



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   def __init__(					self							:			List[Any]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Union[str, Any])				->				Any:

    """simple docstring"""







    super().__init__()

    # make sure scheduler can always be converted to DDIM
    _snake_case    :  int					=					DDIMScheduler.from_config(scheduler.config)

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


   @torch.no_grad()
   def __call__(					self							:			Optional[Any]				,		lowerCAmelCase							:			int = 1				,		lowerCAmelCase							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		lowerCAmelCase							:			float = 0.0				,		lowerCAmelCase							:			int = 50				,		lowerCAmelCase							:			Optional[bool] = None				,		lowerCAmelCase							:			Optional[str] = "pil"				,		lowerCAmelCase							:			bool = True				,		)				->				Union[ImagePipelineOutput, Tuple]:

    """simple docstring"""







    if isinstance(self.unet.config.sample_size				,		lowerCAmelCase):
     _snake_case    :  List[Any]					=					(
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
    else:
     _snake_case    :  List[Any]					=					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

    if isinstance(lowerCAmelCase				,		lowerCAmelCase) and len(lowerCAmelCase) != batch_size:
     raise ValueError(
         F'''You have passed a list of generators of length {len(lowerCAmelCase)}, but requested an effective batch'''
         F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

    _snake_case    :  Optional[Any]					=					randn_tensor(lowerCAmelCase				,		generator=lowerCAmelCase				,		device=self.device				,		dtype=self.unet.dtype)

    # set step values
    self.scheduler.set_timesteps(lowerCAmelCase)

    for t in self.progress_bar(self.scheduler.timesteps):
     # 1. predict noise model_output
     _snake_case    :  str					=					self.unet(lowerCAmelCase				,		lowerCAmelCase).sample

     # 2. predict previous mean of image x_t-1 and add variance depending on eta
     # eta corresponds to η in paper and should be between [0, 1]
     # do x_t -> x_t-1
     _snake_case    :  List[str]					=					self.scheduler.step(
         lowerCAmelCase				,		lowerCAmelCase				,		lowerCAmelCase				,		eta=lowerCAmelCase				,		use_clipped_model_output=lowerCAmelCase				,		generator=lowerCAmelCase).prev_sample

    _snake_case    :  int					=					(image / 2 + 0.5).clamp(0				,		1)
    _snake_case    :  Any					=					image.cpu().permute(0				,		2				,		3				,		1).numpy()
    if output_type == "pil":
     _snake_case    :  Optional[int]					=					self.numpy_to_pil(lowerCAmelCase)

    if not return_dict:
     return (image,)

    return ImagePipelineOutput(images=lowerCAmelCase)





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
