






"""simple docstring"""





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 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							["""image_processor""", """tokenizer"""]
   UpperCamelCase__     	=							"""LayoutLMv2ImageProcessor"""
   UpperCamelCase__     	=							("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")





   def __init__(       self					, UpperCAmelCase_=None					, UpperCAmelCase_=None					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							Dict         =     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_					, )
                 lowercase__:							int         =     kwargs.pop("feature_extractor")

          lowercase__:							Union[str, Any]         =     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					, UpperCAmelCase_					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = True					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = None					, UpperCAmelCase_ = 0					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = None					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = False					, UpperCAmelCase_ = True					, UpperCAmelCase_ = None					, **UpperCAmelCase_					, )			->   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.")

          if return_overflowing_tokens is True and return_offsets_mapping is False:
                 raise ValueError("You cannot return overflowing tokens without returning the offsets mapping.")

          # first, apply the image processor
          lowercase__:							Optional[int]         =     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_):
                        lowercase__:							Dict         =     [text]  # add batch dimension (as the image processor always adds a batch dimension)
                 lowercase__:							int         =     features["words"]

          lowercase__:							Union[str, Any]         =     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
          lowercase__:							List[str]         =     features.pop("pixel_values")
          if return_overflowing_tokens is True:
                 lowercase__:							Dict         =     self.get_overflowing_images(UpperCAmelCase_					, encoded_inputs["overflow_to_sample_mapping"])
          lowercase__:							Optional[Any]         =     images

          return encoded_inputs





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




          lowercase__:							str         =     []
          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					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   str:




          '''simple docstring'''




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





   def __lowercase							(       self					, *UpperCAmelCase_					, **UpperCAmelCase_)			->   int:




          '''simple docstring'''




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





   @property
   def __lowercase							(       self)			->   Optional[Any]:




          '''simple docstring'''




          return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]





   @property
   def __lowercase							(       self)			->   str:




          '''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)			->   List[Any]:




          '''simple docstring'''




          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					, UpperCAmelCase_					, )
          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					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

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






