from typing import List, Optional, Tuple, Union

import torch

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


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)  # pylint: disable=invalid-name


class 						a__							(       __snake_case		):



   def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[Any]:
        super().__init__()
        self.register_modules(unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )







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

        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   )





from __future__ import annotations

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					return np.maximum(0				,    __lowerCamelCase						)


if __name__ == "__main__":
	print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]




