






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   str						=					'MCTCTFeatureExtractor'
						A     :   Optional[Any]						=					'AutoTokenizer'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								super().__init__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       self.feature_extractor
								snake_case_				:							List[Any]						 =       False






						def __call__(	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Union[str, Any]:
								# For backward compatibility
								if self._in_target_context_manager:
										return self.current_processor(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								if "raw_speech" in kwargs:
										warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
										snake_case_				:							Any						 =       kwargs.pop("raw_speech"				)
								else:
										snake_case_				:							str						 =       kwargs.pop("audio"		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Dict						 =       kwargs.pop("sampling_rate"		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[Any]						 =       kwargs.pop("text"		,				_SCREAMING_SNAKE_CASE				)
								if len(_SCREAMING_SNAKE_CASE				) > 0:
										snake_case_				:							str						 =       args[0]
										snake_case_				:							Optional[int]						 =       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:
										snake_case_				:							Any						 =       self.feature_extractor(_SCREAMING_SNAKE_CASE		,				*_SCREAMING_SNAKE_CASE		,				sampling_rate=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
								if text is not None:
										snake_case_				:							List[str]						 =       self.tokenizer(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								if text is None:
										return inputs
								elif audio is None:
										return encodings
								else:
										snake_case_				:							Dict						 =       encodings["input_ids"]
										return inputs






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								# For backward compatibility
								if self._in_target_context_manager:
										return self.current_processor.pad(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							str						 =       kwargs.pop("input_features"		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       kwargs.pop("labels"		,				_SCREAMING_SNAKE_CASE				)
								if len(_SCREAMING_SNAKE_CASE				) > 0:
										snake_case_				:							Tuple						 =       args[0]
										snake_case_				:							Optional[Any]						 =       args[1:]

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

								if labels is None:
										return input_features
								elif input_features is None:
										return labels
								else:
										snake_case_				:							int						 =       labels["input_ids"]
										return input_features






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				str:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@contextmanager
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								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."				)
								snake_case_				:							Optional[int]						 =       True
								snake_case_				:							List[str]						 =       self.tokenizer
								yield
								snake_case_				:							str						 =       self.feature_extractor
								snake_case_				:							Tuple						 =       False








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
