
"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


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

class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										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]:




										'''simple docstring'''

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

										__lowercase       =       audio_length_in_s * self.unet.config.sample_rate

										__lowercase       =       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}.'''       )

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

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

										__lowercase       =       randn_tensor(_lowerCamelCase  ,    generator=_lowerCamelCase  ,    device=self.device  ,    dtype=_lowerCamelCase       )

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

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

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

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

										__lowercase       =       audio[:, :, :original_sample_size]

										if not return_dict:
													return (audio,)

										return AudioPipelineOutput(audios=_lowerCamelCase       )







"""simple docstring"""






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


_lowercase							=				get_logger(__name__)

class 					__a     ( enum.Enum       ):



							'''simple docstring'''







							_lowerCamelCase					:			Tuple          =   """all_checks"""
							_lowerCamelCase					:			Optional[int]          =   """basic_checks"""
							_lowerCamelCase					:			str          =   """no_checks"""





class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__			,   __magic_name__=None      )						->Union[str, Any]:
			if expected_checksums is None:
						logger.info("Unable to verify checksums."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreDownloadedFiles(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedDownloadedFile(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
			__lowercase       =       " for " + verification_name if verification_name is not None else ""
			if len(__magic_name__      ) > 0:
						raise NonMatchingChecksumError(
						    F'''Checksums didn\'t match{for_verification_name}:\n'''
						    F'''{bad_urls}\n'''
						    "Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"      )
			logger.info("All the checksums matched successfully" + for_verification_name      )

class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












class 					__a     ( __a       ):



							'''simple docstring'''












def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Any:
			if expected_splits is None:
						logger.info("Unable to verify splits sizes."      )
						return
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise ExpectedMoreSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			if len(set(__magic_name__      ) - set(__magic_name__      )      ) > 0:
						raise UnexpectedSplits(str(set(__magic_name__      ) - set(__magic_name__      )      )      )
			__lowercase       =       [
			    {"expected": expected_splits[name], "recorded": recorded_splits[name]}
			    for name in expected_splits
			    if expected_splits[name].num_examples != recorded_splits[name].num_examples
			]
			if len(__magic_name__      ) > 0:
						raise NonMatchingSplitsSizesError(str(__magic_name__      )      )
			logger.info("All the splits matched successfully."      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__ = True      )						->dict:
			if record_checksum:
						__lowercase       =       shaaaa()
						with open(__magic_name__			,   "rb"      ) as f:
									for chunk in iter(lambda: f.read(1 << 2_0      )			,   b""      ):
												m.update(__magic_name__      )
									__lowercase       =       m.hexdigest()
			else:
						__lowercase       =       None
			return {"num_bytes": os.path.getsize(__magic_name__      ), "checksum": checksum}







def      lowerCAmelCase__			(		__magic_name__      )						->List[str]:
			if dataset_size and config.IN_MEMORY_MAX_SIZE:
						return dataset_size < config.IN_MEMORY_MAX_SIZE
			else:
						return False





