



from typing import List, Optional, Tuple, Union

import torch

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


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

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						@torch.no_grad()
						def __call__(       self ,       __lowercase = 1 ,       __lowercase = 1_0_0 ,       __lowercase = None ,       __lowercase = None ,       __lowercase = True ,       ) ->				Union[AudioPipelineOutput, Tuple]:
									"""simple docstring"""




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

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

									a__       :						Tuple							=  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__       :						Union[str, Any]							=  int(__lowercase  )
									if sample_size % down_scale_factor != 0:
												a__       :						Tuple							=  (
												    (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__       :						Dict							=  int(__lowercase  )

									a__       :						List[str]							=  next(iter(self.unet.parameters()  )  ).dtype
									a__       :						Any							=  (batch_size, self.unet.config.in_channels, sample_size)
									if isinstance(__lowercase ,       __lowercase  ) and len(__lowercase  ) != batch_size:
												raise ValueError(
												    F'''You have passed a list of generators of length {len(__lowercase  )}, but requested an effective batch'''
												    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

									a__       :						List[Any]							=  randn_tensor(__lowercase ,       generator=__lowercase ,       device=self.device ,       dtype=__lowercase  )

									# set step values
									self.scheduler.set_timesteps(__lowercase ,       device=audio.device  )
									a__       :						int							=  self.scheduler.timesteps.to(__lowercase  )

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

												# 2. compute previous image: x_t -> t_t-1
												a__       :						Union[str, Any]							=  self.scheduler.step(__lowercase ,       __lowercase ,       __lowercase  ).prev_sample

									a__       :						Tuple							=  audio.clamp(-1 ,       1  ).float().cpu().numpy()

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

									if not return_dict:
												return (audio,)

									return AudioPipelineOutput(audios=__lowercase  )









_lowercase		:				Optional[int]      		=[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
			a__       :						List[Any]							=  0
			a__       :						Dict							=  0
			while place < len(_lowercase):
						if (place + 1 < len(_lowercase)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def      lowerCAmelCase_					(  _lowercase    :       int)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			for arabic, roman in ROMAN:
						((a__)	,						(a__))       :						Optional[Any]							=  divmod(_lowercase						,       _lowercase)
						result.append(roman * factor)
						if number == 0:
									break
			return "".join(_lowercase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




