



"""simple docstring"""






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class     UpperCAmelCase    (	unittest.TestCase  ):



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     # For consistency across different places the DisjunctiveConstraint is called,
     # dc.token_ids is a list of integers. It is also initialized only by integers.

     _snake_case   	=      [[1, 2, 4], [1, 2, 3, 4]]
     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)
     self.assertTrue(isinstance(dc.token_ids							,       __lowerCamelCase				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]				)				)

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint([torch.LongTensor([1, 2, 4]				), torch.LongTensor([1, 2, 3, 4, 5]				)]				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # We can't have constraints that are complete subsets of another. This leads to a preverse
     # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
     # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
     # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
     # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
     _snake_case   	=      [[1, 2], [1, 2, 3, 4]]

     with self.assertRaises(__lowerCamelCase				):
       DisjunctiveConstraint(__lowerCamelCase				)  # fails here



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     _snake_case   	=      stepped is True and completed is False and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(3				)
     _snake_case   	=      stepped is True and completed is True and reset is False
     self.assertTrue(__lowerCamelCase				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 3]				)





   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

     _snake_case   	=      DisjunctiveConstraint(__lowerCamelCase				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(4				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.current_seq == [1, 2, 4]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.current_seq == [1, 2, 4, 5]				)

     dc.reset()

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(1				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 3				)
     self.assertTrue(dc.current_seq == [1]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(2				)
     self.assertTrue(not dc.completed				)
     self.assertTrue(dc.remaining() == 2				)
     self.assertTrue(dc.current_seq == [1, 2]				)

     _snake_case	,     _snake_case	,     _snake_case   	=      dc.update(5				)
     self.assertTrue(dc.completed				)  # Completed!
     self.assertTrue(dc.remaining() == 0				)
     self.assertTrue(dc.current_seq == [1, 2, 5]				)






'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


__lowerCAmelCase						:							List[Any]    							=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class     A ( UpperCAmelCase ):





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






							@torch.no_grad()
							def __call__(   self					:				Tuple  ,		__a					:				int = 1  ,		__a					:				int = 1_0_0  ,		__a					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,		__a					:				Optional[float] = None  ,		__a					:				bool = True  ,		)				->						Union[AudioPipelineOutput, Tuple]:

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

								__UpperCAmelCase      					=      audio_length_in_s * self.unet.config.sample_rate

								__UpperCAmelCase      					=      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}."""						)

								__UpperCAmelCase      					=      int(__a						)
								if sample_size % down_scale_factor != 0:
									__UpperCAmelCase      					=      (
									    (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.'''						)
								__UpperCAmelCase      					=      int(__a						)

								__UpperCAmelCase      					=      next(iter(self.unet.parameters()						)						).dtype
								__UpperCAmelCase      					=      (batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(__a  ,		__a						) and len(__a						) != batch_size:
									raise ValueError(
									    f"""You have passed a list of generators of length {len(__a						)}, but requested an effective batch"""
									    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

								__UpperCAmelCase      					=      randn_tensor(__a  ,		generator=__a  ,		device=self.device  ,		dtype=__a						)

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

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

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

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

								__UpperCAmelCase      					=      audio[:, :, :original_sample_size]

								if not return_dict:
									return (audio,)

								return AudioPipelineOutput(audios=__a						)
