from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__lowercase							 =					logging.get_logger(__name__)  # pylint: disable=invalid-name


class 	lowerCamelCase_     (   _a ):






    '''simple docstring'''






    def __init__(	self , __lowercase , __lowercase)       ->	int:
        super().__init__()
        self.register_modules(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase)







    @torch.no_grad()
    def __call__(	self , __lowercase = 1 , __lowercase = 100 , __lowercase = None , __lowercase = None , __lowercase = True , )       ->	Any:

        if audio_length_in_s is None:
            __UpperCamelCase     :List[Any]			  =		self.unet.config.sample_size / self.unet.config.sample_rate

        __UpperCamelCase     :Optional[Any]			  =		audio_length_in_s * self.unet.config.sample_rate

        __UpperCamelCase     :Optional[Any]			  =		2 ** len(self.unet.up_blocks)
        if sample_size < 3 * down_scale_factor:
            raise ValueError(
                f"""{audio_length_in_s} is too small. Make sure it\'s bigger or equal to"""
                f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""")

        __UpperCamelCase     :int			  =		int(__UpperCAmelCase)
        if sample_size % down_scale_factor != 0:
            __UpperCamelCase     :Any			  =		(
                (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
            ) * down_scale_factor
            logger.info(
                f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                ''' process.''')
        __UpperCamelCase     :List[Any]			  =		int(__UpperCAmelCase)

        __UpperCamelCase     :Dict			  =		next(iter(self.unet.parameters())).dtype
        __UpperCamelCase     :str			  =		(batch_size, self.unet.config.in_channels, sample_size)
        if isinstance(__UpperCAmelCase , __UpperCAmelCase) and len(__UpperCAmelCase) != batch_size:
            raise ValueError(
                f"""You have passed a list of generators of length {len(__UpperCAmelCase)}, but requested an effective batch"""
                f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

        __UpperCamelCase     :List[Any]			  =		randn_tensor(__UpperCAmelCase , generator=__UpperCAmelCase , device=self.device , dtype=__UpperCAmelCase)

        # set step values
        self.scheduler.set_timesteps(__UpperCAmelCase , device=audio.device)
        __UpperCamelCase     :List[str]			  =		self.scheduler.timesteps.to(__UpperCAmelCase)

        for t in self.progress_bar(self.scheduler.timesteps):
            # 1. predict noise model_output
            __UpperCamelCase     :Dict			  =		self.unet(__UpperCAmelCase , __UpperCAmelCase).sample

            # 2. compute previous image: x_t -> t_t-1
            __UpperCamelCase     :Optional[Any]			  =		self.scheduler.step(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase).prev_sample

        __UpperCamelCase     :Tuple			  =		audio.clamp(-1 , 1).float().cpu().numpy()

        __UpperCamelCase     :str			  =		audio[:, :, :original_sample_size]

        if not return_dict:
            return (audio,)

        return AudioPipelineOutput(audios=__UpperCAmelCase)









"""simple docstring"""







from __future__ import annotations

from PIL import Image

# Define glider example
__lowercase    					=      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__lowercase    					=      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      lowercase			(				A_       )->      list[list[int]]:
  '''simple docstring'''







  a						:		str      							=			[]
  for i in range(len(A_       )       ):
    a						:		str      							=			[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      a						:		Union[str, Any]      							=			0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(A_       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(A_       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(A_       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      a						:		Tuple      							=			cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(A_       )
  return next_generation


def      lowercase			(				A_  ,   A_       )->      list[Image.Image]:
  '''simple docstring'''







  a						:		List[str]      							=			[]
  for _ in range(A_       ):
    # Create output image
    a						:		str      							=			Image.new("RGB"  ,   (len(cells[0]       ), len(A_       ))       )
    a						:		Union[str, Any]      							=			img.load()

    # Save cells to image
    for x in range(len(A_       )       ):
      for y in range(len(cells[0]       )       ):
        a						:		Optional[Any]      							=			255 - cells[y][x] * 255
        a						:		str      							=			(colour, colour, colour)

        # Save image
    images.append(A_       )
    a						:		Tuple      							=			new_generation(A_       )
  return images


if __name__ == "__main__":
 __lowercase    					=      generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])






