
"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Union[str, Any]	=       ["""image_processor""", """tokenizer"""]
    __lowercase				:			Dict	=       """AutoImageProcessor"""
    __lowercase				:			int	=       """AutoTokenizer"""
    def __init__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):
      _snake_case  :    int            = None
      if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."    ,     snake_case_    ,     )
        _snake_case  :    Union[str, Any]            = kwargs.pop("feature_extractor"  )

      _snake_case  :    List[str]            = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("You have to specify an image_processor."  )
      if tokenizer is None:
        raise ValueError("You have to specify a tokenizer."  )

      super().__init__(snake_case_    ,     snake_case_  )
      _snake_case  :    List[str]            = self.image_processor
    def __call__(     self    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     **snake_case_  ):

      if text is None and images is None:
        raise ValueError("You have to specify either text or images. Both cannot be none."  )

      if text is not None:
        _snake_case  :    List[str]            = self.tokenizer(snake_case_    ,     return_tensors=snake_case_    ,     **snake_case_  )

      if images is not None:
        _snake_case  :    Optional[int]            = self.image_processor(snake_case_    ,     return_tensors=snake_case_    ,     **snake_case_  )

      if text is not None and images is not None:
        _snake_case  :    Dict            = image_features.pixel_values
        return encoding
      elif text is not None:
        return encoding
      else:
        return BatchEncoding(data=dict(**snake_case_  )    ,     tensor_type=snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.batch_decode(*snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return self.tokenizer.decode(*snake_case_    ,     **snake_case_  )
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = self.tokenizer.model_input_names
      _snake_case  :    int            = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,     snake_case_    ,     )
      return self.image_processor_class


    @property
    def        lowerCamelCase__						(     self  ):
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,     snake_case_    ,     )
      return self.image_processor









"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


