





"""simple docstring"""




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






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




										'''simple docstring'''



										_UpperCamelCase     :   int      =       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     :   str      =       kwargs.pop('''feature_extractor'''				)

										_UpperCamelCase     :   str      =       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						:       List[str],					lowerCAmelCase__						:       int,					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] = False,					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[Any],					)      -> BatchEncoding:




										'''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
										_UpperCamelCase     :   List[str]      =       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     :   List[str]      =       [text]  # add batch dimension (as the image processor always adds a batch dimension)
																_UpperCamelCase     :   List[str]      =       features['''words''']

										_UpperCamelCase     :   Union[str, Any]      =       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     :   List[str]      =       features.pop('''pixel_values'''				)
										if return_overflowing_tokens is True:
																_UpperCamelCase     :   Optional[Any]      =       self.get_overflowing_images(lowerCAmelCase__,					encoded_inputs['''overflow_to_sample_mapping''']				)
										_UpperCamelCase     :   List[Any]      =       images

										return encoded_inputs






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Dict				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       []
										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 							snake_case      (					self						:       List[str],					*lowerCAmelCase__						:       Dict,					**lowerCAmelCase__						:       int				)      -> Optional[Any]:




										'''simple docstring'''



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






				def 							snake_case      (					self						:       Tuple,					*lowerCAmelCase__						:       Optional[int],					**lowerCAmelCase__						:       Dict				)      -> List[Any]:




										'''simple docstring'''



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






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




										'''simple docstring'''



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






				@property
				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''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 							snake_case      (					self						:       List[str]				)      -> 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  __UpperCamelCase    (						snake_case      )    ->		bool:


   '''simple docstring'''







   __A    =  42
   __A    =  list(snake_case      )
   __A   ,		__A    =  _vector[:-1], int(_vector[-1]      )
   __A    =  [int(snake_case      ) for digit in __vector]

   vector.reverse()
   for i, digit in enumerate(snake_case      ):
      if i & 1 == 0:
         __A    =  digit * 2
         if doubled > 9:
            doubled -= 9
         check_digit += doubled
      else:
         check_digit += digit

   return check_digit % 1_0 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("""79927398713""")
    assert not is_luhn("""79927398714""")

