


'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

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


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




class      lowerCamelCase_  (      __a ):






      def __init__(   self							:       Dict		,		_A							:       List[str]		,		_A							:       int					):


          '''simple docstring'''


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






      @torch.no_grad()
      def __call__(   self							:       List[Any]		,		_A							:       int = 1		,		_A							:       int = 100		,		_A							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None		,		_A							:       Optional[float] = None		,		_A							:       bool = True		,		):


          '''simple docstring'''


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

          UpperCAmelCase__     :						Union[str, Any]						=							audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__     :						List[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__     :						List[Any]						=							int(_A					)
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__     :						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.'''					)
          UpperCAmelCase__     :						Dict						=							int(_A					)

          UpperCAmelCase__     :						Optional[Any]						=							next(iter(self.unet.parameters()					)					).dtype
          UpperCAmelCase__     :						int						=							(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__     :						Optional[int]						=							randn_tensor(_A		,		generator=_A		,		device=self.device		,		dtype=_A					)

          # set step values
          self.scheduler.set_timesteps(_A		,		device=audio.device					)
          UpperCAmelCase__     :						List[str]						=							self.scheduler.timesteps.to(_A					)

          for t in self.progress_bar(self.scheduler.timesteps					):
              # 1. predict noise model_output
              UpperCAmelCase__     :						Optional[int]						=							self.unet(_A		,		_A					).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__     :						List[Any]						=							self.scheduler.step(_A		,		_A		,		_A					).prev_sample

          UpperCAmelCase__     :						Any						=							audio.clamp(-1		,		1					).float().cpu().numpy()

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

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A					)


'''simple docstring'''




import fire

from utils import calculate_rouge, save_json







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__=None						,							**snake_case__		):




    '''simple docstring'''



    A			:  Optional[Any]   =		[x.strip() for x in open(snake_case__		).readlines()]
    A			:  Tuple   =		[x.strip() for x in open(snake_case__		).readlines()][: len(snake_case__		)]
    A			:  Union[str, Any]   =		calculate_rouge(snake_case__						,							snake_case__						,							**snake_case__		)
    if save_path is not None:
        save_json(snake_case__						,							snake_case__						,							indent=snake_case__		)
    return metrics  # these print nicely


if __name__ == "__main__":
      fire.Fire(calculate_rouge_path)




