






from typing import List, Optional, Tuple, Union

import torch

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


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


class    _lowerCAmelCase			(		a__		):



				"""simple docstring"""



				def __init__(      self    :    str			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    List[Any]):






											'''simple docstring'''







											super().__init__()
											self.register_modules(unet=lowerCamelCase_			,		scheduler=lowerCamelCase_)



				@torch.no_grad()
				def __call__(      self    :    Dict			,		UpperCamelCase__    :    List[Any] = 1			,		UpperCamelCase__    :    Tuple = 1_0_0			,		UpperCamelCase__    :    Dict = None			,		UpperCamelCase__    :    Optional[Any] = None			,		UpperCamelCase__    :    Tuple = True			,		):






											'''simple docstring'''







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

											snake_case__	  =       audio_length_in_s * self.unet.config.sample_rate

											snake_case__	  =       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}.''')

											snake_case__	  =       int(lowerCamelCase_)
											if sample_size % down_scale_factor != 0:
																		snake_case__	  =       (
																		    (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.""")
											snake_case__	  =       int(lowerCamelCase_)

											snake_case__	  =       next(iter(self.unet.parameters())).dtype
											snake_case__	  =       (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.''')

											snake_case__	  =       randn_tensor(lowerCamelCase_			,		generator=lowerCamelCase_			,		device=self.device			,		dtype=lowerCamelCase_)

											# set step values
											self.scheduler.set_timesteps(lowerCamelCase_			,		device=audio.device)
											snake_case__	  =       self.scheduler.timesteps.to(lowerCamelCase_)

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

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

											snake_case__	  =       audio.clamp(-1			,		1).float().cpu().numpy()

											snake_case__	  =       audio[:, :, :original_sample_size]

											if not return_dict:
																		return (audio,)

											return AudioPipelineOutput(audios=lowerCamelCase_)








from math import factorial

a__       =     {str(digit): factorial(digit) for digit in range(1_0)}

def _UpperCAmelCase   (	a						:	int ):
							if not isinstance(a  ,					a ):
														raise TypeError("""Parameter number must be int""" )

							if number < 0:
														raise ValueError("""Parameter number must be greater than or equal to 0""" )

							# Converts number in string to iterate on its digits and adds its factorial.
							return sum(DIGIT_FACTORIAL[digit] for digit in str(a ) )

def _UpperCAmelCase   (	a						:	int = 60  ,					a						:	int = 100_0000 ):

							if not isinstance(a  ,					a ) or not isinstance(a  ,					a ):
														raise TypeError("""Parameters chain_length and number_limit must be int""" )

							if chain_length <= 0 or number_limit <= 0:
														raise ValueError(
														    """Parameters chain_length and number_limit must be greater than 0""" )

							# the counter for the chains with the exact desired length
							snake_case__	  =       0
							# the cached sizes of the previous chains
							snake_case__	  =       {}

							for start_chain_element in range(1  ,					a ):
														# The temporary set will contain the elements of the chain
														snake_case__	  =       set()
														snake_case__	  =       0

														# Stop computing the chain when you find a cached size, a repeating item or the
														# length is greater then the desired one.
														snake_case__	  =       start_chain_element
														while (
														    chain_element not in chain_sets_lengths
														    and chain_element not in chain_set
														    and chain_set_length <= chain_length
														):
																					chain_set.add(a )
																					chain_set_length += 1
																					snake_case__	  =       digit_factorial_sum(a )

														if chain_element in chain_sets_lengths:
																					chain_set_length += chain_sets_lengths[chain_element]

														snake_case__	  =       chain_set_length

														# If chain contains the exact amount of elements increase the counter
														if chain_set_length == chain_length:
																					chains_counter += 1

							return chains_counter


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution()}''')
