






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						( UpperCAmelCase_ ):
  lowercase_						:			Optional[int]						=					['''image_processor''', '''tokenizer''']
  lowercase_						:			Any						=					'''LayoutLMv3ImageProcessor'''
  lowercase_						:			str						=					('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')

  def __init__(    self						:	str   ,				lowerCamelCase__						:	List[str]=None   ,				lowerCamelCase__						:	Any=None   ,				**lowerCamelCase__						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					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__   ,				)
        __UpperCamelCase	:			Union[str, Any]					=					kwargs.pop("""feature_extractor"""			)

     __UpperCamelCase	:			List[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__(lowerCamelCase__   ,				lowerCamelCase__			)

  def __call__(    self						:	Optional[Any]   ,				lowerCamelCase__						:	List[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__						:	List[str]   ,				):
     """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
     __UpperCamelCase	:			Optional[int]					=					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__			):
           __UpperCamelCase	:			Union[str, Any]					=					[text]  # add batch dimension (as the image processor always adds a batch dimension)
        __UpperCamelCase	:			Tuple					=					features["""words"""]

     __UpperCamelCase	:			Dict					=					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
     __UpperCamelCase	:			Tuple					=					features.pop("""pixel_values"""			)
     if return_overflowing_tokens is True:
        __UpperCamelCase	:			Optional[int]					=					self.get_overflowing_images(lowerCamelCase__   ,				encoded_inputs["""overflow_to_sample_mapping"""]			)
     __UpperCamelCase	:			Any					=					images

     return encoded_inputs

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	List[Any]   ,				lowerCamelCase__						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					[]
     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						:	Any   ,				*lowerCamelCase__						:	Optional[int]   ,				**lowerCamelCase__						:	List[Any]			):
     """simple docstring"""

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

  def    a					(    self						:	Tuple   ,				*lowerCamelCase__						:	Optional[Any]   ,				**lowerCamelCase__						:	Union[str, Any]			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	int			):
     """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						:	Optional[int]			):
     """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							:					str					,							__lowerCAmelCase							:					Tuple					,							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Tuple							)     ->  Optional[Any]:  # noqa: E741
   while r - l > 1:
      __UpperCamelCase	:			int					=					(l + r) // 2
      if v[m] >= key:
         __UpperCamelCase	:			Dict					=					m
      else:
         __UpperCamelCase	:			Optional[Any]					=					m  # noqa: E741
   return r


def 				__lowerCamelCase     (							__lowerCAmelCase							:					list[int]							)     ->  int:
   if len(__lowerCAmelCase							) == 0:
      return 0

   __UpperCamelCase	:			Optional[Any]					=					[0] * len(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					1

   __UpperCamelCase	:			Union[str, Any]					=					v[0]

   for i in range(1					,							len(__lowerCAmelCase							)							):
      if v[i] < tail[0]:
         __UpperCamelCase	:			Dict					=					v[i]
      elif v[i] > tail[length - 1]:
         __UpperCamelCase	:			Optional[int]					=					v[i]
         length += 1
      else:
         __UpperCamelCase	:			List[Any]					=					v[i]

   return length


if __name__ == "__main__":
       import doctest

       doctest.testmod()



