






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  __UpperCAmelCase						(SCREAMING_SNAKE_CASE__       ):
			__snake_case							: Union[str, Any]							=				["image_processor", "tokenizer"]
			__snake_case							: List[str]							=				"AutoImageProcessor"
			__snake_case							: List[str]							=				"AutoTokenizer"







			def __init__(      self:    List[Any] ,      UpperCAmelCase_:    List[str]=None ,      UpperCAmelCase_:    int=None ,      **UpperCAmelCase_:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead.""" ,      lowerCAmelCase__ ,      )
															_SCREAMING_SNAKE_CASE       =    kwargs.pop("""feature_extractor"""	)

									_SCREAMING_SNAKE_CASE       =    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__(lowerCAmelCase__ ,      lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    self.image_processor
									_SCREAMING_SNAKE_CASE       =    False







			def __call__(      self:    Optional[int] ,      *UpperCAmelCase_:    Dict ,      **UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




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

									_SCREAMING_SNAKE_CASE       =    kwargs.pop("""images""" ,      lowerCAmelCase__	)
									_SCREAMING_SNAKE_CASE       =    kwargs.pop("""text""" ,      lowerCAmelCase__	)
									if len(lowerCAmelCase__	) > 0:
															_SCREAMING_SNAKE_CASE       =    args[0]
															_SCREAMING_SNAKE_CASE       =    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:
															_SCREAMING_SNAKE_CASE       =    self.image_processor(lowerCAmelCase__ ,      *lowerCAmelCase__ ,      **lowerCAmelCase__	)
									if text is not None:
															_SCREAMING_SNAKE_CASE       =    self.tokenizer(lowerCAmelCase__ ,      **lowerCAmelCase__	)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															_SCREAMING_SNAKE_CASE       =    encodings["input_ids"]
															return inputs







			def 				UpperCamelCase (      self:    Any ,      *UpperCAmelCase_:    List[Any] ,      **UpperCAmelCase_:    Optional[Any]	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    List[Any] ,      *UpperCAmelCase_:    Any ,      **UpperCAmelCase_:    str	):







									'''simple docstring'''




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







			@contextmanager
			def 				UpperCamelCase (      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."""	)
									_SCREAMING_SNAKE_CASE       =    True
									_SCREAMING_SNAKE_CASE       =    self.tokenizer
									yield
									_SCREAMING_SNAKE_CASE       =    self.image_processor
									_SCREAMING_SNAKE_CASE       =    False







			@property
			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" ,      lowerCAmelCase__ ,      )
									return self.image_processor_class







			@property
			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									warnings.warn(
									    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" ,      lowerCAmelCase__ ,      )
									return self.image_processor







"""simple docstring"""
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,
)


_SCREAMING_SNAKE_CASE						 =     {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5Tokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     ["""T5TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """T5EncoderModel""",
        """T5ForConditionalGeneration""",
        """T5Model""",
        """T5PreTrainedModel""",
        """load_tf_weights_in_t5""",
        """T5ForQuestionAnswering""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFT5EncoderModel""",
        """TFT5ForConditionalGeneration""",
        """TFT5Model""",
        """TFT5PreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE						 =     [
        """FlaxT5EncoderModel""",
        """FlaxT5ForConditionalGeneration""",
        """FlaxT5Model""",
        """FlaxT5PreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta import TaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_ta_fast import TaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_ta import (
            T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TaEncoderModel,
            TaForConditionalGeneration,
            TaForQuestionAnswering,
            TaModel,
            TaPreTrainedModel,
            load_tf_weights_in_ta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_ta import (
            TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFTaEncoderModel,
            TFTaForConditionalGeneration,
            TFTaModel,
            TFTaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_ta import (
            FlaxTaEncoderModel,
            FlaxTaForConditionalGeneration,
            FlaxTaModel,
            FlaxTaPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE						 =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



