


from typing import List, Optional, Tuple, Union

import torch

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


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



class 		snake_case_(       a__			):






     def __init__(	self  :					Dict					,				UpperCamelCase_  :					Optional[Any]					,				UpperCamelCase_  :					Union[str, Any]				):
           super().__init__()
           self.register_modules(unet=UpperCamelCase_					,				scheduler=UpperCamelCase_				)






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

           if audio_length_in_s is None:
                 lowerCAmelCase					:	List[str]       =   self.unet.config.sample_size / self.unet.config.sample_rate

           lowerCAmelCase					:	int       =   audio_length_in_s * self.unet.config.sample_rate

           lowerCAmelCase					:	Optional[int]       =   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					:	List[Any]       =   int(UpperCamelCase_				)
           if sample_size % down_scale_factor != 0:
                 lowerCAmelCase					:	List[str]       =   (
                     (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					:	Optional[int]       =   int(UpperCamelCase_				)

           lowerCAmelCase					:	str       =   next(iter(self.unet.parameters()				)				).dtype
           lowerCAmelCase					:	Optional[Any]       =   (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.'''				)

           lowerCAmelCase					:	int       =   randn_tensor(UpperCamelCase_					,				generator=UpperCamelCase_					,				device=self.device					,				dtype=UpperCamelCase_				)

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

           for t in self.progress_bar(self.scheduler.timesteps				):
                 # 1. predict noise model_output
                 lowerCAmelCase					:	Optional[Any]       =   self.unet(UpperCamelCase_					,				UpperCamelCase_				).sample

                 # 2. compute previous image: x_t -> t_t-1
                 lowerCAmelCase					:	Optional[Any]       =   self.scheduler.step(UpperCamelCase_					,				UpperCamelCase_					,				UpperCamelCase_				).prev_sample

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

           lowerCAmelCase					:	Dict       =   audio[:, :, :original_sample_size]

           if not return_dict:
                 return (audio,)

           return AudioPipelineOutput(audios=UpperCamelCase_				)






"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


snake_case__		:     Optional[Any]    				=				namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			List[str]			):
      return (abs(source - target			) / target) < 0.01






@pytest.mark.integration
def      _snake_case						(						_snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   _TestCommandArgs(dataset=_snake_case ,   all_configs=_snake_case ,   save_infos=_snake_case			)
      lowerCAmelCase					:	str       =   TestCommand(*_snake_case			)
      test_command.run()
      lowerCAmelCase					:	str       =   os.path.join(_snake_case ,   '''README.md'''			)
      assert os.path.exists(_snake_case			)
      lowerCAmelCase					:	Tuple       =   DatasetInfosDict.from_directory(_snake_case			)
      lowerCAmelCase					:	List[str]       =   DatasetInfosDict(
          {
              '''default''': DatasetInfo(
                  features=Features(
                      {
                          '''tokens''': Sequence(Value('''string'''			)			),
                          '''ner_tags''': Sequence(
                              ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']			)			),
                          '''langs''': Sequence(Value('''string'''			)			),
                          '''spans''': Sequence(Value('''string'''			)			),
                      }			) ,   splits=[
                      {
                          '''name''': '''train''',
                          '''num_bytes''': 2351563,
                          '''num_examples''': 10000,
                      },
                      {
                          '''name''': '''validation''',
                          '''num_bytes''': 238418,
                          '''num_examples''': 1000,
                      },
                  ] ,   download_size=3940680 ,   dataset_size=2589981 ,   )
          }			)
      assert dataset_infos.keys() == expected_dataset_infos.keys()
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            lowerCAmelCase,      lowerCAmelCase					:	Union[str, Any]       =   getattr(dataset_infos['''default'''] ,   _snake_case			), getattr(expected_dataset_infos['''default'''] ,   _snake_case			)
            if key == "num_bytes":
                  assert is_apercent_close(_snake_case ,   _snake_case			)
            elif key == "splits":
                  assert list(_snake_case			) == list(_snake_case			)
                  for split in result:
                        assert result[split].name == expected[split].name
                        assert result[split].num_examples == expected[split].num_examples
                        assert is_apercent_close(result[split].num_bytes ,   expected[split].num_bytes			)
            else:
                  result == expected


