code: 
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  
  class 					__lowercase					(       SCREAMING_SNAKE_CASE__     ):
  
  
  
  		"""simple docstring"""
  
  		UpperCamelCase    :				int 					=		["image_processor", "tokenizer"]
  		UpperCamelCase    :				Tuple 					=		"AutoImageProcessor"
  		UpperCamelCase    :				Dict 					=		"AutoTokenizer"
  
  
  
  
  
  
  
  		def __init__(   self		,			A=None		,			A=None		,			**A  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					lowerCamelCase								=     None
  					if "feature_extractor" in kwargs:
  								warnings.warn(
  								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
  								    """ instead."""		,			A		,			)
  								lowerCamelCase								=     kwargs.pop("""feature_extractor"""  )
  
  					lowerCamelCase								=     image_processor if image_processor is not None else feature_extractor
  					if image_processor is None:
  								raise ValueError("""You need to specify an `image_processor`."""  )
  					if tokenizer is None:
  								raise ValueError("""You need to specify a `tokenizer`."""  )
  
  					super().__init__(A		,			A  )
  					lowerCamelCase								=     self.image_processor
  					lowerCamelCase								=     False
  
  
  
  
  
  
  
  		def __call__(   self		,			*A		,			**A  )     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self._in_target_context_manager:
  								return self.current_processor(*A		,			**A  )
  
  					lowerCamelCase								=     kwargs.pop("""images"""		,			A  )
  					lowerCamelCase								=     kwargs.pop("""text"""		,			A  )
  					if len(A  ) > 0:
  								lowerCamelCase								=     args[0]
  								lowerCamelCase								=     args[1:]
  
  					if images is None and text is None:
  								raise ValueError("""You need to specify either an `images` or `text` input to process."""  )
  
  					if images is not None:
  								lowerCamelCase								=     self.image_processor(A		,			*A		,			**A  )
  					if text is not None:
  								lowerCamelCase								=     self.tokenizer(A		,			**A  )
  
  					if text is None:
  								return inputs
  					elif images is None:
  								return encodings
  					else:
  								lowerCamelCase								=     encodings["""input_ids"""]
  								return inputs
  
  
  
  
  
  
  
  		def 				__A      (   self		,			*A		,			**A  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*A		,			**A  )
  
  
  
  
  
  
  
  		def 				__A      (   self		,			*A		,			**A  )     ->   Optional[int]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*A		,			**A  )
  
  
  
  
  
  
  
  		@contextmanager
  		def 				__A      (   self  )     ->   List[str]:
  
  
  
  
  
  
  
  					'''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 images inputs, or in a separate call."""  )
  					lowerCamelCase								=     True
  					lowerCamelCase								=     self.tokenizer
  					yield
  					lowerCamelCase								=     self.image_processor
  					lowerCamelCase								=     False
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					warnings.warn(
  					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""		,			A		,			)
  					return self.image_processor_class
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					warnings.warn(
  					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,			A		,			)
  					return self.image_processor
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase      :						List[str]    =		{
      "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizer"]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						str    =		["BigBirdTokenizerFast"]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "BigBirdForCausalLM",
  		    "BigBirdForMaskedLM",
  		    "BigBirdForMultipleChoice",
  		    "BigBirdForPreTraining",
  		    "BigBirdForQuestionAnswering",
  		    "BigBirdForSequenceClassification",
  		    "BigBirdForTokenClassification",
  		    "BigBirdLayer",
  		    "BigBirdModel",
  		    "BigBirdPreTrainedModel",
  		    "load_tf_weights_in_big_bird",
  		]
  
  try:
  		if not is_flax_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		UpperCAmelCase      :						Dict    =		[
  		    "FlaxBigBirdForCausalLM",
  		    "FlaxBigBirdForMaskedLM",
  		    "FlaxBigBirdForMultipleChoice",
  		    "FlaxBigBirdForPreTraining",
  		    "FlaxBigBirdForQuestionAnswering",
  		    "FlaxBigBirdForSequenceClassification",
  		    "FlaxBigBirdForTokenClassification",
  		    "FlaxBigBirdModel",
  		    "FlaxBigBirdPreTrainedModel",
  		]
  
  if TYPE_CHECKING:
  		from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
  
  		try:
  				if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird import BigBirdTokenizer
  
  		try:
  				if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .tokenization_big_bird_fast import BigBirdTokenizerFast
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_big_bird import (
  				    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    BigBirdForCausalLM,
  				    BigBirdForMaskedLM,
  				    BigBirdForMultipleChoice,
  				    BigBirdForPreTraining,
  				    BigBirdForQuestionAnswering,
  				    BigBirdForSequenceClassification,
  				    BigBirdForTokenClassification,
  				    BigBirdLayer,
  				    BigBirdModel,
  				    BigBirdPreTrainedModel,
  				    load_tf_weights_in_big_bird,
  				)
  
  		try:
  				if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_flax_big_bird import (
  				    FlaxBigBirdForCausalLM,
  				    FlaxBigBirdForMaskedLM,
  				    FlaxBigBirdForMultipleChoice,
  				    FlaxBigBirdForPreTraining,
  				    FlaxBigBirdForQuestionAnswering,
  				    FlaxBigBirdForSequenceClassification,
  				    FlaxBigBirdForTokenClassification,
  				    FlaxBigBirdModel,
  				    FlaxBigBirdPreTrainedModel,
  				)
  
  else:
  		import sys
  
  		UpperCAmelCase      :						Dict    =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  