
'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A				(							__snake_case						):





     def __init__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Any:






         """simple docstring"""






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





     @torch.no_grad()
     def __call__(							self						,			SCREAMING_SNAKE_CASE = 1						,			SCREAMING_SNAKE_CASE = 100						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = True						,			) ->				Union[AudioPipelineOutput, Tuple]:






         """simple docstring"""






         if audio_length_in_s is None:
             A       :   Union[str, Any]					=						self.unet.config.sample_size / self.unet.config.sample_rate

         A       :   Dict					=						audio_length_in_s * self.unet.config.sample_rate

         A       :   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}.'	)

         A       :   Union[str, Any]					=						int(SCREAMING_SNAKE_CASE	)
         if sample_size % down_scale_factor != 0:
             A       :   int					=						(
                 (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       :   Optional[Any]					=						int(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						next(iter(self.unet.parameters()	)	).dtype
         A       :   Any					=						(batch_size, self.unet.config.in_channels, sample_size)
         if isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) and len(SCREAMING_SNAKE_CASE	) != batch_size:
             raise ValueError(
                 F'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE	)}, but requested an effective batch'
                 F' size of {batch_size}. Make sure the batch size matches the length of the generators.'	)

         A       :   str					=						randn_tensor(SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			device=self.device						,			dtype=SCREAMING_SNAKE_CASE	)

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE						,			device=audio.device	)
         A       :   Optional[Any]					=						self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE	)

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

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

         A       :   str					=						audio.clamp(-1						,			1	).float().cpu().numpy()

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

         if not return_dict:
             return (audio,)

         return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE	)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			lowercase       =    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			lowercase       =    max(
			    mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							) ,							mf_knapsack(i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							j - wt[i - 1]							) + val[i - 1] ,							)
		lowercase       =    val
	return f[i][j]






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    [[0] * (w + 1) for _ in range(n + 1							)]

	for i in range(1 ,							n + 1							):
		for w_ in range(1 ,							w + 1							):
			if wt[i - 1] <= w_:
				lowercase       =    max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,							dp[i - 1][w_]							)
			else:
				lowercase       =    dp[i - 1][w_]

	return dp[n][w_], dp






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	if not (isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							) and isinstance(__SCREAMING_SNAKE_CASE ,							(list, tuple)							)):
		raise ValueError(
		    'Both the weights and values vectors must be either lists or tuples'							)

	lowercase       =    len(__SCREAMING_SNAKE_CASE							)
	if num_items != len(__SCREAMING_SNAKE_CASE							):
		lowercase       =    (
		    'The number of weights must be the same as the number of values.\n'
		    F'''But got {num_items} weights and {len(__SCREAMING_SNAKE_CASE							)} values'''
		)
		raise ValueError(__SCREAMING_SNAKE_CASE							)
	for i in range(__SCREAMING_SNAKE_CASE							):
		if not isinstance(wt[i] ,							__SCREAMING_SNAKE_CASE							):
			lowercase       =    (
			    'All weights must be integers but got weight of '
			    F'''type {type(wt[i]							)} at index {i}'''
			)
			raise TypeError(__SCREAMING_SNAKE_CASE							)

	lowercase  ,					lowercase       =    knapsack(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
	lowercase       =    set()
	_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)

	return optimal_val, example_optional_set






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							)
		else:
			optimal_set.add(__SCREAMING_SNAKE_CASE							)
			_construct_solution(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							i - 1 ,							j - wt[i - 1] ,							__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = [3, 2, 4, 4]
	UpperCAmelCase	      = [4, 3, 2, 3]
	UpperCAmelCase	      = 4
	UpperCAmelCase	      = 6
	UpperCAmelCase	      = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
	UpperCAmelCase      , UpperCAmelCase	      = knapsack(w, wt, val, n)
	print(optimal_solution)
	print(mf_knapsack(n, wt, val, w))  # switched the n and w

	# testing the dynamic programming problem with example
	# the optimal subset for the above example are items 3 and 4
	UpperCAmelCase      , UpperCAmelCase	      = knapsack_with_example_solution(w, wt, val)
	assert optimal_solution == 8
	assert optimal_subset == {3, 4}
	print('''optimal_value = ''', optimal_solution)
	print('''An optimal subset corresponding to the optimal value''', optimal_subset)

