






'''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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "tokenizer"]
    lowerCAmelCase__      				=							"LayoutLMv3ImageProcessor"
    lowerCAmelCase__      				=							("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")

    def __init__(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]=None				,     __lowerCamelCase     :     Tuple=None				,     **__lowerCamelCase     :     List[Any]     )							->							Optional[Any]:

     '''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."				,     __lowerCamelCase				,     )
      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__(__lowerCamelCase				,     __lowerCamelCase     )

    def __call__(					self     :     Tuple				,     __lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,     __lowerCamelCase     :     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,     __lowerCamelCase     :     Union[List[List[int]], List[List[List[int]]]] = None				,     __lowerCamelCase     :     Optional[Union[List[int], List[List[int]]]] = None				,     __lowerCamelCase     :     bool = True				,     __lowerCamelCase     :     Union[bool, str, PaddingStrategy] = False				,     __lowerCamelCase     :     Union[bool, str, TruncationStrategy] = None				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     int = 0				,     __lowerCamelCase     :     Optional[int] = None				,     __lowerCamelCase     :     Optional[bool] = None				,     __lowerCamelCase     :     Optional[bool] = None				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = False				,     __lowerCamelCase     :     bool = True				,     __lowerCamelCase     :     Optional[Union[str, TensorType]] = None				,     **__lowerCamelCase     :     Dict				,     )							->							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
     lowerCamelCase__  =     self.image_processor(images=__lowerCamelCase				,     return_tensors=__lowerCamelCase     )

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

     lowerCamelCase__  =     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=__lowerCamelCase				,     add_special_tokens=__lowerCamelCase				,     padding=__lowerCamelCase				,     truncation=__lowerCamelCase				,     max_length=__lowerCamelCase				,     stride=__lowerCamelCase				,     pad_to_multiple_of=__lowerCamelCase				,     return_token_type_ids=__lowerCamelCase				,     return_attention_mask=__lowerCamelCase				,     return_overflowing_tokens=__lowerCamelCase				,     return_special_tokens_mask=__lowerCamelCase				,     return_offsets_mapping=__lowerCamelCase				,     return_length=__lowerCamelCase				,     verbose=__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase				,     )

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

     return encoded_inputs

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     Union[str, Any]     )							->							Any:

     '''simple docstring'''




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

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

     return images_with_overflow

    def    a__				(					self     :     List[Any]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Union[str, Any]     )							->							List[Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[int]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




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

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

     '''simple docstring'''




     return ["input_ids", "bbox", "attention_mask", "pixel_values"]

    @property
    def    a__				(					self     :     List[str]     )							->							List[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    a__				(					self     :     Any     )							->							Dict:

     '''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 ..utils import DummyObject, requires_backends
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     List[str]				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Dict     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     str				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     str				,     *__lowerCamelCase     :     List[Any]				,     **__lowerCamelCase     :     int     )							->							List[str]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Dict				,     *__lowerCamelCase     :     Any				,     **__lowerCamelCase     :     str     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Dict     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     Dict				,     *__lowerCamelCase     :     str				,     **__lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Optional[int]				,     **__lowerCamelCase     :     Optional[Any]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     Optional[int]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )
class 				lowercase       (							metaclass=_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["flax", "transformers"]

    def __init__(					self     :     int				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     requires_backends(self				,     ["flax", "transformers"]     )

    @classmethod
    def    a__				(					cls     :     Any				,     *__lowerCamelCase     :     Tuple				,     **__lowerCamelCase     :     str     )							->							List[str]:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )




    @classmethod
    def    a__				(					cls     :     List[Any]				,     *__lowerCamelCase     :     List[str]				,     **__lowerCamelCase     :     List[Any]     )							->							int:

     '''simple docstring'''




     requires_backends(cls				,     ["flax", "transformers"]     )

