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make_pack_jsonl_for_wiki.py

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+import json
+import os
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import re
+import concurrent.futures
+from pathlib import Path
+
+# 検索するディレクトリのパス
+file_path = 'wiki_concat_jsonl.jsonl'
+
+model_id = "geniacllm/dMoE_8B_math_iter8999"
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False, torch_dtype="auto", device_map="auto")
+print(" load tokenizer ")
+
+# .jsonl ファイルのフルパスを格納するリスト
+jsonl_files = []
+jsonl_datas = []
+
+with open(file_path, 'r', encoding='utf-8') as file:
+    for line in file:
+        # JSON形式のデータをPythonの辞書に変換
+        data = json.loads(line)
+        jsonl_datas.append(data["text"])
+
+# print("data", gsm8l_datas )
+print("data", len(jsonl_datas) )
+print("data", jsonl_datas[-1] )
+
+# model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
+# print(model)
+# inputs = tokenizer("地球温暖化の主な原因となる物質は", return_tensors="pt").to(model.device)
+# inputs
+
+all_data = []
+word_num = 0
+
+short_full_document_list =[]
+complete_full_document_list =[]
+count = 0
+
+total_items = len(jsonl_datas)
+# big_list = list(range(total_items))
+
+# サブリストの数
+num_chunks = 25
+# 各サブリストの長さを計算
+items_per_sublist = total_items // num_chunks
+# サブリストを生成
+chunks = [jsonl_datas[i * items_per_sublist:(i + 1) * items_per_sublist] for i in range(num_chunks)]
+
+# 最後のサブリストに残りのアイテムを追加する
+if total_items % num_chunks != 0:
+    remaining_items = jsonl_datas[num_chunks * items_per_sublist:]
+    chunks[-1].extend(remaining_items)
+
+# 並列を行う関数
+def process_item(sublists):
+    for index, one_data in tqdm( enumerate(sublists)):
+        one_data_token = tokenizer(one_data, return_tensors="pt", add_special_tokens=True)
+        one_data_token_len =  one_data_token.input_ids[0].size(0) 
+        if   one_data_token_len < 50:
+            pass
+        elif   one_data_token_len < 1600:
+            short_full_document_list.append( {'length': one_data_token_len, 'text': one_data } )
+        elif  one_data_token_len < 2010:
+            complete_full_document_list.append( {'length': one_data_token_len, 'text': one_data } )
+        else:
+            one_sentences = one_data.split('。')
+            collect_one_sentences = []
+            split_one_sentence_len = 0
+            for index, sentence in enumerate(one_sentences):
+                one_sentence_token = tokenizer(sentence, return_tensors="pt", add_special_tokens=True)
+                one_sentence_token_len =  one_sentence_token.input_ids[0].size(0) 
+                if split_one_sentence_len + one_sentence_token_len < 2010:
+                    split_one_sentence_len = split_one_sentence_len + one_sentence_token_len
+                    collect_one_sentences.append( sentence )
+                elif one_sentence_token_len > 2010:
+                    print(" Warning : Over one-sentence token  :", one_sentence_token_len  )
+                    print( sentence  )
+                    split_document = "\n".join(collect_one_sentences)
+                    short_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } )
+                    split_one_sentence_len = 0
+                    collect_one_sentences.clear()
+                else :
+                    split_document = "\n".join(collect_one_sentences)
+                    complete_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } )
+                    split_one_sentence_len = one_sentence_token_len
+                    collect_one_sentences.clear()
+                    collect_one_sentences.append( sentence )
+            split_document = "\n".join(collect_one_sentences)
+            if split_one_sentence_len < 1500 :
+                short_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } )
+            elif split_one_sentence_len < 2015 :
+                complete_full_document_list.append( {'length': split_one_sentence_len, 'text': split_document } )
+            else:
+                print(" Warning : Over split-one-sentence token  :", split_one_sentence_len  )
+                print( split_document  )
+        # if index > 96605:
+        #     break
+
+    complete_full_document_sub_list = []
+
+    while True:
+        if len(short_full_document_list) == 0:
+            break
+
+        if len(short_full_document_list)%500 == 0:
+            print( "残り : ", len(short_full_document_list) )
+        target = short_full_document_list[0]
+        left_len = 2010 -  target['length'] 
+        if left_len < 0 :
+            print(" Error : Over token   target :", target['length']  )
+        del short_full_document_list[0]  # 0番目の要素を削除
+        if len(short_full_document_list) == 0:
+            complete_full_document_sub_list.append( target )
+            break
+        else:
+            # ステップ1: 最小差分を計算（デフォルト値を使用）
+            closest_length_diff = min((left_len - rec['length'] for rec in short_full_document_list if rec['length'] <= left_len), default=None)
+
+            if closest_length_diff is None:
+                complete_full_document_sub_list.append( target )
+                # print( "complete_full_document_list.append"  )
+            else:
+                # 特定の条件に合致するデータのインデックスを探す
+                index = next((i for i, rec in enumerate(short_full_document_list) if rec['length'] == left_len - closest_length_diff ), None)
+                merge_document = short_full_document_list[index]
+                del short_full_document_list[index]  # index番目の要素を削除 
+                # 結果の出力
+                if index is not None:
+                    merge_texts = target['text'] + "</s>" + merge_document['text'] 
+                    if target['length']  + merge_document['length']  < 1500 :
+                        short_full_document_list.append( {'length': target['length']  + merge_document['length'] + 1 , 'text': merge_texts } )
+                        # print( "merge_texts" , merge_texts )
+                        # print( "short_full_document_list" , short_full_document_list[-1] )
+                    elif  target['length']  + merge_document['length']  > 2022 :
+                        print(" Error : Over token   target :", target['length'] , "  merge_document : ", merge_document['length']    )
+                    else:
+                        complete_full_document_sub_list.append( {'length': target['length']  + merge_document['length'] + 1 , 'text': merge_texts } )
+                        # print( "merge_texts" , merge_texts )
+                else:
+                    print(f"長さが {merge_document['length'] } のデータは見つかりませんでした。")
+
+    return complete_full_document_sub_list
+
+# print( "L110 sublists " , len( chunks) )
+# プロセスプールを使用して各チャンクを並列処理
+with concurrent.futures.ProcessPoolExecutor() as executor:
+    results = list(executor.map(process_item, chunks))
+# print( "L114 results " , len( results) )
+# for result in results:
+    # print( "L116 result " , len( result) )
+
+# リスト内包表記を使って平坦化
+# sumを使って平坦化
+complete_full_document_list.extend( sum(results, [] ))
+
+print( "L121 complete_full_document_list " , len( complete_full_document_list) )
+
+length_list = []
+collect_length_list = []
+file_count = 0
+
+complete_full_document_list_len = len(complete_full_document_list)
+# 各ファイルに格納する最大ドキュメント数
+max_docs_per_file = 9999
+
+# 必要なファイルの数
+jsonl_num = complete_full_document_list_len // max_docs_per_file + (1 if complete_full_document_list_len % max_docs_per_file != 0 else 0)
+
+# ファイルにデータを分割して保存
+for i in range(jsonl_num):
+    start_index = i * max_docs_per_file
+    end_index = start_index + max_docs_per_file
+    # ファイル名を生成し、連番を付ける
+    filename = f"{i}_speech_text.jsonl"  # ファイル名形式: "番号_名前.jsonl"
+
+    # ファイルを開いてデータを書き込む
+    with open(filename, 'w', encoding='utf-8') as file:
+        for document in complete_full_document_list[start_index:end_index]:
+            # 各テキストに対するJSONオブジェクトを作成
+            json_obj = {
+                "text": document['text'] ,
+                "is_rejected": False,
+                "reason": {}
+            }
+            # JSON形式の文字列に変換
+            json_line = json.dumps(json_obj, ensure_ascii=False)
+            # ファイルに書き込み
+            file.write(json_line + '\n')
+            length_list.append( document['length'] ) 
+            document_tokens = tokenizer(document['text'] , return_tensors="pt", add_special_tokens=True)
+            document_tokens_len =  document_tokens.input_ids[0].size(0) 
+            collect_length_list.append( document_tokens_len )
+            if document_tokens_len > 2021:
+                print("L209 Error : Over token ", document['length'] , "  collect : ", document_tokens_len  )
+print( "len(complete_full_document_list) " , len(complete_full_document_list) )
+# print( "length_list " , length_list )