cache.
+        """
+        retval = self._wheel_cache.get(
+            link=link,
+            package_name=package_name,
+            supported_tags=supported_tags,
+        )
+        if retval is not link:
+            return CacheEntry(retval, persistent=True)
+        retval = self._ephem_cache.get(
+            link=link,
+            package_name=package_name,
+            supported_tags=supported_tags,
+        )
+        if retval is not link:
+            return CacheEntry(retval, persistent=False)
+        return None
+    @staticmethod
+    def record_download_origin(cache_dir: str, download_info: DirectUrl) -> None:
+        origin_path = Path(cache_dir) / ORIGIN_JSON_NAME
+        if origin_path.exists():
+            try:
+                origin = DirectUrl.from_json(origin_path.read_text(encoding="utf-8"))
+            except Exception as e:
+                logger.warning(
+                    "Could not read origin file %s in cache entry (%s). "
+                    "Will attempt to overwrite it.",
+                    origin_path,
+                    e,
+                )
+            else:
+                # TODO: use DirectUrl.equivalent when
+                # https://github.com/pypa/pip/pull/10564 is merged.
+                if origin.url != download_info.url:
+                    logger.warning(
+                        "Origin URL %s in cache entry %s does not match download URL "
+                        "%s. This is likely a pip bug or a cache corruption issue. "
+                        "Will overwrite it with the new value.",
+                        origin.url,
+                        cache_dir,
+                        download_info.url,
+                    )
+        origin_path.write_text(download_info.to_json(), encoding="utf-8")

myenv/Lib/site-packages/pip/_internal/cli/__init__.py ADDED Viewed

	@@ -0,0 +1,4 @@

+"""Subpackage containing all of pip's command line interface related code
+"""
+# This file intentionally does not import submodules

myenv/Lib/site-packages/pip/_internal/cli/autocompletion.py ADDED Viewed

	@@ -0,0 +1,171 @@

+"""Logic that powers autocompletion installed by ``pip completion``.
+"""
+import optparse
+import os
+import sys
+from itertools import chain
+from typing import Any, Iterable, List, Optional
+from pip._internal.cli.main_parser import create_main_parser
+from pip._internal.commands import commands_dict, create_command
+from pip._internal.metadata import get_default_environment
+def autocomplete() -> None:
+    """Entry Point for completion of main and subcommand options."""
+    # Don't complete if user hasn't sourced bash_completion file.
+    if "PIP_AUTO_COMPLETE" not in os.environ:
+        return
+    cwords = os.environ["COMP_WORDS"].split()[1:]
+    cword = int(os.environ["COMP_CWORD"])
+    try:
+        current = cwords[cword - 1]
+    except IndexError:
+        current = ""
+    parser = create_main_parser()
+    subcommands = list(commands_dict)
+    options = []
+    # subcommand
+    subcommand_name: Optional[str] = None
+    for word in cwords:
+        if word in subcommands:
+            subcommand_name = word
+            break
+    # subcommand options
+    if subcommand_name is not None:
+        # special case: 'help' subcommand has no options
+        if subcommand_name == "help":
+            sys.exit(1)
+        # special case: list locally installed dists for show and uninstall
+        should_list_installed = not current.startswith("-") and subcommand_name in [
+            "show",
+            "uninstall",
+        ]
+        if should_list_installed:
+            env = get_default_environment()
+            lc = current.lower()
+            installed = [
+                dist.canonical_name
+                for dist in env.iter_installed_distributions(local_only=True)
+                if dist.canonical_name.startswith(lc)
+                and dist.canonical_name not in cwords[1:]
+            ]
+            # if there are no dists installed, fall back to option completion
+            if installed:
+                for dist in installed:
+                    print(dist)
+                sys.exit(1)
+        should_list_installables = (
+            not current.startswith("-") and subcommand_name == "install"
+        )
+        if should_list_installables:
+            for path in auto_complete_paths(current, "path"):
+                print(path)
+            sys.exit(1)
+        subcommand = create_command(subcommand_name)
+        for opt in subcommand.parser.option_list_all:
+            if opt.help != optparse.SUPPRESS_HELP:
+                for opt_str in opt._long_opts + opt._short_opts:
+                    options.append((opt_str, opt.nargs))
+        # filter out previously specified options from available options
+        prev_opts = [x.split("=")[0] for x in cwords[1 : cword - 1]]
+        options = [(x, v) for (x, v) in options if x not in prev_opts]
+        # filter options by current input
+        options = [(k, v) for k, v in options if k.startswith(current)]
+        # get completion type given cwords and available subcommand options
+        completion_type = get_path_completion_type(
+            cwords,
+            cword,
+            subcommand.parser.option_list_all,
+        )
+        # get completion files and directories if ``completion_type`` is
+        # ``<file>``, ``<dir>`` or ``<path>``
+        if completion_type:
+            paths = auto_complete_paths(current, completion_type)
+            options = [(path, 0) for path in paths]
+        for option in options:
+            opt_label = option[0]
+            # append '=' to options which require args
+            if option[1] and option[0][:2] == "--":
+                opt_label += "="
+            print(opt_label)
+    else:
+        # show main parser options only when necessary
+        opts = [i.option_list for i in parser.option_groups]
+        opts.append(parser.option_list)
+        flattened_opts = chain.from_iterable(opts)
+        if current.startswith("-"):
+            for opt in flattened_opts:
+                if opt.help != optparse.SUPPRESS_HELP:
+                    subcommands += opt._long_opts + opt._short_opts
+        else:
+            # get completion type given cwords and all available options
+            completion_type = get_path_completion_type(cwords, cword, flattened_opts)
+            if completion_type:
+                subcommands = list(auto_complete_paths(current, completion_type))
+        print(" ".join([x for x in subcommands if x.startswith(current)]))
+    sys.exit(1)
+def get_path_completion_type(
+    cwords: List[str], cword: int, opts: Iterable[Any]
+) -> Optional[str]:
+    """Get the type of path completion (``file``, ``dir``, ``path`` or None)
+    :param cwords: same as the environmental variable ``COMP_WORDS``
+    :param cword: same as the environmental variable ``COMP_CWORD``
+    :param opts: The available options to check
+    :return: path completion type (``file``, ``dir``, ``path`` or None)
+    """
+    if cword < 2 or not cwords[cword - 2].startswith("-"):
+        return None
+    for opt in opts:
+        if opt.help == optparse.SUPPRESS_HELP:
+            continue
+        for o in str(opt).split("/"):
+            if cwords[cword - 2].split("=")[0] == o:
+                if not opt.metavar or any(
+                    x in ("path", "file", "dir") for x in opt.metavar.split("/")
+                ):
+                    return opt.metavar
+    return None
+def auto_complete_paths(current: str, completion_type: str) -> Iterable[str]:
+    """If ``completion_type`` is ``file`` or ``path``, list all regular files
+    and directories starting with ``current``; otherwise only list directories
+    starting with ``current``.
+    :param current: The word to be completed
+    :param completion_type: path completion type(``file``, ``path`` or ``dir``)
+    :return: A generator of regular files and/or directories
+    """
+    directory, filename = os.path.split(current)
+    current_path = os.path.abspath(directory)
+    # Don't complete paths if they can't be accessed
+    if not os.access(current_path, os.R_OK):
+        return
+    filename = os.path.normcase(filename)
+    # list all files that start with ``filename``
+    file_list = (
+        x for x in os.listdir(current_path) if os.path.normcase(x).startswith(filename)
+    )
+    for f in file_list:
+        opt = os.path.join(current_path, f)
+        comp_file = os.path.normcase(os.path.join(directory, f))
+        # complete regular files when there is not ``<dir>`` after option
+        # complete directories when there is ``<file>``, ``<path>`` or
+        # ``<dir>``after option
+        if completion_type != "dir" and os.path.isfile(opt):
+            yield comp_file
+        elif os.path.isdir(opt):
+            yield os.path.join(comp_file, "")

myenv/Lib/site-packages/pip/_internal/cli/base_command.py ADDED Viewed

	@@ -0,0 +1,236 @@

+"""Base Command class, and related routines"""
+import functools
+import logging
+import logging.config
+import optparse
+import os
+import sys
+import traceback
+from optparse import Values
+from typing import Any, Callable, List, Optional, Tuple
+from pip._vendor.rich import traceback as rich_traceback
+from pip._internal.cli import cmdoptions
+from pip._internal.cli.command_context import CommandContextMixIn
+from pip._internal.cli.parser import ConfigOptionParser, UpdatingDefaultsHelpFormatter
+from pip._internal.cli.status_codes import (
+    ERROR,
+    PREVIOUS_BUILD_DIR_ERROR,
+    UNKNOWN_ERROR,
+    VIRTUALENV_NOT_FOUND,
+)
+from pip._internal.exceptions import (
+    BadCommand,
+    CommandError,
+    DiagnosticPipError,
+    InstallationError,
+    NetworkConnectionError,
+    PreviousBuildDirError,
+    UninstallationError,
+)
+from pip._internal.utils.filesystem import check_path_owner
+from pip._internal.utils.logging import BrokenStdoutLoggingError, setup_logging
+from pip._internal.utils.misc import get_prog, normalize_path
+from pip._internal.utils.temp_dir import TempDirectoryTypeRegistry as TempDirRegistry
+from pip._internal.utils.temp_dir import global_tempdir_manager, tempdir_registry
+from pip._internal.utils.virtualenv import running_under_virtualenv
+__all__ = ["Command"]
+logger = logging.getLogger(__name__)
+class Command(CommandContextMixIn):
+    usage: str = ""
+    ignore_require_venv: bool = False
+    def __init__(self, name: str, summary: str, isolated: bool = False) -> None:
+        super().__init__()
+        self.name = name
+        self.summary = summary
+        self.parser = ConfigOptionParser(
+            usage=self.usage,
+            prog=f"{get_prog()} {name}",
+            formatter=UpdatingDefaultsHelpFormatter(),
+            add_help_option=False,
+            name=name,
+            description=self.__doc__,
+            isolated=isolated,
+        )
+        self.tempdir_registry: Optional[TempDirRegistry] = None
+        # Commands should add options to this option group
+        optgroup_name = f"{self.name.capitalize()} Options"
+        self.cmd_opts = optparse.OptionGroup(self.parser, optgroup_name)
+        # Add the general options
+        gen_opts = cmdoptions.make_option_group(
+            cmdoptions.general_group,
+            self.parser,
+        )
+        self.parser.add_option_group(gen_opts)
+        self.add_options()
+    def add_options(self) -> None:
+        pass
+    def handle_pip_version_check(self, options: Values) -> None:
+        """
+        This is a no-op so that commands by default do not do the pip version
+        check.
+        """
+        # Make sure we do the pip version check if the index_group options
+        # are present.
+        assert not hasattr(options, "no_index")
+    def run(self, options: Values, args: List[str]) -> int:
+        raise NotImplementedError
+    def parse_args(self, args: List[str]) -> Tuple[Values, List[str]]:
+        # factored out for testability
+        return self.parser.parse_args(args)
+    def main(self, args: List[str]) -> int:
+        try:
+            with self.main_context():
+                return self._main(args)
+        finally:
+            logging.shutdown()
+    def _main(self, args: List[str]) -> int:
+        # We must initialize this before the tempdir manager, otherwise the
+        # configuration would not be accessible by the time we clean up the
+        # tempdir manager.
+        self.tempdir_registry = self.enter_context(tempdir_registry())
+        # Intentionally set as early as possible so globally-managed temporary
+        # directories are available to the rest of the code.
+        self.enter_context(global_tempdir_manager())
+        options, args = self.parse_args(args)
+        # Set verbosity so that it can be used elsewhere.
+        self.verbosity = options.verbose - options.quiet
+        level_number = setup_logging(
+            verbosity=self.verbosity,
+            no_color=options.no_color,
+            user_log_file=options.log,
+        )
+        always_enabled_features = set(options.features_enabled) & set(
+            cmdoptions.ALWAYS_ENABLED_FEATURES
+        )
+        if always_enabled_features:
+            logger.warning(
+                "The following features are always enabled: %s. ",
+                ", ".join(sorted(always_enabled_features)),
+            )
+        # Make sure that the --python argument isn't specified after the
+        # subcommand. We can tell, because if --python was specified,
+        # we should only reach this point if we're running in the created
+        # subprocess, which has the _PIP_RUNNING_IN_SUBPROCESS environment
+        # variable set.
+        if options.python and "_PIP_RUNNING_IN_SUBPROCESS" not in os.environ:
+            logger.critical(
+                "The --python option must be placed before the pip subcommand name"
+            )
+            sys.exit(ERROR)
+        # TODO: Try to get these passing down from the command?
+        #       without resorting to os.environ to hold these.
+        #       This also affects isolated builds and it should.
+        if options.no_input:
+            os.environ["PIP_NO_INPUT"] = "1"
+        if options.exists_action:
+            os.environ["PIP_EXISTS_ACTION"] = " ".join(options.exists_action)
+        if options.require_venv and not self.ignore_require_venv:
+            # If a venv is required check if it can really be found
+            if not running_under_virtualenv():
+                logger.critical("Could not find an activated virtualenv (required).")
+                sys.exit(VIRTUALENV_NOT_FOUND)
+        if options.cache_dir:
+            options.cache_dir = normalize_path(options.cache_dir)
+            if not check_path_owner(options.cache_dir):
+                logger.warning(
+                    "The directory '%s' or its parent directory is not owned "
+                    "or is not writable by the current user. The cache "
+                    "has been disabled. Check the permissions and owner of "
+                    "that directory. If executing pip with sudo, you should "
+                    "use sudo's -H flag.",
+                    options.cache_dir,
+                )
+                options.cache_dir = None
+        def intercepts_unhandled_exc(
+            run_func: Callable[..., int]
+        ) -> Callable[..., int]:
+            @functools.wraps(run_func)
+            def exc_logging_wrapper(*args: Any) -> int:
+                try:
+                    status = run_func(*args)
+                    assert isinstance(status, int)
+                    return status
+                except DiagnosticPipError as exc:
+                    logger.error("[present-rich] %s", exc)
+                    logger.debug("Exception information:", exc_info=True)
+                    return ERROR
+                except PreviousBuildDirError as exc:
+                    logger.critical(str(exc))
+                    logger.debug("Exception information:", exc_info=True)
+                    return PREVIOUS_BUILD_DIR_ERROR
+                except (
+                    InstallationError,
+                    UninstallationError,
+                    BadCommand,
+                    NetworkConnectionError,
+                ) as exc:
+                    logger.critical(str(exc))
+                    logger.debug("Exception information:", exc_info=True)
+                    return ERROR
+                except CommandError as exc:
+                    logger.critical("%s", exc)
+                    logger.debug("Exception information:", exc_info=True)
+                    return ERROR
+                except BrokenStdoutLoggingError:
+                    # Bypass our logger and write any remaining messages to
+                    # stderr because stdout no longer works.
+                    print("ERROR: Pipe to stdout was broken", file=sys.stderr)
+                    if level_number <= logging.DEBUG:
+                        traceback.print_exc(file=sys.stderr)
+                    return ERROR
+                except KeyboardInterrupt:
+                    logger.critical("Operation cancelled by user")
+                    logger.debug("Exception information:", exc_info=True)
+                    return ERROR
+                except BaseException:
+                    logger.critical("Exception:", exc_info=True)
+                    return UNKNOWN_ERROR
+            return exc_logging_wrapper
+        try:
+            if not options.debug_mode:
+                run = intercepts_unhandled_exc(self.run)
+            else:
+                run = self.run
+                rich_traceback.install(show_locals=True)
+            return run(options, args)
+        finally:
+            self.handle_pip_version_check(options)

myenv/Lib/site-packages/pip/_internal/cli/cmdoptions.py ADDED Viewed

	@@ -0,0 +1,1074 @@

+"""
+shared options and groups
+The principle here is to define options once, but *not* instantiate them
+globally. One reason being that options with action='append' can carry state
+between parses. pip parses general options twice internally, and shouldn't
+pass on state. To be consistent, all options will follow this design.
+"""
+# The following comment should be removed at some point in the future.
+# mypy: strict-optional=False
+import importlib.util
+import logging
+import os
+import textwrap
+from functools import partial
+from optparse import SUPPRESS_HELP, Option, OptionGroup, OptionParser, Values
+from textwrap import dedent
+from typing import Any, Callable, Dict, Optional, Tuple
+from pip._vendor.packaging.utils import canonicalize_name
+from pip._internal.cli.parser import ConfigOptionParser
+from pip._internal.exceptions import CommandError
+from pip._internal.locations import USER_CACHE_DIR, get_src_prefix
+from pip._internal.models.format_control import FormatControl
+from pip._internal.models.index import PyPI
+from pip._internal.models.target_python import TargetPython
+from pip._internal.utils.hashes import STRONG_HASHES
+from pip._internal.utils.misc import strtobool
+logger = logging.getLogger(__name__)
+def raise_option_error(parser: OptionParser, option: Option, msg: str) -> None:
+    """
+    Raise an option parsing error using parser.error().
+    Args:
+      parser: an OptionParser instance.
+      option: an Option instance.
+      msg: the error text.
+    """
+    msg = f"{option} error: {msg}"
+    msg = textwrap.fill(" ".join(msg.split()))
+    parser.error(msg)
+def make_option_group(group: Dict[str, Any], parser: ConfigOptionParser) -> OptionGroup:
+    """
+    Return an OptionGroup object
+    group  -- assumed to be dict with 'name' and 'options' keys
+    parser -- an optparse Parser
+    """
+    option_group = OptionGroup(parser, group["name"])
+    for option in group["options"]:
+        option_group.add_option(option())
+    return option_group
+def check_dist_restriction(options: Values, check_target: bool = False) -> None:
+    """Function for determining if custom platform options are allowed.
+    :param options: The OptionParser options.
+    :param check_target: Whether or not to check if --target is being used.
+    """
+    dist_restriction_set = any(
+        [
+            options.python_version,
+            options.platforms,
+            options.abis,
+            options.implementation,
+        ]
+    )
+    binary_only = FormatControl(set(), {":all:"})
+    sdist_dependencies_allowed = (
+        options.format_control != binary_only and not options.ignore_dependencies
+    )
+    # Installations or downloads using dist restrictions must not combine
+    # source distributions and dist-specific wheels, as they are not
+    # guaranteed to be locally compatible.
+    if dist_restriction_set and sdist_dependencies_allowed:
+        raise CommandError(
+            "When restricting platform and interpreter constraints using "
+            "--python-version, --platform, --abi, or --implementation, "
+            "either --no-deps must be set, or --only-binary=:all: must be "
+            "set and --no-binary must not be set (or must be set to "
+            ":none:)."
+        )
+    if check_target:
+        if dist_restriction_set and not options.target_dir:
+            raise CommandError(
+                "Can not use any platform or abi specific options unless "
+                "installing via '--target'"
+            )
+def _path_option_check(option: Option, opt: str, value: str) -> str:
+    return os.path.expanduser(value)
+def _package_name_option_check(option: Option, opt: str, value: str) -> str:
+    return canonicalize_name(value)
+class PipOption(Option):
+    TYPES = Option.TYPES + ("path", "package_name")
+    TYPE_CHECKER = Option.TYPE_CHECKER.copy()
+    TYPE_CHECKER["package_name"] = _package_name_option_check
+    TYPE_CHECKER["path"] = _path_option_check
+###########
+# options #
+###########
+help_: Callable[..., Option] = partial(
+    Option,
+    "-h",
+    "--help",
+    dest="help",
+    action="help",
+    help="Show help.",
+)
+debug_mode: Callable[..., Option] = partial(
+    Option,
+    "--debug",
+    dest="debug_mode",
+    action="store_true",
+    default=False,
+    help=(
+        "Let unhandled exceptions propagate outside the main subroutine, "
+        "instead of logging them to stderr."
+    ),
+)
+isolated_mode: Callable[..., Option] = partial(
+    Option,
+    "--isolated",
+    dest="isolated_mode",
+    action="store_true",
+    default=False,
+    help=(
+        "Run pip in an isolated mode, ignoring environment variables and user "
+        "configuration."
+    ),
+)
+require_virtualenv: Callable[..., Option] = partial(
+    Option,
+    "--require-virtualenv",
+    "--require-venv",
+    dest="require_venv",
+    action="store_true",
+    default=False,
+    help=(
+        "Allow pip to only run in a virtual environment; "
+        "exit with an error otherwise."
+    ),
+)
+override_externally_managed: Callable[..., Option] = partial(
+    Option,
+    "--break-system-packages",
+    dest="override_externally_managed",
+    action="store_true",
+    help="Allow pip to modify an EXTERNALLY-MANAGED Python installation",
+)
+python: Callable[..., Option] = partial(
+    Option,
+    "--python",
+    dest="python",
+    help="Run pip with the specified Python interpreter.",
+)
+verbose: Callable[..., Option] = partial(
+    Option,
+    "-v",
+    "--verbose",
+    dest="verbose",
+    action="count",
+    default=0,
+    help="Give more output. Option is additive, and can be used up to 3 times.",
+)
+no_color: Callable[..., Option] = partial(
+    Option,
+    "--no-color",
+    dest="no_color",
+    action="store_true",
+    default=False,
+    help="Suppress colored output.",
+)
+version: Callable[..., Option] = partial(
+    Option,
+    "-V",
+    "--version",
+    dest="version",
+    action="store_true",
+    help="Show version and exit.",
+)
+quiet: Callable[..., Option] = partial(
+    Option,
+    "-q",
+    "--quiet",
+    dest="quiet",
+    action="count",
+    default=0,
+    help=(
+        "Give less output. Option is additive, and can be used up to 3"
+        " times (corresponding to WARNING, ERROR, and CRITICAL logging"
+        " levels)."
+    ),
+)
+progress_bar: Callable[..., Option] = partial(
+    Option,
+    "--progress-bar",
+    dest="progress_bar",
+    type="choice",
+    choices=["on", "off"],
+    default="on",
+    help="Specify whether the progress bar should be used [on, off] (default: on)",
+)
+log: Callable[..., Option] = partial(
+    PipOption,
+    "--log",
+    "--log-file",
+    "--local-log",
+    dest="log",
+    metavar="path",
+    type="path",
+    help="Path to a verbose appending log.",
+)
+no_input: Callable[..., Option] = partial(
+    Option,
+    # Don't ask for input
+    "--no-input",
+    dest="no_input",
+    action="store_true",
+    default=False,
+    help="Disable prompting for input.",
+)
+keyring_provider: Callable[..., Option] = partial(
+    Option,
+    "--keyring-provider",
+    dest="keyring_provider",
+    choices=["auto", "disabled", "import", "subprocess"],
+    default="auto",
+    help=(
+        "Enable the credential lookup via the keyring library if user input is allowed."
+        " Specify which mechanism to use [disabled, import, subprocess]."
+        " (default: disabled)"
+    ),
+)
+proxy: Callable[..., Option] = partial(
+    Option,
+    "--proxy",
+    dest="proxy",
+    type="str",
+    default="",
+    help="Specify a proxy in the form scheme://[user:passwd@]proxy.server:port.",
+)
+retries: Callable[..., Option] = partial(
+    Option,
+    "--retries",
+    dest="retries",
+    type="int",
+    default=5,
+    help="Maximum number of retries each connection should attempt "
+    "(default %default times).",
+)
+timeout: Callable[..., Option] = partial(
+    Option,
+    "--timeout",
+    "--default-timeout",
+    metavar="sec",
+    dest="timeout",
+    type="float",
+    default=15,
+    help="Set the socket timeout (default %default seconds).",
+)
+def exists_action() -> Option:
+    return Option(
+        # Option when path already exist
+        "--exists-action",
+        dest="exists_action",
+        type="choice",
+        choices=["s", "i", "w", "b", "a"],
+        default=[],
+        action="append",
+        metavar="action",
+        help="Default action when a path already exists: "
+        "(s)witch, (i)gnore, (w)ipe, (b)ackup, (a)bort.",
+    )
+cert: Callable[..., Option] = partial(
+    PipOption,
+    "--cert",
+    dest="cert",
+    type="path",
+    metavar="path",
+    help=(
+        "Path to PEM-encoded CA certificate bundle. "
+        "If provided, overrides the default. "
+        "See 'SSL Certificate Verification' in pip documentation "
+        "for more information."
+    ),
+)
+client_cert: Callable[..., Option] = partial(
+    PipOption,
+    "--client-cert",
+    dest="client_cert",
+    type="path",
+    default=None,
+    metavar="path",
+    help="Path to SSL client certificate, a single file containing the "
+    "private key and the certificate in PEM format.",
+)
+index_url: Callable[..., Option] = partial(
+    Option,
+    "-i",
+    "--index-url",
+    "--pypi-url",
+    dest="index_url",
+    metavar="URL",
+    default=PyPI.simple_url,
+    help="Base URL of the Python Package Index (default %default). "
+    "This should point to a repository compliant with PEP 503 "
+    "(the simple repository API) or a local directory laid out "
+    "in the same format.",
+)
+def extra_index_url() -> Option:
+    return Option(
+        "--extra-index-url",
+        dest="extra_index_urls",
+        metavar="URL",
+        action="append",
+        default=[],
+        help="Extra URLs of package indexes to use in addition to "
+        "--index-url. Should follow the same rules as "
+        "--index-url.",
+    )
+no_index: Callable[..., Option] = partial(
+    Option,
+    "--no-index",
+    dest="no_index",
+    action="store_true",
+    default=False,
+    help="Ignore package index (only looking at --find-links URLs instead).",
+)
+def find_links() -> Option:
+    return Option(
+        "-f",
+        "--find-links",
+        dest="find_links",
+        action="append",
+        default=[],
+        metavar="url",
+        help="If a URL or path to an html file, then parse for links to "
+        "archives such as sdist (.tar.gz) or wheel (.whl) files. "
+        "If a local path or file:// URL that's a directory, "
+        "then look for archives in the directory listing. "
+        "Links to VCS project URLs are not supported.",
+    )
+def trusted_host() -> Option:
+    return Option(
+        "--trusted-host",
+        dest="trusted_hosts",
+        action="append",
+        metavar="HOSTNAME",
+        default=[],
+        help="Mark this host or host:port pair as trusted, even though it "
+        "does not have valid or any HTTPS.",
+    )
+def constraints() -> Option:
+    return Option(
+        "-c",
+        "--constraint",
+        dest="constraints",
+        action="append",
+        default=[],
+        metavar="file",
+        help="Constrain versions using the given constraints file. "
+        "This option can be used multiple times.",
+    )
+def requirements() -> Option:
+    return Option(
+        "-r",
+        "--requirement",
+        dest="requirements",
+        action="append",
+        default=[],
+        metavar="file",
+        help="Install from the given requirements file. "
+        "This option can be used multiple times.",
+    )
+def editable() -> Option:
+    return Option(
+        "-e",
+        "--editable",
+        dest="editables",
+        action="append",
+        default=[],
+        metavar="path/url",
+        help=(
+            "Install a project in editable mode (i.e. setuptools "
+            '"develop mode") from a local project path or a VCS url.'
+        ),
+    )
+def _handle_src(option: Option, opt_str: str, value: str, parser: OptionParser) -> None:
+    value = os.path.abspath(value)
+    setattr(parser.values, option.dest, value)
+src: Callable[..., Option] = partial(
+    PipOption,
+    "--src",
+    "--source",
+    "--source-dir",
+    "--source-directory",
+    dest="src_dir",
+    type="path",
+    metavar="dir",
+    default=get_src_prefix(),
+    action="callback",
+    callback=_handle_src,
+    help="Directory to check out editable projects into. "
+    'The default in a virtualenv is "<venv path>/src". '
+    'The default for global installs is "<current dir>/src".',
+)
+def _get_format_control(values: Values, option: Option) -> Any:
+    """Get a format_control object."""
+    return getattr(values, option.dest)
+def _handle_no_binary(
+    option: Option, opt_str: str, value: str, parser: OptionParser
+) -> None:
+    existing = _get_format_control(parser.values, option)
+    FormatControl.handle_mutual_excludes(
+        value,
+        existing.no_binary,
+        existing.only_binary,
+    )
+def _handle_only_binary(
+    option: Option, opt_str: str, value: str, parser: OptionParser
+) -> None:
+    existing = _get_format_control(parser.values, option)
+    FormatControl.handle_mutual_excludes(
+        value,
+        existing.only_binary,
+        existing.no_binary,
+    )
+def no_binary() -> Option:
+    format_control = FormatControl(set(), set())
+    return Option(
+        "--no-binary",
+        dest="format_control",
+        action="callback",
+        callback=_handle_no_binary,
+        type="str",
+        default=format_control,
+        help="Do not use binary packages. Can be supplied multiple times, and "
+        'each time adds to the existing value. Accepts either ":all:" to '
+        'disable all binary packages, ":none:" to empty the set (notice '
+        "the colons), or one or more package names with commas between "
+        "them (no colons). Note that some packages are tricky to compile "
+        "and may fail to install when this option is used on them.",
+    )
+def only_binary() -> Option:
+    format_control = FormatControl(set(), set())
+    return Option(
+        "--only-binary",
+        dest="format_control",
+        action="callback",
+        callback=_handle_only_binary,
+        type="str",
+        default=format_control,
+        help="Do not use source packages. Can be supplied multiple times, and "
+        'each time adds to the existing value. Accepts either ":all:" to '
+        'disable all source packages, ":none:" to empty the set, or one '
+        "or more package names with commas between them. Packages "
+        "without binary distributions will fail to install when this "
+        "option is used on them.",
+    )
+platforms: Callable[..., Option] = partial(
+    Option,
+    "--platform",
+    dest="platforms",
+    metavar="platform",
+    action="append",
+    default=None,
+    help=(
+        "Only use wheels compatible with <platform>. Defaults to the "
+        "platform of the running system. Use this option multiple times to "
+        "specify multiple platforms supported by the target interpreter."
+    ),
+)
+# This was made a separate function for unit-testing purposes.
+def _convert_python_version(value: str) -> Tuple[Tuple[int, ...], Optional[str]]:
+    """
+    Convert a version string like "3", "37", or "3.7.3" into a tuple of ints.
+    :return: A 2-tuple (version_info, error_msg), where `error_msg` is
+        non-None if and only if there was a parsing error.
+    """
+    if not value:
+        # The empty string is the same as not providing a value.
+        return (None, None)
+    parts = value.split(".")
+    if len(parts) > 3:
+        return ((), "at most three version parts are allowed")
+    if len(parts) == 1:
+        # Then we are in the case of "3" or "37".
+        value = parts[0]
+        if len(value) > 1:
+            parts = [value[0], value[1:]]
+    try:
+        version_info = tuple(int(part) for part in parts)
+    except ValueError:
+        return ((), "each version part must be an integer")
+    return (version_info, None)
+def _handle_python_version(
+    option: Option, opt_str: str, value: str, parser: OptionParser
+) -> None:
+    """
+    Handle a provided --python-version value.
+    """
+    version_info, error_msg = _convert_python_version(value)
+    if error_msg is not None:
+        msg = "invalid --python-version value: {!r}: {}".format(
+            value,
+            error_msg,
+        )
+        raise_option_error(parser, option=option, msg=msg)
+    parser.values.python_version = version_info
+python_version: Callable[..., Option] = partial(
+    Option,
+    "--python-version",
+    dest="python_version",
+    metavar="python_version",
+    action="callback",
+    callback=_handle_python_version,
+    type="str",
+    default=None,
+    help=dedent(
+        """\
+    The Python interpreter version to use for wheel and "Requires-Python"
+    compatibility checks. Defaults to a version derived from the running
+    interpreter. The version can be specified using up to three dot-separated
+    integers (e.g. "3" for 3.0.0, "3.7" for 3.7.0, or "3.7.3"). A major-minor
+    version can also be given as a string without dots (e.g. "37" for 3.7.0).
+    """
+    ),
+)
+implementation: Callable[..., Option] = partial(
+    Option,
+    "--implementation",
+    dest="implementation",
+    metavar="implementation",
+    default=None,
+    help=(
+        "Only use wheels compatible with Python "
+        "implementation <implementation>, e.g. 'pp', 'jy', 'cp', "
+        " or 'ip'. If not specified, then the current "
+        "interpreter implementation is used.  Use 'py' to force "
+        "implementation-agnostic wheels."
+    ),
+)
+abis: Callable[..., Option] = partial(
+    Option,
+    "--abi",
+    dest="abis",
+    metavar="abi",
+    action="append",
+    default=None,
+    help=(
+        "Only use wheels compatible with Python abi <abi>, e.g. 'pypy_41'. "
+        "If not specified, then the current interpreter abi tag is used. "
+        "Use this option multiple times to specify multiple abis supported "
+        "by the target interpreter. Generally you will need to specify "
+        "--implementation, --platform, and --python-version when using this "
+        "option."
+    ),
+)
+def add_target_python_options(cmd_opts: OptionGroup) -> None:
+    cmd_opts.add_option(platforms())
+    cmd_opts.add_option(python_version())
+    cmd_opts.add_option(implementation())
+    cmd_opts.add_option(abis())
+def make_target_python(options: Values) -> TargetPython:
+    target_python = TargetPython(
+        platforms=options.platforms,
+        py_version_info=options.python_version,
+        abis=options.abis,
+        implementation=options.implementation,
+    )
+    return target_python
+def prefer_binary() -> Option:
+    return Option(
+        "--prefer-binary",
+        dest="prefer_binary",
+        action="store_true",
+        default=False,
+        help="Prefer older binary packages over newer source packages.",
+    )
+cache_dir: Callable[..., Option] = partial(
+    PipOption,
+    "--cache-dir",
+    dest="cache_dir",
+    default=USER_CACHE_DIR,
+    metavar="dir",
+    type="path",
+    help="Store the cache data in <dir>.",
+)
+def _handle_no_cache_dir(
+    option: Option, opt: str, value: str, parser: OptionParser
+) -> None:
+    """
+    Process a value provided for the --no-cache-dir option.
+    This is an optparse.Option callback for the --no-cache-dir option.
+    """
+    # The value argument will be None if --no-cache-dir is passed via the
+    # command-line, since the option doesn't accept arguments.  However,
+    # the value can be non-None if the option is triggered e.g. by an
+    # environment variable, like PIP_NO_CACHE_DIR=true.
+    if value is not None:
+        # Then parse the string value to get argument error-checking.
+        try:
+            strtobool(value)
+        except ValueError as exc:
+            raise_option_error(parser, option=option, msg=str(exc))
+    # Originally, setting PIP_NO_CACHE_DIR to a value that strtobool()
+    # converted to 0 (like "false" or "no") caused cache_dir to be disabled
+    # rather than enabled (logic would say the latter).  Thus, we disable
+    # the cache directory not just on values that parse to True, but (for
+    # backwards compatibility reasons) also on values that parse to False.
+    # In other words, always set it to False if the option is provided in
+    # some (valid) form.
+    parser.values.cache_dir = False
+no_cache: Callable[..., Option] = partial(
+    Option,
+    "--no-cache-dir",
+    dest="cache_dir",
+    action="callback",
+    callback=_handle_no_cache_dir,
+    help="Disable the cache.",
+)
+no_deps: Callable[..., Option] = partial(
+    Option,
+    "--no-deps",
+    "--no-dependencies",
+    dest="ignore_dependencies",
+    action="store_true",
+    default=False,
+    help="Don't install package dependencies.",
+)
+ignore_requires_python: Callable[..., Option] = partial(
+    Option,
+    "--ignore-requires-python",
+    dest="ignore_requires_python",
+    action="store_true",
+    help="Ignore the Requires-Python information.",
+)
+no_build_isolation: Callable[..., Option] = partial(
+    Option,
+    "--no-build-isolation",
+    dest="build_isolation",
+    action="store_false",
+    default=True,
+    help="Disable isolation when building a modern source distribution. "
+    "Build dependencies specified by PEP 518 must be already installed "
+    "if this option is used.",
+)
+check_build_deps: Callable[..., Option] = partial(
+    Option,
+    "--check-build-dependencies",
+    dest="check_build_deps",
+    action="store_true",
+    default=False,
+    help="Check the build dependencies when PEP517 is used.",
+)
+def _handle_no_use_pep517(
+    option: Option, opt: str, value: str, parser: OptionParser
+) -> None:
+    """
+    Process a value provided for the --no-use-pep517 option.
+    This is an optparse.Option callback for the no_use_pep517 option.
+    """
+    # Since --no-use-pep517 doesn't accept arguments, the value argument
+    # will be None if --no-use-pep517 is passed via the command-line.
+    # However, the value can be non-None if the option is triggered e.g.
+    # by an environment variable, for example "PIP_NO_USE_PEP517=true".
+    if value is not None:
+        msg = """A value was passed for --no-use-pep517,
+        probably using either the PIP_NO_USE_PEP517 environment variable
+        or the "no-use-pep517" config file option. Use an appropriate value
+        of the PIP_USE_PEP517 environment variable or the "use-pep517"
+        config file option instead.
+        """
+        raise_option_error(parser, option=option, msg=msg)
+    # If user doesn't wish to use pep517, we check if setuptools and wheel are installed
+    # and raise error if it is not.
+    packages = ("setuptools", "wheel")
+    if not all(importlib.util.find_spec(package) for package in packages):
+        msg = (
+            f"It is not possible to use --no-use-pep517 "
+            f"without {' and '.join(packages)} installed."
+        )
+        raise_option_error(parser, option=option, msg=msg)
+    # Otherwise, --no-use-pep517 was passed via the command-line.
+    parser.values.use_pep517 = False
+use_pep517: Any = partial(
+    Option,
+    "--use-pep517",
+    dest="use_pep517",
+    action="store_true",
+    default=None,
+    help="Use PEP 517 for building source distributions "
+    "(use --no-use-pep517 to force legacy behaviour).",
+)
+no_use_pep517: Any = partial(
+    Option,
+    "--no-use-pep517",
+    dest="use_pep517",
+    action="callback",
+    callback=_handle_no_use_pep517,
+    default=None,
+    help=SUPPRESS_HELP,
+)
+def _handle_config_settings(
+    option: Option, opt_str: str, value: str, parser: OptionParser
+) -> None:
+    key, sep, val = value.partition("=")
+    if sep != "=":
+        parser.error(f"Arguments to {opt_str} must be of the form KEY=VAL")  # noqa
+    dest = getattr(parser.values, option.dest)
+    if dest is None:
+        dest = {}
+        setattr(parser.values, option.dest, dest)
+    if key in dest:
+        if isinstance(dest[key], list):
+            dest[key].append(val)
+        else:
+            dest[key] = [dest[key], val]
+    else:
+        dest[key] = val
+config_settings: Callable[..., Option] = partial(
+    Option,
+    "-C",
+    "--config-settings",
+    dest="config_settings",
+    type=str,
+    action="callback",
+    callback=_handle_config_settings,
+    metavar="settings",
+    help="Configuration settings to be passed to the PEP 517 build backend. "
+    "Settings take the form KEY=VALUE. Use multiple --config-settings options "
+    "to pass multiple keys to the backend.",
+)
+build_options: Callable[..., Option] = partial(
+    Option,
+    "--build-option",
+    dest="build_options",
+    metavar="options",
+    action="append",
+    help="Extra arguments to be supplied to 'setup.py bdist_wheel'.",
+)
+global_options: Callable[..., Option] = partial(
+    Option,
+    "--global-option",
+    dest="global_options",
+    action="append",
+    metavar="options",
+    help="Extra global options to be supplied to the setup.py "
+    "call before the install or bdist_wheel command.",
+)
+no_clean: Callable[..., Option] = partial(
+    Option,
+    "--no-clean",
+    action="store_true",
+    default=False,
+    help="Don't clean up build directories.",
+)
+pre: Callable[..., Option] = partial(
+    Option,
+    "--pre",
+    action="store_true",
+    default=False,
+    help="Include pre-release and development versions. By default, "
+    "pip only finds stable versions.",
+)
+disable_pip_version_check: Callable[..., Option] = partial(
+    Option,
+    "--disable-pip-version-check",
+    dest="disable_pip_version_check",
+    action="store_true",
+    default=False,
+    help="Don't periodically check PyPI to determine whether a new version "
+    "of pip is available for download. Implied with --no-index.",
+)
+root_user_action: Callable[..., Option] = partial(
+    Option,
+    "--root-user-action",
+    dest="root_user_action",
+    default="warn",
+    choices=["warn", "ignore"],
+    help="Action if pip is run as a root user. By default, a warning message is shown.",
+)
+def _handle_merge_hash(
+    option: Option, opt_str: str, value: str, parser: OptionParser
+) -> None:
+    """Given a value spelled "algo:digest", append the digest to a list
+    pointed to in a dict by the algo name."""
+    if not parser.values.hashes:
+        parser.values.hashes = {}
+    try:
+        algo, digest = value.split(":", 1)
+    except ValueError:
+        parser.error(
+            "Arguments to {} must be a hash name "  # noqa
+            "followed by a value, like --hash=sha256:"
+            "abcde...".format(opt_str)
+        )
+    if algo not in STRONG_HASHES:
+        parser.error(
+            "Allowed hash algorithms for {} are {}.".format(  # noqa
+                opt_str, ", ".join(STRONG_HASHES)
+            )
+        )
+    parser.values.hashes.setdefault(algo, []).append(digest)
+hash: Callable[..., Option] = partial(
+    Option,
+    "--hash",
+    # Hash values eventually end up in InstallRequirement.hashes due to
+    # __dict__ copying in process_line().
+    dest="hashes",
+    action="callback",
+    callback=_handle_merge_hash,
+    type="string",
+    help="Verify that the package's archive matches this "
+    "hash before installing. Example: --hash=sha256:abcdef...",
+)
+require_hashes: Callable[..., Option] = partial(
+    Option,
+    "--require-hashes",
+    dest="require_hashes",
+    action="store_true",
+    default=False,
+    help="Require a hash to check each requirement against, for "
+    "repeatable installs. This option is implied when any package in a "
+    "requirements file has a --hash option.",
+)
+list_path: Callable[..., Option] = partial(
+    PipOption,
+    "--path",
+    dest="path",
+    type="path",
+    action="append",
+    help="Restrict to the specified installation path for listing "
+    "packages (can be used multiple times).",
+)
+def check_list_path_option(options: Values) -> None:
+    if options.path and (options.user or options.local):
+        raise CommandError("Cannot combine '--path' with '--user' or '--local'")
+list_exclude: Callable[..., Option] = partial(
+    PipOption,
+    "--exclude",
+    dest="excludes",
+    action="append",
+    metavar="package",
+    type="package_name",
+    help="Exclude specified package from the output",
+)
+no_python_version_warning: Callable[..., Option] = partial(
+    Option,
+    "--no-python-version-warning",
+    dest="no_python_version_warning",
+    action="store_true",
+    default=False,
+    help="Silence deprecation warnings for upcoming unsupported Pythons.",
+)
+# Features that are now always on. A warning is printed if they are used.
+ALWAYS_ENABLED_FEATURES = [
+    "no-binary-enable-wheel-cache",  # always on since 23.1
+]
+use_new_feature: Callable[..., Option] = partial(
+    Option,
+    "--use-feature",
+    dest="features_enabled",
+    metavar="feature",
+    action="append",
+    default=[],
+    choices=[
+        "fast-deps",
+        "truststore",
+    ]
+    + ALWAYS_ENABLED_FEATURES,
+    help="Enable new functionality, that may be backward incompatible.",
+)
+use_deprecated_feature: Callable[..., Option] = partial(
+    Option,
+    "--use-deprecated",
+    dest="deprecated_features_enabled",
+    metavar="feature",
+    action="append",
+    default=[],
+    choices=[
+        "legacy-resolver",
+    ],
+    help=("Enable deprecated functionality, that will be removed in the future."),
+)
+##########
+# groups #
+##########
+general_group: Dict[str, Any] = {
+    "name": "General Options",
+    "options": [
+        help_,
+        debug_mode,
+        isolated_mode,
+        require_virtualenv,
+        python,
+        verbose,
+        version,
+        quiet,
+        log,
+        no_input,
+        keyring_provider,
+        proxy,
+        retries,
+        timeout,
+        exists_action,
+        trusted_host,
+        cert,
+        client_cert,
+        cache_dir,
+        no_cache,
+        disable_pip_version_check,
+        no_color,
+        no_python_version_warning,
+        use_new_feature,
+        use_deprecated_feature,
+    ],
+}
+index_group: Dict[str, Any] = {
+    "name": "Package Index Options",
+    "options": [
+        index_url,
+        extra_index_url,
+        no_index,
+        find_links,
+    ],
+}

myenv/Lib/site-packages/pip/_internal/cli/command_context.py ADDED Viewed

	@@ -0,0 +1,27 @@

+from contextlib import ExitStack, contextmanager
+from typing import ContextManager, Generator, TypeVar
+_T = TypeVar("_T", covariant=True)
+class CommandContextMixIn:
+    def __init__(self) -> None:
+        super().__init__()
+        self._in_main_context = False
+        self._main_context = ExitStack()
+    @contextmanager
+    def main_context(self) -> Generator[None, None, None]:
+        assert not self._in_main_context
+        self._in_main_context = True
+        try:
+            with self._main_context:
+                yield
+        finally:
+            self._in_main_context = False
+    def enter_context(self, context_provider: ContextManager[_T]) -> _T:
+        assert self._in_main_context
+        return self._main_context.enter_context(context_provider)

myenv/Lib/site-packages/pip/_internal/cli/main.py ADDED Viewed

	@@ -0,0 +1,79 @@

+"""Primary application entrypoint.
+"""
+import locale
+import logging
+import os
+import sys
+import warnings
+from typing import List, Optional
+from pip._internal.cli.autocompletion import autocomplete
+from pip._internal.cli.main_parser import parse_command
+from pip._internal.commands import create_command
+from pip._internal.exceptions import PipError
+from pip._internal.utils import deprecation
+logger = logging.getLogger(__name__)
+# Do not import and use main() directly! Using it directly is actively
+# discouraged by pip's maintainers. The name, location and behavior of
+# this function is subject to change, so calling it directly is not
+# portable across different pip versions.
+# In addition, running pip in-process is unsupported and unsafe. This is
+# elaborated in detail at
+# https://pip.pypa.io/en/stable/user_guide/#using-pip-from-your-program.
+# That document also provides suggestions that should work for nearly
+# all users that are considering importing and using main() directly.
+# However, we know that certain users will still want to invoke pip
+# in-process. If you understand and accept the implications of using pip
+# in an unsupported manner, the best approach is to use runpy to avoid
+# depending on the exact location of this entry point.
+# The following example shows how to use runpy to invoke pip in that
+# case:
+#
+#     sys.argv = ["pip", your, args, here]
+#     runpy.run_module("pip", run_name="__main__")
+#
+# Note that this will exit the process after running, unlike a direct
+# call to main. As it is not safe to do any processing after calling
+# main, this should not be an issue in practice.
+def main(args: Optional[List[str]] = None) -> int:
+    if args is None:
+        args = sys.argv[1:]
+    # Suppress the pkg_resources deprecation warning
+    # Note - we use a module of .*pkg_resources to cover
+    # the normal case (pip._vendor.pkg_resources) and the
+    # devendored case (a bare pkg_resources)
+    warnings.filterwarnings(
+        action="ignore", category=DeprecationWarning, module=".*pkg_resources"
+    )
+    # Configure our deprecation warnings to be sent through loggers
+    deprecation.install_warning_logger()
+    autocomplete()
+    try:
+        cmd_name, cmd_args = parse_command(args)
+    except PipError as exc:
+        sys.stderr.write(f"ERROR: {exc}")
+        sys.stderr.write(os.linesep)
+        sys.exit(1)
+    # Needed for locale.getpreferredencoding(False) to work
+    # in pip._internal.utils.encoding.auto_decode
+    try:
+        locale.setlocale(locale.LC_ALL, "")
+    except locale.Error as e:
+        # setlocale can apparently crash if locale are uninitialized
+        logger.debug("Ignoring error %s when setting locale", e)
+    command = create_command(cmd_name, isolated=("--isolated" in cmd_args))
+    return command.main(cmd_args)

myenv/Lib/site-packages/pip/_internal/cli/main_parser.py ADDED Viewed

	@@ -0,0 +1,134 @@

+"""A single place for constructing and exposing the main parser
+"""
+import os
+import subprocess
+import sys
+from typing import List, Optional, Tuple
+from pip._internal.build_env import get_runnable_pip
+from pip._internal.cli import cmdoptions
+from pip._internal.cli.parser import ConfigOptionParser, UpdatingDefaultsHelpFormatter
+from pip._internal.commands import commands_dict, get_similar_commands
+from pip._internal.exceptions import CommandError
+from pip._internal.utils.misc import get_pip_version, get_prog
+__all__ = ["create_main_parser", "parse_command"]
+def create_main_parser() -> ConfigOptionParser:
+    """Creates and returns the main parser for pip's CLI"""
+    parser = ConfigOptionParser(
+        usage="\n%prog <command> [options]",
+        add_help_option=False,
+        formatter=UpdatingDefaultsHelpFormatter(),
+        name="global",
+        prog=get_prog(),
+    )
+    parser.disable_interspersed_args()
+    parser.version = get_pip_version()
+    # add the general options
+    gen_opts = cmdoptions.make_option_group(cmdoptions.general_group, parser)
+    parser.add_option_group(gen_opts)
+    # so the help formatter knows
+    parser.main = True  # type: ignore
+    # create command listing for description
+    description = [""] + [
+        f"{name:27} {command_info.summary}"
+        for name, command_info in commands_dict.items()
+    ]
+    parser.description = "\n".join(description)
+    return parser
+def identify_python_interpreter(python: str) -> Optional[str]:
+    # If the named file exists, use it.
+    # If it's a directory, assume it's a virtual environment and
+    # look for the environment's Python executable.
+    if os.path.exists(python):
+        if os.path.isdir(python):
+            # bin/python for Unix, Scripts/python.exe for Windows
+            # Try both in case of odd cases like cygwin.
+            for exe in ("bin/python", "Scripts/python.exe"):
+                py = os.path.join(python, exe)
+                if os.path.exists(py):
+                    return py
+        else:
+            return python
+    # Could not find the interpreter specified
+    return None
+def parse_command(args: List[str]) -> Tuple[str, List[str]]:
+    parser = create_main_parser()
+    # Note: parser calls disable_interspersed_args(), so the result of this
+    # call is to split the initial args into the general options before the
+    # subcommand and everything else.
+    # For example:
+    #  args: ['--timeout=5', 'install', '--user', 'INITools']
+    #  general_options: ['--timeout==5']
+    #  args_else: ['install', '--user', 'INITools']
+    general_options, args_else = parser.parse_args(args)
+    # --python
+    if general_options.python and "_PIP_RUNNING_IN_SUBPROCESS" not in os.environ:
+        # Re-invoke pip using the specified Python interpreter
+        interpreter = identify_python_interpreter(general_options.python)
+        if interpreter is None:
+            raise CommandError(
+                f"Could not locate Python interpreter {general_options.python}"
+            )
+        pip_cmd = [
+            interpreter,
+            get_runnable_pip(),
+        ]
+        pip_cmd.extend(args)
+        # Set a flag so the child doesn't re-invoke itself, causing
+        # an infinite loop.
+        os.environ["_PIP_RUNNING_IN_SUBPROCESS"] = "1"
+        returncode = 0
+        try:
+            proc = subprocess.run(pip_cmd)
+            returncode = proc.returncode
+        except (subprocess.SubprocessError, OSError) as exc:
+            raise CommandError(f"Failed to run pip under {interpreter}: {exc}")
+        sys.exit(returncode)
+    # --version
+    if general_options.version:
+        sys.stdout.write(parser.version)
+        sys.stdout.write(os.linesep)
+        sys.exit()
+    # pip || pip help -> print_help()
+    if not args_else or (args_else[0] == "help" and len(args_else) == 1):
+        parser.print_help()
+        sys.exit()
+    # the subcommand name
+    cmd_name = args_else[0]
+    if cmd_name not in commands_dict:
+        guess = get_similar_commands(cmd_name)
+        msg = [f'unknown command "{cmd_name}"']
+        if guess:
+            msg.append(f'maybe you meant "{guess}"')
+        raise CommandError(" - ".join(msg))
+    # all the args without the subcommand
+    cmd_args = args[:]
+    cmd_args.remove(cmd_name)
+    return cmd_name, cmd_args

myenv/Lib/site-packages/pip/_internal/cli/parser.py ADDED Viewed

	@@ -0,0 +1,294 @@

+"""Base option parser setup"""
+import logging
+import optparse
+import shutil
+import sys
+import textwrap
+from contextlib import suppress
+from typing import Any, Dict, Generator, List, Tuple
+from pip._internal.cli.status_codes import UNKNOWN_ERROR
+from pip._internal.configuration import Configuration, ConfigurationError
+from pip._internal.utils.misc import redact_auth_from_url, strtobool
+logger = logging.getLogger(__name__)
+class PrettyHelpFormatter(optparse.IndentedHelpFormatter):
+    """A prettier/less verbose help formatter for optparse."""
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        # help position must be aligned with __init__.parseopts.description
+        kwargs["max_help_position"] = 30
+        kwargs["indent_increment"] = 1
+        kwargs["width"] = shutil.get_terminal_size()[0] - 2
+        super().__init__(*args, **kwargs)
+    def format_option_strings(self, option: optparse.Option) -> str:
+        return self._format_option_strings(option)
+    def _format_option_strings(
+        self, option: optparse.Option, mvarfmt: str = " <{}>", optsep: str = ", "
+    ) -> str:
+        """
+        Return a comma-separated list of option strings and metavars.
+        :param option:  tuple of (short opt, long opt), e.g: ('-f', '--format')
+        :param mvarfmt: metavar format string
+        :param optsep:  separator
+        """
+        opts = []
+        if option._short_opts:
+            opts.append(option._short_opts[0])
+        if option._long_opts:
+            opts.append(option._long_opts[0])
+        if len(opts) > 1:
+            opts.insert(1, optsep)
+        if option.takes_value():
+            assert option.dest is not None
+            metavar = option.metavar or option.dest.lower()
+            opts.append(mvarfmt.format(metavar.lower()))
+        return "".join(opts)
+    def format_heading(self, heading: str) -> str:
+        if heading == "Options":
+            return ""
+        return heading + ":\n"
+    def format_usage(self, usage: str) -> str:
+        """
+        Ensure there is only one newline between usage and the first heading
+        if there is no description.
+        """
+        msg = "\nUsage: {}\n".format(self.indent_lines(textwrap.dedent(usage), "  "))
+        return msg
+    def format_description(self, description: str) -> str:
+        # leave full control over description to us
+        if description:
+            if hasattr(self.parser, "main"):
+                label = "Commands"
+            else:
+                label = "Description"
+            # some doc strings have initial newlines, some don't
+            description = description.lstrip("\n")
+            # some doc strings have final newlines and spaces, some don't
+            description = description.rstrip()
+            # dedent, then reindent
+            description = self.indent_lines(textwrap.dedent(description), "  ")
+            description = f"{label}:\n{description}\n"
+            return description
+        else:
+            return ""
+    def format_epilog(self, epilog: str) -> str:
+        # leave full control over epilog to us
+        if epilog:
+            return epilog
+        else:
+            return ""
+    def indent_lines(self, text: str, indent: str) -> str:
+        new_lines = [indent + line for line in text.split("\n")]
+        return "\n".join(new_lines)
+class UpdatingDefaultsHelpFormatter(PrettyHelpFormatter):
+    """Custom help formatter for use in ConfigOptionParser.
+    This is updates the defaults before expanding them, allowing
+    them to show up correctly in the help listing.
+    Also redact auth from url type options
+    """
+    def expand_default(self, option: optparse.Option) -> str:
+        default_values = None
+        if self.parser is not None:
+            assert isinstance(self.parser, ConfigOptionParser)
+            self.parser._update_defaults(self.parser.defaults)
+            assert option.dest is not None
+            default_values = self.parser.defaults.get(option.dest)
+        help_text = super().expand_default(option)
+        if default_values and option.metavar == "URL":
+            if isinstance(default_values, str):
+                default_values = [default_values]
+            # If its not a list, we should abort and just return the help text
+            if not isinstance(default_values, list):
+                default_values = []
+            for val in default_values:
+                help_text = help_text.replace(val, redact_auth_from_url(val))
+        return help_text
+class CustomOptionParser(optparse.OptionParser):
+    def insert_option_group(
+        self, idx: int, *args: Any, **kwargs: Any
+    ) -> optparse.OptionGroup:
+        """Insert an OptionGroup at a given position."""
+        group = self.add_option_group(*args, **kwargs)
+        self.option_groups.pop()
+        self.option_groups.insert(idx, group)
+        return group
+    @property
+    def option_list_all(self) -> List[optparse.Option]:
+        """Get a list of all options, including those in option groups."""
+        res = self.option_list[:]
+        for i in self.option_groups:
+            res.extend(i.option_list)
+        return res
+class ConfigOptionParser(CustomOptionParser):
+    """Custom option parser which updates its defaults by checking the
+    configuration files and environmental variables"""
+    def __init__(
+        self,
+        *args: Any,
+        name: str,
+        isolated: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        self.name = name
+        self.config = Configuration(isolated)
+        assert self.name
+        super().__init__(*args, **kwargs)
+    def check_default(self, option: optparse.Option, key: str, val: Any) -> Any:
+        try:
+            return option.check_value(key, val)
+        except optparse.OptionValueError as exc:
+            print(f"An error occurred during configuration: {exc}")
+            sys.exit(3)
+    def _get_ordered_configuration_items(
+        self,
+    ) -> Generator[Tuple[str, Any], None, None]:
+        # Configuration gives keys in an unordered manner. Order them.
+        override_order = ["global", self.name, ":env:"]
+        # Pool the options into different groups
+        section_items: Dict[str, List[Tuple[str, Any]]] = {
+            name: [] for name in override_order
+        }
+        for section_key, val in self.config.items():
+            # ignore empty values
+            if not val:
+                logger.debug(
+                    "Ignoring configuration key '%s' as it's value is empty.",
+                    section_key,
+                )
+                continue
+            section, key = section_key.split(".", 1)
+            if section in override_order:
+                section_items[section].append((key, val))
+        # Yield each group in their override order
+        for section in override_order:
+            for key, val in section_items[section]:
+                yield key, val
+    def _update_defaults(self, defaults: Dict[str, Any]) -> Dict[str, Any]:
+        """Updates the given defaults with values from the config files and
+        the environ. Does a little special handling for certain types of
+        options (lists)."""
+        # Accumulate complex default state.
+        self.values = optparse.Values(self.defaults)
+        late_eval = set()
+        # Then set the options with those values
+        for key, val in self._get_ordered_configuration_items():
+            # '--' because configuration supports only long names
+            option = self.get_option("--" + key)
+            # Ignore options not present in this parser. E.g. non-globals put
+            # in [global] by users that want them to apply to all applicable
+            # commands.
+            if option is None:
+                continue
+            assert option.dest is not None
+            if option.action in ("store_true", "store_false"):
+                try:
+                    val = strtobool(val)
+                except ValueError:
+                    self.error(
+                        "{} is not a valid value for {} option, "  # noqa
+                        "please specify a boolean value like yes/no, "
+                        "true/false or 1/0 instead.".format(val, key)
+                    )
+            elif option.action == "count":
+                with suppress(ValueError):
+                    val = strtobool(val)
+                with suppress(ValueError):
+                    val = int(val)
+                if not isinstance(val, int) or val < 0:
+                    self.error(
+                        "{} is not a valid value for {} option, "  # noqa
+                        "please instead specify either a non-negative integer "
+                        "or a boolean value like yes/no or false/true "
+                        "which is equivalent to 1/0.".format(val, key)
+                    )
+            elif option.action == "append":
+                val = val.split()
+                val = [self.check_default(option, key, v) for v in val]
+            elif option.action == "callback":
+                assert option.callback is not None
+                late_eval.add(option.dest)
+                opt_str = option.get_opt_string()
+                val = option.convert_value(opt_str, val)
+                # From take_action
+                args = option.callback_args or ()
+                kwargs = option.callback_kwargs or {}
+                option.callback(option, opt_str, val, self, *args, **kwargs)
+            else:
+                val = self.check_default(option, key, val)
+            defaults[option.dest] = val
+        for key in late_eval:
+            defaults[key] = getattr(self.values, key)
+        self.values = None
+        return defaults
+    def get_default_values(self) -> optparse.Values:
+        """Overriding to make updating the defaults after instantiation of
+        the option parser possible, _update_defaults() does the dirty work."""
+        if not self.process_default_values:
+            # Old, pre-Optik 1.5 behaviour.
+            return optparse.Values(self.defaults)
+        # Load the configuration, or error out in case of an error
+        try:
+            self.config.load()
+        except ConfigurationError as err:
+            self.exit(UNKNOWN_ERROR, str(err))
+        defaults = self._update_defaults(self.defaults.copy())  # ours
+        for option in self._get_all_options():
+            assert option.dest is not None
+            default = defaults.get(option.dest)
+            if isinstance(default, str):
+                opt_str = option.get_opt_string()
+                defaults[option.dest] = option.check_value(opt_str, default)
+        return optparse.Values(defaults)
+    def error(self, msg: str) -> None:
+        self.print_usage(sys.stderr)
+        self.exit(UNKNOWN_ERROR, f"{msg}\n")