

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_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  ['''image_processor''', '''tokenizer''']
 _lowerCAmelCase       		=  '''LayoutLMv2ImageProcessor'''
 _lowerCAmelCase       		=  ('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')




 def __init__( self     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     **_UpperCAmelCase       ):
   __UpperCAmelCase  :     List[str]		=    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
         ' instead.'     ,     _UpperCAmelCase     ,     )
     __UpperCAmelCase  :     int		=    kwargs.pop('feature_extractor'       )

   __UpperCAmelCase  :     Union[str, 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__(_UpperCAmelCase     ,     _UpperCAmelCase       )




 def __call__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = 0     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = None     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = False     ,     _UpperCAmelCase = True     ,     _UpperCAmelCase = None     ,     **_UpperCAmelCase     ,     ):
   # 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  :     Optional[Any]		=    self.image_processor(images=_UpperCAmelCase     ,     return_tensors=_UpperCAmelCase       )

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

   __UpperCAmelCase  :     str		=    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=_UpperCAmelCase     ,     add_special_tokens=_UpperCAmelCase     ,     padding=_UpperCAmelCase     ,     truncation=_UpperCAmelCase     ,     max_length=_UpperCAmelCase     ,     stride=_UpperCAmelCase     ,     pad_to_multiple_of=_UpperCAmelCase     ,     return_token_type_ids=_UpperCAmelCase     ,     return_attention_mask=_UpperCAmelCase     ,     return_overflowing_tokens=_UpperCAmelCase     ,     return_special_tokens_mask=_UpperCAmelCase     ,     return_offsets_mapping=_UpperCAmelCase     ,     return_length=_UpperCAmelCase     ,     verbose=_UpperCAmelCase     ,     return_tensors=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )

   # add pixel values
   __UpperCAmelCase  :     Tuple		=    features.pop('pixel_values'       )
   if return_overflowing_tokens is True:
     __UpperCAmelCase  :     Dict		=    self.get_overflowing_images(_UpperCAmelCase     ,     encoded_inputs['overflow_to_sample_mapping']       )
   __UpperCAmelCase  :     Optional[Any]		=    images

   return encoded_inputs




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   __UpperCAmelCase  :     Optional[int]		=    []
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]       )

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

   return images_with_overflow




 def 						a					( self     ,     *_UpperCAmelCase     ,     **_UpperCAmelCase       ):
   return self.tokenizer.batch_decode(*_UpperCAmelCase     ,     **_UpperCAmelCase       )




 def 						a					( self     ,     *_UpperCAmelCase     ,     **_UpperCAmelCase       ):
   return self.tokenizer.decode(*_UpperCAmelCase     ,     **_UpperCAmelCase       )




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




 @property
 def 						a					( self       ):
   warnings.warn(
       '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,     _UpperCAmelCase     ,     )
   return self.image_processor_class




 @property
 def 						a					( self       ):
   warnings.warn(
       '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,     _UpperCAmelCase     ,     )
   return self.image_processor








from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any


class     lowerCamelCase_   :
 _lowerCAmelCase       		=  ('''_front''', '''_back''', '''_len''')


 @dataclass
 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None
  _lowerCAmelCase       		=  None



 class     lowerCamelCase_   :
  _lowerCAmelCase       		=  ('''_cur''',)




  def __init__( self     ,     _UpperCAmelCase       ):
    __UpperCAmelCase  :     Tuple		=    cur




  def __iter__( self       ):
    return self




  def 						a					( self       ):
    if self._cur is None:
      # finished iterating
      raise StopIteration
    __UpperCAmelCase  :     str		=    self._cur.val
    __UpperCAmelCase  :     Dict		=    self._cur.next_node

    return val




 def __init__( self     ,     _UpperCAmelCase = None       ):
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     Any		=    None
   __UpperCAmelCase  :     int		=    0

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




 def 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     Dict		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Tuple		=    self._back
     __UpperCAmelCase  :     Optional[Any]		=    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 						a					( self     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Optional[Any]		=    self._Node(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )
   if self.is_empty():
     # front = back
     __UpperCAmelCase  :     str		=    node
     __UpperCAmelCase  :     List[str]		=    1
   else:
     # connect nodes
     __UpperCAmelCase  :     List[str]		=    self._front
     __UpperCAmelCase  :     Any		=    node
     __UpperCAmelCase  :     Dict		=    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 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.append(_UpperCAmelCase       )




 def 						a					( self     ,     _UpperCAmelCase       ):
   for val in iterable:
     self.appendleft(_UpperCAmelCase       )




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

   __UpperCAmelCase  :     List[Any]		=    self._back
   __UpperCAmelCase  :     Optional[int]		=    self._back.prev_node  # set new back
   # drop the last node - python will deallocate memory automatically
   __UpperCAmelCase  :     int		=    None

   self._len -= 1

   return topop.val




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

   __UpperCAmelCase  :     List[Any]		=    self._front
   __UpperCAmelCase  :     Union[str, Any]		=    self._front.next_node  # set new front and drop the first node
   __UpperCAmelCase  :     Union[str, Any]		=    None

   self._len -= 1

   return topop.val




 def 						a					( self       ):
   return self._front is None




 def __len__( self       ):
   return self._len




 def __eq__( self     ,     _UpperCAmelCase       ):

   if not isinstance(_UpperCAmelCase     ,     _UpperCAmelCase       ):
     return NotImplemented

   __UpperCAmelCase  :     List[str]		=    self._front
   __UpperCAmelCase  :     str		=    other._front

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

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

   return True




 def __iter__( self       ):
   return Deque._Iterator(self._front       )




 def __repr__( self       ):
   __UpperCAmelCase  :     int		=    []
   __UpperCAmelCase  :     Dict		=    self._front
   while aux is not None:
     # append the values in a list to display
     values_list.append(aux.val       )
     __UpperCAmelCase  :     List[str]		=    aux.next_node

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()





