
'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__lowercase			(					_lowercase      ):
			lowerCamelCase     :						str		 =     "AutoFeatureExtractor"
			lowerCamelCase     :						str		 =     "Speech2Text2Tokenizer"

			def __init__(self     ,				A     ,				A ):
								super().__init__(A     ,				A )
								lowerCamelCase_   : Dict       =						self.feature_extractor
								lowerCamelCase_   : str       =						False

			def __call__(self     ,				*A     ,				**A ):
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*A     ,				**A )

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

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

			def     UpperCAmelCase__	(self     ,				*A     ,				**A ):
								return self.tokenizer.batch_decode(*A     ,				**A )

			def     UpperCAmelCase__	(self     ,				*A     ,				**A ):
								return self.tokenizer.decode(*A     ,				**A )






			@contextmanager
			def     UpperCAmelCase__	(self ):
								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.''' )
								lowerCamelCase_   : Tuple       =						True
								lowerCamelCase_   : Optional[Any]       =						self.tokenizer
								yield
								lowerCamelCase_   : Dict       =						self.feature_extractor
								lowerCamelCase_   : int       =						False




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


