





'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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


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

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     super().__init__()
     self.register_modules(unet=_lowerCAmelCase      ,  scheduler=_lowerCAmelCase		)





 @torch.no_grad()
 def __call__(						self      ,  _lowerCAmelCase = 1      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = True      ,  )     ->				Union[AudioPipelineOutput, Tuple]:

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

     _lowerCAmelCase						      =   audio_length_in_s * self.unet.config.sample_rate

     _lowerCAmelCase						      =   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}.'''		)

     _lowerCAmelCase						      =   int(_lowerCAmelCase		)
     if sample_size % down_scale_factor != 0:
         _lowerCAmelCase						      =   (
             (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."		)
     _lowerCAmelCase						      =   int(_lowerCAmelCase		)

     _lowerCAmelCase						      =   next(iter(self.unet.parameters()		)		).dtype
     _lowerCAmelCase						      =   (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.'''		)

     _lowerCAmelCase						      =   randn_tensor(_lowerCAmelCase      ,  generator=_lowerCAmelCase      ,  device=self.device      ,  dtype=_lowerCAmelCase		)

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

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

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

     _lowerCAmelCase						      =   audio.clamp(-1      ,  1		).float().cpu().numpy()

     _lowerCAmelCase						      =   audio[:, :, :original_sample_size]

     if not return_dict:
         return (audio,)

     return AudioPipelineOutput(audios=_lowerCAmelCase		)










"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 							snake_case (  A__		):
			def wrapper(*A__      ,**A__		):
						UpperCAmelCase_	:					Union[str, Any]     	=   timeit.default_timer()
						UpperCAmelCase_	:					Union[str, Any]     	=   func(*A__      ,**A__		)
						UpperCAmelCase_	:					Dict     	=   timeit.default_timer() - starttime
						return delta

			UpperCAmelCase_	:					Optional[Any]     	=   func.__name__

			return wrapper

def 							snake_case (  A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					str     	=   []
			UpperCAmelCase_	:					List[str]     	=   seq_shapes or {}
			for i in range(A__		):
						UpperCAmelCase_	:					int     	=   {}
						for col_id, (k, v) in enumerate(features.items()		):
									if isinstance(A__      ,_ArrayXD		):
												UpperCAmelCase_	:					Optional[int]     	=   np.random.rand(*v.shape		).astype(v.dtype		)
									elif isinstance(A__      ,datasets.Value		):
												if v.dtype == "string":
															UpperCAmelCase_	:					List[str]     	=   "The small grey turtle was surprisingly fast when challenged."
												else:
															UpperCAmelCase_	:					List[str]     	=   np.random.randint(10      ,size=1		).astype(v.dtype		).item()
									elif isinstance(A__      ,datasets.Sequence		):
												while isinstance(A__      ,datasets.Sequence		):
															UpperCAmelCase_	:					List[str]     	=   v.feature
												UpperCAmelCase_	:					List[Any]     	=   seq_shapes[k]
												UpperCAmelCase_	:					Dict     	=   np.random.rand(*A__		).astype(v.dtype		)
									UpperCAmelCase_	:					Dict     	=   data

						dummy_data.append((i, example)		)

			return dummy_data




def 							snake_case (  A__      ,A__      ,A__=1_00      ,A__=None		):
			UpperCAmelCase_	:					Optional[Any]     	=   generate_examples(A__      ,num_examples=A__      ,seq_shapes=A__		)

			with ArrowWriter(features=A__      ,path=A__		) as writer:
						for key, record in dummy_data:
									UpperCAmelCase_	:					Any     	=   features.encode_example(A__		)
									writer.write(A__		)

						UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   writer.finalize()

			if not num_final_examples == num_examples:
						raise ValueError(
						    F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""		)

			UpperCAmelCase_	:					List[Any]     	=   datasets.Dataset.from_file(filename=A__      ,info=datasets.DatasetInfo(features=A__		)		)

			return dataset



