# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNet2DModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class PNDMPipeline(DiffusionPipeline):
    r"""
    Pipeline for unconditional image generation.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
    implemented for all pipelines (downloading, saving, running on a particular device, etc.).

    Parameters:
        unet ([`UNet2DModel`]):
            A `UNet2DModel` to denoise the encoded image latents.
        scheduler ([`PNDMScheduler`]):
            A `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image.
    """

    unet: UNet2DModel
    scheduler: PNDMScheduler

    def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):
        super().__init__()

        scheduler = PNDMScheduler.from_config(scheduler.config)

        self.register_modules(unet=unet, scheduler=scheduler)

    @torch.no_grad()
    def __call__(
        self,
        batch_size: int = 1,
        num_inference_steps: int = 50,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        **kwargs,
    ) -> Union[ImagePipelineOutput, Tuple]:
        r"""
        The call function to the pipeline for generation.

        Args:
            batch_size (`int`, `optional`, defaults to 1):
                The number of images to generate.
            num_inference_steps (`int`, `optional`, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            generator (`torch.Generator`, `optional`):
                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
                generation deterministic.
            output_type (`str`, `optional`, defaults to `"pil"`):
                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.

        Example:

        ```py
        >>> from diffusers import PNDMPipeline

        >>> # load model and scheduler
        >>> pndm = PNDMPipeline.from_pretrained("google/ddpm-cifar10-32")

        >>> # run pipeline in inference (sample random noise and denoise)
        >>> image = pndm().images[0]

        >>> # save image
        >>> image.save("pndm_generated_image.png")
        ```

        Returns:
            [`~pipelines.ImagePipelineOutput`] or `tuple`:
                If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
                returned where the first element is a list with the generated images.
        """
        # For more information on the sampling method you can take a look at Algorithm 2 of
        # the official paper: https://arxiv.org/pdf/2202.09778.pdf

        # Sample gaussian noise to begin loop
        image = randn_tensor(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
            generator=generator,
            device=self.device,
        )

        self.scheduler.set_timesteps(num_inference_steps)
        for t in self.progress_bar(self.scheduler.timesteps):
            model_output = self.unet(image, t).sample

            image = self.scheduler.step(model_output, t, image).prev_sample

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=image)
