
"""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 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__()
          self.register_modules(unet=UpperCamelCase_     ,	scheduler=UpperCamelCase_      )



      @torch.no_grad()
      def __call__(   self     ,	UpperCamelCase_ = 1     ,	UpperCamelCase_ = 100     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	):

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

          UpperCAmelCase__	:      Any     							=					audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__	:      str     							=					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__	:      str     							=					int(UpperCamelCase_      )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__	:      Union[str, 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__	:      Optional[int]     							=					int(UpperCamelCase_      )

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

          UpperCAmelCase__	:      str     							=					randn_tensor(UpperCamelCase_     ,	generator=UpperCamelCase_     ,	device=self.device     ,	dtype=UpperCamelCase_      )

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

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

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

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

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

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=UpperCamelCase_      )



"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

