



"""simple docstring"""






from queue import PriorityQueue
from typing import Any

import numpy as np






def      snake_case__   (		__lowerCamelCase				:    dict							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    set							,		__lowerCamelCase				:    set							,		__lowerCamelCase				:    dict							,		__lowerCamelCase				:    dict							,		__lowerCamelCase				:    PriorityQueue							,		__lowerCamelCase				:    dict							,		__lowerCamelCase				:    float | int							,		):
			"""simple docstring"""
			for nxt, d in graph[v]:
						if nxt in visited_forward:
									continue
						lowerCamelCase__		:		Optional[Any]			=cst_fwd.get(__lowerCamelCase							,		np.inf	)
						lowerCamelCase__		:		List[str]			=cst_fwd[v] + d
						if new_cost_f < old_cost_f:
									queue.put((new_cost_f, nxt)	)
									lowerCamelCase__		:		Tuple			=new_cost_f
									lowerCamelCase__		:		Optional[Any]			=v
						if nxt in visited_backward:
									if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
												lowerCamelCase__		:		Optional[Any]			=cst_fwd[v] + d + cst_bwd[nxt]
			return shortest_distance






def      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    dict							,		__lowerCamelCase				:    dict	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=-1

			lowerCamelCase__		:		List[Any]			=set()
			lowerCamelCase__		:		Any			=set()
			lowerCamelCase__		:		Any			={source: 0}
			lowerCamelCase__		:		Any			={destination: 0}
			lowerCamelCase__		:		List[Any]			={source: None}
			lowerCamelCase__		:		int			={destination: None}
			lowerCamelCase__		:		PriorityQueue[Any]			=PriorityQueue()
			lowerCamelCase__		:		PriorityQueue[Any]			=PriorityQueue()

			lowerCamelCase__		:		List[Any]			=np.inf

			queue_forward.put((0, source)	)
			queue_backward.put((0, destination)	)

			if source == destination:
						return 0

			while not queue_forward.empty() and not queue_backward.empty():
						lowerCamelCase__	,   lowerCamelCase__		:		Tuple			=queue_forward.get()
						visited_forward.add(__lowerCamelCase	)

						lowerCamelCase__	,   lowerCamelCase__		:		Optional[Any]			=queue_backward.get()
						visited_backward.add(__lowerCamelCase	)

						lowerCamelCase__		:		Union[str, Any]			=pass_and_relaxation(
						    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		)

						lowerCamelCase__		:		Optional[int]			=pass_and_relaxation(
						    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		)

						if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
									break

			if shortest_distance != np.inf:
						lowerCamelCase__		:		int			=shortest_distance
			return shortest_path_distance


_lowercase : Union[str, Any]      						=						{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
_lowercase : Any      						=						{
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


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

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		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}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		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.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, 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.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

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

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

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




