




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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[str]    =					"LayoutLMv2ImageProcessor"
      lowerCAmelCase__			: List[Any]    =					("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")







      def __init__(     self					: Optional[int] ,       snake_case					: Dict=None ,       snake_case					: Dict=None ,       **snake_case					: Any				):
          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 ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						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__(snake_case ,       snake_case				)







      def __call__(     self					: List[str] ,       snake_case					: Dict ,       snake_case					: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,       snake_case					: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None ,       snake_case					: Union[List[List[int]], List[List[List[int]]]] = None ,       snake_case					: Optional[Union[List[int], List[List[int]]]] = None ,       snake_case					: bool = True ,       snake_case					: Union[bool, str, PaddingStrategy] = False ,       snake_case					: Union[bool, str, TruncationStrategy] = None ,       snake_case					: Optional[int] = None ,       snake_case					: int = 0 ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[bool] = None ,       snake_case					: Optional[bool] = None ,       snake_case					: bool = False ,       snake_case					: bool = False ,       snake_case					: bool = False ,       snake_case					: bool = False ,       snake_case					: bool = True ,       snake_case					: Optional[Union[str, TensorType]] = None ,       **snake_case					: Any ,       ):
          # verify input
          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
          __UpperCamelCase  		=						self.image_processor(images=snake_case ,       return_tensors=snake_case				)

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

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

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

          return encoded_inputs







      def       snake_case	(     self					: Any ,       snake_case					: Dict ,       snake_case					: Union[str, Any]				):
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          __UpperCamelCase  		=						[]
          for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]				)

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

          return images_with_overflow







      def       snake_case	(     self					: Tuple ,       *snake_case					: Any ,       **snake_case					: str				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Any ,       *snake_case					: Dict ,       **snake_case					: str				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Tuple				):
          return ["input_ids", "bbox", "attention_mask", "image"]







      @property
      def       snake_case	(     self					: List[Any]				):
          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       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor












from abc import ABC, abstractmethod
from argparse import ArgumentParser

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @staticmethod
      @abstractmethod
      def       snake_case	(     snake_case					: ArgumentParser				):
          raise NotImplementedError()







      @abstractmethod
      def       snake_case	(     self					: Optional[Any]				):
          raise NotImplementedError()






