


from typing import List, Optional, Tuple, Union

import torch

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


__UpperCAmelCase    :Optional[int]				       =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			a       (	_UpperCAmelCase ):


     """simple docstring"""






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






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




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

          __UpperCAmelCase			:				Tuple													=    audio_length_in_s * self.unet.config.sample_rate

          __UpperCAmelCase			:				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}.'    )

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

          __UpperCAmelCase			:				List[str]													=    next(iter(self.unet.parameters()    )    ).dtype
          __UpperCAmelCase			:				List[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.'    )

          __UpperCAmelCase			:				Tuple													=    randn_tensor(lowerCamelCase_							,				generator=lowerCamelCase_							,				device=self.device							,				dtype=lowerCamelCase_    )

          # set step values
          self.scheduler.set_timesteps(lowerCamelCase_							,				device=audio.device    )
          __UpperCAmelCase			:				List[Any]													=    self.scheduler.timesteps.to(lowerCamelCase_    )

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

               # 2. compute previous image: x_t -> t_t-1
               __UpperCAmelCase			:				Optional[int]													=    self.scheduler.step(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_    ).prev_sample

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

          __UpperCAmelCase			:				Any													=    audio[:, :, :original_sample_size]

          if not return_dict:
               return (audio,)

          return AudioPipelineOutput(audios=lowerCamelCase_    )



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__UpperCAmelCase    :List[str]				       =     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__UpperCAmelCase    :List[Any]				       =     "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''float'''    ),
                      '''references''': datasets.Value('''float'''    ),
                  }    )							,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}