
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		["""image_processor""", """tokenizer"""]
  a__           =		"""LayoutLMv3ImageProcessor"""
  a__           =		("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")
  def __init__( self			: Dict			,							UpperCamelCase__			: Tuple=None			,							UpperCamelCase__			: List[Any]=None			,							**UpperCamelCase__			: List[Any]    )			->	List[str]:







    """simple docstring"""






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

    __magic_name__				     =       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__(UpperCamelCase__			,							UpperCamelCase__    )
  def __call__( self			: Tuple			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,							UpperCamelCase__			: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,							UpperCamelCase__			: Union[List[List[int]], List[List[List[int]]]] = None			,							UpperCamelCase__			: Optional[Union[List[int], List[List[int]]]] = None			,							UpperCamelCase__			: bool = True			,							UpperCamelCase__			: Union[bool, str, PaddingStrategy] = False			,							UpperCamelCase__			: Union[bool, str, TruncationStrategy] = None			,							UpperCamelCase__			: Optional[int] = None			,							UpperCamelCase__			: int = 0			,							UpperCamelCase__			: Optional[int] = None			,							UpperCamelCase__			: Optional[bool] = None			,							UpperCamelCase__			: Optional[bool] = None			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = False			,							UpperCamelCase__			: bool = True			,							UpperCamelCase__			: Optional[Union[str, TensorType]] = None			,							**UpperCamelCase__			: List[Any]			,							)			->	BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""    )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""    )

    # first, apply the image processor
    __magic_name__				     =       self.image_processor(images=UpperCamelCase__			,							return_tensors=UpperCamelCase__    )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__			,							UpperCamelCase__    ):
        __magic_name__				     =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__				     =       features["""words"""]

    __magic_name__				     =       self.tokenizer(
        text=text if text is not None else features["""words"""]			,							text_pair=text_pair if text_pair is not None else None			,							boxes=boxes if boxes is not None else features["""boxes"""]			,							word_labels=UpperCamelCase__			,							add_special_tokens=UpperCamelCase__			,							padding=UpperCamelCase__			,							truncation=UpperCamelCase__			,							max_length=UpperCamelCase__			,							stride=UpperCamelCase__			,							pad_to_multiple_of=UpperCamelCase__			,							return_token_type_ids=UpperCamelCase__			,							return_attention_mask=UpperCamelCase__			,							return_overflowing_tokens=UpperCamelCase__			,							return_special_tokens_mask=UpperCamelCase__			,							return_offsets_mapping=UpperCamelCase__			,							return_length=UpperCamelCase__			,							verbose=UpperCamelCase__			,							return_tensors=UpperCamelCase__			,							**UpperCamelCase__			,							)

    # add pixel values
    __magic_name__				     =       features.pop("""pixel_values"""    )
    if return_overflowing_tokens is True:
      __magic_name__				     =       self.get_overflowing_images(UpperCamelCase__			,							encoded_inputs["""overflow_to_sample_mapping"""]    )
    __magic_name__				     =       images

    return encoded_inputs
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: str    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       []
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]    )

    if len(UpperCamelCase__    ) != len(UpperCamelCase__    ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__    )} and {len(UpperCamelCase__    )}'''    )

    return images_with_overflow
  def 							_lowercase							( self			: Optional[Any]			,							*UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Dict    )			->	Tuple:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__			,							**UpperCamelCase__    )
  def 							_lowercase							( self			: List[str]			,							*UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: Any    )			->	List[str]:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__			,							**UpperCamelCase__    )
  @property
  def 							_lowercase							( self			: Any    )			->	Union[str, Any]:







    """simple docstring"""






    return ["input_ids", "bbox", "attention_mask", "pixel_values"]
  @property
  def 							_lowercase							( self			: str    )			->	Tuple:







    """simple docstring"""






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





  @property
  def 							_lowercase							( self			: Dict    )			->	Tuple:







    """simple docstring"""






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



from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

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

