


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  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							['image_processor', 'tokenizer']
  _SCREAMING_SNAKE_CASE		     =							'LayoutLMv2ImageProcessor'
  _SCREAMING_SNAKE_CASE		     =							('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')



  def __init__(  self							:							int     ,					_lowerCAmelCase							:							List[Any]=None     ,					_lowerCAmelCase							:							List[str]=None     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			Any:




     """simple docstring"""
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."     ,					_lowerCAmelCase     ,					)
        snake_case_            =       kwargs.pop("feature_extractor"	)

     snake_case_            =       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							:							Optional[int]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_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							:							Any     ,					) ->			BatchEncoding:




     """simple docstring"""
     # 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
     snake_case_            =       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	):
           snake_case_            =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
        snake_case_            =       features["words"]

     snake_case_            =       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
     snake_case_            =       features.pop("pixel_values"	)
     if return_overflowing_tokens is True:
        snake_case_            =       self.get_overflowing_images(_lowerCAmelCase     ,					encoded_inputs["overflow_to_sample_mapping"]	)
     snake_case_            =       images

     return encoded_inputs



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Dict     ,					_lowerCAmelCase							:							List[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
     snake_case_            =       []
     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        lowerCAmelCase__	(  self							:							Optional[Any]     ,					*_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			List[str]:




     """simple docstring"""
     return self.tokenizer.batch_decode(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					*_lowerCAmelCase							:							Dict     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			Optional[Any]:




     """simple docstring"""
     return self.tokenizer.decode(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



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




     """simple docstring"""
     return ["input_ids", "bbox", "attention_mask", "image"]



  @property
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




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








from __future__ import annotations



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->list[str]:
   '''simple docstring'''






   if partitions <= 0:
      raise ValueError("partitions must be a positive number!"				)
   if partitions > number_of_bytes:
      raise ValueError("partitions can not > number_of_bytes!"				)
   snake_case_            =       number_of_bytes // partitions
   snake_case_            =       []
   for i in range(lowerCAmelCase_				):
      snake_case_            =       i * bytes_per_partition + 1
      snake_case_            =       (
          number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
      )
      allocation_list.append(F'''{start_bytes}-{end_bytes}'''				)
   return allocation_list


if __name__ == "__main__":
   import doctest

   doctest.testmod()




