


'''simple docstring'''
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'Wav2Vec2FeatureExtractor'
		_a									=       'AutoTokenizer'




		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_					,     UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				self.feature_extractor
							UpperCamelCase__     :str							      =				False




		@classmethod
		def 					lowerCAmelCase__       (	cls					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							try:
												return super().from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)
							except OSError:
												warnings.warn(
												    F'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
												    ''' include a `tokenizer_class` attribute is deprecated and will be '''
												    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
												    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
												    '''file to suppress this warning: '''					,     UpperCamelCase_					,     )

												UpperCamelCase__     :Optional[int]							      =				WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)
												UpperCamelCase__     :Optional[Any]							      =				WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase_					,     **UpperCamelCase_							)

												return cls(feature_extractor=UpperCamelCase_					,     tokenizer=UpperCamelCase_							)




		def __call__(	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor(*UpperCamelCase_					,     **UpperCamelCase_							)

							if "raw_speech" in kwargs:
												warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''							)
												UpperCamelCase__     :Any							      =				kwargs.pop('''raw_speech'''							)
							else:
												UpperCamelCase__     :Any							      =				kwargs.pop('''audio'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				kwargs.pop('''sampling_rate'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				kwargs.pop('''text'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :List[Any]							      =				args[0]
												UpperCamelCase__     :Tuple							      =				args[1:]

							if audio is None and text is None:
												raise ValueError('''You need to specify either an `audio` or `text` input to process.'''							)

							if audio is not None:
												UpperCamelCase__     :List[str]							      =				self.feature_extractor(UpperCamelCase_					,     *UpperCamelCase_					,     sampling_rate=UpperCamelCase_					,     **UpperCamelCase_							)
							if text is not None:
												UpperCamelCase__     :List[Any]							      =				self.tokenizer(UpperCamelCase_					,     **UpperCamelCase_							)

							if text is None:
												return inputs
							elif audio is None:
												return encodings
							else:
												UpperCamelCase__     :int							      =				encodings['''input_ids''']
												return inputs




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							if self._in_target_context_manager:
												return self.current_processor.pad(*UpperCamelCase_					,     **UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				kwargs.pop('''input_features'''					,     UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				kwargs.pop('''labels'''					,     UpperCamelCase_							)
							if len(UpperCamelCase_							) > 0:
												UpperCamelCase__     :Any							      =				args[0]
												UpperCamelCase__     :Union[str, Any]							      =				args[1:]

							if input_features is not None:
												UpperCamelCase__     :str							      =				self.feature_extractor.pad(UpperCamelCase_					,     *UpperCamelCase_					,     **UpperCamelCase_							)
							if labels is not None:
												UpperCamelCase__     :Dict							      =				self.tokenizer.pad(UpperCamelCase_					,     **UpperCamelCase_							)

							if labels is None:
												return input_features
							elif input_features is None:
												return labels
							else:
												UpperCamelCase__     :Union[str, Any]							      =				labels['''input_ids''']
												return input_features




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_					,     **UpperCamelCase_							)




		@contextmanager
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your audio inputs, or in a separate call.'''							)
							UpperCamelCase__     :Optional[int]							      =				True
							UpperCamelCase__     :Dict							      =				self.tokenizer
							yield
							UpperCamelCase__     :Union[str, Any]							      =				self.feature_extractor
							UpperCamelCase__     :int							      =				False




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=None							):

							'''simple docstring'''


							if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps