from typing import List, Optional, Tuple, Union

import torch

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


SCREAMING_SNAKE_CASE			:						Tuple				  =		logging.get_logger(__name__)  # pylint: disable=invalid-name
class  A_			(							UpperCAmelCase__			):

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any							):
   super().__init__()
   self.register_modules(unet=_UpperCAmelCase  ,				scheduler=_UpperCAmelCase							)

  @torch.no_grad()
  def __call__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any] = 1  ,				__SCREAMING_SNAKE_CASE			:   List[str] = 1_00  ,				__SCREAMING_SNAKE_CASE			:   Any = None  ,				__SCREAMING_SNAKE_CASE			:   List[str] = None  ,				__SCREAMING_SNAKE_CASE			:   str = True  ,				):

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

   __a							=	audio_length_in_s * self.unet.config.sample_rate

   __a							=	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							=	int(_UpperCAmelCase							)
   if sample_size % down_scale_factor != 0:
    __a							=	(
        (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							=	int(_UpperCAmelCase							)

   __a							=	next(iter(self.unet.parameters()							)							).dtype
   __a							=	(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."""							)

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

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

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

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

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

   __a							=	audio[:, :, :original_sample_size]

   if not return_dict:
    return (audio,)

   return AudioPipelineOutput(audios=_UpperCAmelCase							)










import re
from pathlib import Path
from unittest import TestCase

import pytest


@pytest.mark.integration
class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'			)
        UpperCamelCase_						=  input_file.read()
        UpperCamelCase_						=  regexp.search(_UpperCAmelCase			)

     return match




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Dict:

     with open(_UpperCAmelCase		,		encoding='utf-8'			) as input_file:
        UpperCamelCase_						=  re.compile(R'#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'		,		re.DOTALL			)
        UpperCamelCase_						=  input_file.read()
        # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
        UpperCamelCase_						=  regexp.finditer(_UpperCAmelCase			)

     UpperCamelCase_						=  [match for match in matches if match is not None and match.group(1			) is not None]
     return matches[0] if matches else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_encoding_on_file_open(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     UpperCamelCase_						=  Path('./datasets'			)
     UpperCamelCase_						=  list(dataset_paths.absolute().glob('**/*.py'			)			)

     for dataset in dataset_files:
        if self._no_print_statements(str(_UpperCAmelCase			)			):
           raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""			)





