from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class        a_				:






 '''simple docstring'''






 UpperCAmelCase_							=      ('_front', '_back', '_len')
 @dataclass
 class        a_				:






  '''simple docstring'''






  UpperCAmelCase_							=      None
  UpperCAmelCase_							=      None
  UpperCAmelCase_							=      None






 class        a_				:






  '''simple docstring'''






  UpperCAmelCase_							=      ('_cur',)




  def __init__(    self      :						str    ,					lowercase__      :						Deque._Node | None):







         '''simple docstring'''



         lowerCAmelCase__  			=      cur




  def __iter__(    self      :						List[str]):







         '''simple docstring'''



         return self




  def 	__snake_case		(    self      :						Union[str, Any]):







         '''simple docstring'''



         if self._cur is None:
                # finished iterating
                raise StopIteration
         lowerCAmelCase__  			=      self._cur.val
         lowerCAmelCase__  			=      self._cur.next_node

         return val






 def __init__(    self      :						Dict    ,					lowercase__      :						Iterable[Any] | None = None):







        '''simple docstring'''



        lowerCAmelCase__  			=      None
        lowerCAmelCase__  			=      None
        lowerCAmelCase__  			=      0

        if iterable is not None:
               # append every value to the deque
               for val in iterable:
                      self.append(lowercase__)




 def 	__snake_case		(    self      :						int    ,					lowercase__      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      self._Node(lowercase__    ,					lowercase__    ,					lowercase__)
        if self.is_empty():
               # front = back
               lowerCAmelCase__  			=      lowerCAmelCase__  			=      node
               lowerCAmelCase__  			=      1
        else:
               # connect nodes
               lowerCAmelCase__  			=      node
               lowerCAmelCase__  			=      self._back
               lowerCAmelCase__  			=      node  # assign new back to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."




 def 	__snake_case		(    self      :						Dict    ,					lowercase__      :						Any):







        '''simple docstring'''



        lowerCAmelCase__  			=      self._Node(lowercase__    ,					lowercase__    ,					lowercase__)
        if self.is_empty():
               # front = back
               lowerCAmelCase__  			=      lowerCAmelCase__  			=      node
               lowerCAmelCase__  			=      1
        else:
               # connect nodes
               lowerCAmelCase__  			=      self._front
               lowerCAmelCase__  			=      node
               lowerCAmelCase__  			=      node  # assign new front to the new node

               self._len += 1

               # make sure there were no errors
               assert not self.is_empty(), "Error on appending value."




 def 	__snake_case		(    self      :						Union[str, Any]    ,					lowercase__      :						Iterable[Any]):







        '''simple docstring'''



        for val in iterable:
               self.append(lowercase__)




 def 	__snake_case		(    self      :						Optional[Any]    ,					lowercase__      :						Iterable[Any]):







        '''simple docstring'''



        for val in iterable:
               self.appendleft(lowercase__)




 def 	__snake_case		(    self      :						int):







        '''simple docstring'''



        assert not self.is_empty(), "Deque is empty."

        lowerCAmelCase__  			=      self._back
        lowerCAmelCase__  			=      self._back.prev_node  # set new back
        # drop the last node - python will deallocate memory automatically
        lowerCAmelCase__  			=      None

        self._len -= 1

        return topop.val




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        assert not self.is_empty(), "Deque is empty."

        lowerCAmelCase__  			=      self._front
        lowerCAmelCase__  			=      self._front.next_node  # set new front and drop the first node
        lowerCAmelCase__  			=      None

        self._len -= 1

        return topop.val




 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



        return self._front is None




 def __len__(    self      :						Any):







        '''simple docstring'''



        return self._len




 def __eq__(    self      :						str    ,					lowercase__      :						object):







        '''simple docstring'''



        if not isinstance(lowercase__    ,					lowercase__):
               return NotImplemented

        lowerCAmelCase__  			=      self._front
        lowerCAmelCase__  			=      other._front

        # if the length of the dequeues are not the same, they are not equal
        if len(self) != len(lowercase__):
               return False

        while me is not None and oth is not None:
               # compare every value
               if me.val != oth.val:
                      return False
               lowerCAmelCase__  			=      me.next_node
               lowerCAmelCase__  			=      oth.next_node

        return True




 def __iter__(    self      :						List[str]):







        '''simple docstring'''



        return Deque._Iterator(self._front)




 def __repr__(    self      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        lowerCAmelCase__  			=      self._front
        while aux is not None:
               # append the values in a list to display
               values_list.append(aux.val)
               lowerCAmelCase__  			=      aux.next_node

        return F"""[{', '.join(repr(lowercase__) for val in values_list)}]"""


if __name__ == "__main__":
    import doctest

    doctest.testmod()

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_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						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.'    ,					lowercase__    ,					)
               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__(lowercase__    ,					lowercase__)




 def __call__(    self      :						Any    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,					lowercase__      :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,					lowercase__      :						Union[List[List[int]], List[List[List[int]]]] = None    ,					lowercase__      :						Optional[Union[List[int], List[List[int]]]] = None    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[bool, str, PaddingStrategy] = False    ,					lowercase__      :						Union[bool, str, TruncationStrategy] = False    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						int = 0    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[str, TensorType]] = None    ,					**lowercase__      :						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.')

        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
        lowerCAmelCase__  			=      self.image_processor(images=lowercase__    ,					return_tensors=lowercase__)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(lowercase__    ,					lowercase__):
                      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=lowercase__    ,					add_special_tokens=lowercase__    ,					padding=lowercase__    ,					truncation=lowercase__    ,					max_length=lowercase__    ,					stride=lowercase__    ,					pad_to_multiple_of=lowercase__    ,					return_token_type_ids=lowercase__    ,					return_attention_mask=lowercase__    ,					return_overflowing_tokens=lowercase__    ,					return_special_tokens_mask=lowercase__    ,					return_offsets_mapping=lowercase__    ,					return_length=lowercase__    ,					verbose=lowercase__    ,					return_tensors=lowercase__    ,					**lowercase__    ,					)

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

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



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

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

        return images_with_overflow




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



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




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					lowercase__    ,					)
        return self.image_processor_class




 @property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					lowercase__    ,					)
        return self.image_processor
