from typing import List, Optional, Tuple, Union

import torch

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


__magic_name__    							=							logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					__SCREAMING_SNAKE_CASE       (       UpperCamelCase):






       """simple docstring"""

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







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

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

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

          __snake_case   :			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}."""				)

          __snake_case   :			Optional[int]										=						int(_lowerCAmelCase				)
          if sample_size % down_scale_factor != 0:
             __snake_case   :			List[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.'				)
          __snake_case   :			Optional[int]										=						int(_lowerCAmelCase				)

          __snake_case   :			Optional[int]										=						next(iter(self.unet.parameters()				)				).dtype
          __snake_case   :			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."""				)

          __snake_case   :			Tuple										=						randn_tensor(_lowerCAmelCase			,		generator=_lowerCAmelCase			,		device=self.device			,		dtype=_lowerCAmelCase				)

          # set step values
          self.scheduler.set_timesteps(_lowerCAmelCase			,		device=audio.device				)
          __snake_case   :			Any										=						self.scheduler.timesteps.to(_lowerCAmelCase				)

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

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

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

          __snake_case   :			Dict										=						audio[:, :, :original_sample_size]

          if not return_dict:
             return (audio,)

          return AudioPipelineOutput(audios=_lowerCAmelCase				)










'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				int(input("Enter the power for  P-Series"))
       print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
       print(p_series(nth_term, power))


