



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


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

class      __magic_name__						(   _UpperCamelCase       ):







       def __init__(							self :	int     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Tuple		):
         super().__init__()
         self.register_modules(unet=_UpperCAmelCase     ,scheduler=_UpperCAmelCase		)







       @torch.no_grad()
       def __call__(							self :	str     ,_UpperCAmelCase :	int = 1     ,_UpperCAmelCase :	int = 100     ,_UpperCAmelCase :	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,_UpperCAmelCase :	Optional[float] = None     ,_UpperCAmelCase :	bool = True     ,):

         if audio_length_in_s is None:
           _a					:				str				=							self.unet.config.sample_size / self.unet.config.sample_rate

         _a					:				Any				=							audio_length_in_s * self.unet.config.sample_rate

         _a					:				Tuple				=							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}."""		)

         _a					:				Tuple				=							int(_UpperCAmelCase		)
         if sample_size % down_scale_factor != 0:
           _a					:				Tuple				=							(
               (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.'		)
         _a					:				List[Any]				=							int(_UpperCAmelCase		)

         _a					:				int				=							next(iter(self.unet.parameters()		)		).dtype
         _a					:				int				=							(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."""		)

         _a					:				Union[str, Any]				=							randn_tensor(_UpperCAmelCase     ,generator=_UpperCAmelCase     ,device=self.device     ,dtype=_UpperCAmelCase		)

         # set step values
         self.scheduler.set_timesteps(_UpperCAmelCase     ,device=audio.device		)
         _a					:				str				=							self.scheduler.timesteps.to(_UpperCAmelCase		)

         for t in self.progress_bar(self.scheduler.timesteps		):
           # 1. predict noise model_output
           _a					:				Any				=							self.unet(_UpperCAmelCase     ,_UpperCAmelCase		).sample

           # 2. compute previous image: x_t -> t_t-1
           _a					:				Dict				=							self.scheduler.step(_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase		).prev_sample

         _a					:				List[str]				=							audio.clamp(-1     ,1		).float().cpu().numpy()

         _a					:				Union[str, Any]				=							audio[:, :, :original_sample_size]

         if not return_dict:
           return (audio,)

         return AudioPipelineOutput(audios=_UpperCAmelCase		)









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




