import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""image_processor""", """tokenizer"""]
      a__				=		"""AutoImageProcessor"""
      a__				=		"""AutoTokenizer"""



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      **lowercase)     ->      List[str]:





        '''simple docstring'''





        a__:					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.'   ,      lowercase   ,      )
          a__:					Optional[int]      							=     kwargs.pop('feature_extractor')

        a__:					Optional[int]      							=     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)
        a__:					Any      							=     self.image_processor
        a__:					str      							=     False



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





        if self._in_target_context_manager:
          return self.current_processor(*lowercase   ,      **lowercase)

        a__:					Any      							=     kwargs.pop('images'   ,      lowercase)
        a__:					Any      							=     kwargs.pop('text'   ,      lowercase)
        if len(lowercase) > 0:
          a__:					int      							=     args[0]
          a__:					Union[str, Any]      							=     args[1:]

        if images is None and text is None:
          raise ValueError('You need to specify either an `images` or `text` input to process.')

        if images is not None:
          a__:					str      							=     self.image_processor(lowercase   ,      *lowercase   ,      **lowercase)
        if text is not None:
          a__:					List[str]      							=     self.tokenizer(lowercase   ,      **lowercase)

        if text is None:
          return inputs
        elif images is None:
          return encodings
        else:
          a__:					Dict      							=     encodings['input_ids']
          return inputs



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      List[str]:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self   ,      *lowercase   ,      **lowercase)     ->      str:





        '''simple docstring'''





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



      @contextmanager
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        warnings.warn(
            '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
            'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
            'your images inputs, or in a separate call.')
        a__:					Any      							=     True
        a__:					Any      							=     self.tokenizer
        yield
        a__:					Optional[Any]      							=     self.image_processor
        a__:					int      							=     False



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=False   ,      lowercase=None)     ->      Union[str, Any]:





        '''simple docstring'''





        if added_vocab is None:
          a__:					List[Any]      							=     self.tokenizer.get_added_vocab()

        a__:					List[str]      							=     {}

        while tokens:
          a__:					Any      							=     re.search(r'<s_(.*?)>'   ,      lowercase   ,      re.IGNORECASE)
          if start_token is None:
            break
          a__:					List[str]      							=     start_token.group(1)
          a__:					List[Any]      							=     re.search(rf'</s_{key}>'   ,      lowercase   ,      re.IGNORECASE)
          a__:					Union[str, Any]      							=     start_token.group()
          if end_token is None:
            a__:					Dict      							=     tokens.replace(lowercase   ,      '')
          else:
            a__:					Dict      							=     end_token.group()
            a__:					int      							=     re.escape(lowercase)
            a__:					List[str]      							=     re.escape(lowercase)
            a__:					Any      							=     re.search(f'{start_token_escaped}(.*?){end_token_escaped}'   ,      lowercase   ,      re.IGNORECASE)
            if content is not None:
              a__:					Any      							=     content.group(1).strip()
              if r"<s_" in content and r"</s_" in content:  # non-leaf node
                a__:					List[str]      							=     self.tokenajson(lowercase   ,      is_inner_value=lowercase   ,      added_vocab=lowercase)
                if value:
                  if len(lowercase) == 1:
                    a__:					Tuple      							=     value[0]
                  a__:					int      							=     value
              else:  # leaf nodes
                a__:					Any      							=     []
                for leaf in content.split(r'<sep/>'):
                  a__:					Tuple      							=     leaf.strip()
                  if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                    a__:					Tuple      							=     leaf[1:-2]  # for categorical special tokens
                  output[key].append(lowercase)
                if len(output[key]) == 1:
                  a__:					str      							=     output[key][0]

            a__:					str      							=     tokens[tokens.find(lowercase) + len(lowercase) :].strip()
            if tokens[:6] == r"<sep/>":  # non-leaf nodes
              return [output] + self.tokenajson(tokens[6:]   ,      is_inner_value=lowercase   ,      added_vocab=lowercase)

        if len(lowercase):
          return [output] if is_inner_value else output
        else:
          return [] if is_inner_value else {"text_sequence": tokens}



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





        '''simple docstring'''





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



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Any      							=     credit_card_number
  a__:					Tuple      							=     0
  a__:					List[str]      							=     len(_SCREAMING_SNAKE_CASE       ) - 2
  for i in range(_SCREAMING_SNAKE_CASE		,					-1		,					-2       ):
    #  double the value of every second digit
    a__:					Tuple      							=     int(cc_number[i]       )
    digit *= 2
    # If doubling of a number results in a two digit number
    # i.e greater than 9(e.g., 6 × 2 = 12),
    # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
    # to get a single digit number.
    if digit > 9:
      digit %= 10
      digit += 1
    a__:					Optional[Any]      							=     cc_number[:i] + str(_SCREAMING_SNAKE_CASE       ) + cc_number[i + 1 :]
    total += digit

  # Sum up the remaining digits
  for i in range(len(_SCREAMING_SNAKE_CASE       ) - 1		,					-1		,					-2       ):
    total += int(cc_number[i]       )

  return total % 10 == 0







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Optional[int]      							=     F'{credit_card_number} is an invalid credit card number because'
  if not credit_card_number.isdigit():
    print(F'{error_message} it has nonnumerical characters.'       )
    return False

  if not 13 <= len(_SCREAMING_SNAKE_CASE       ) <= 16:
    print(F'{error_message} of its length.'       )
    return False

  if not validate_initial_digits(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} of its first two digits.'       )
    return False

  if not luhn_validation(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} it fails the Luhn check.'       )
    return False

  print(F'{credit_card_number} is a valid credit card number.'       )
  return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 validate_credit_card_number('4111111111111111')
 validate_credit_card_number('32323')


