


'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     List[str]     = credit_card_number
       _A					:     Dict     = 0
       _A					:     int     = len(lowerCamelCase       ) - 2
       for i in range(lowerCamelCase  ,-1  ,-2       ):
              #  double the value of every second digit
              _A					:     int     = 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					:     Union[str, Any]     = cc_number[:i] + str(lowerCamelCase       ) + cc_number[i + 1 :]
              total += digit

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

       return total % 10 == 0






def 				lowerCAmelCase__	(    lowerCamelCase					:      str       ):
       _A					:     str     = 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(lowerCamelCase       ) <= 16:
              print(F'{error_message} of its length.'       )
              return False

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

       if not luhn_validation(lowerCamelCase       ):
              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''')










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A				:					List[str]    						=			{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					int    						=			['''ElectraTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[str]    						=			[
       '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ElectraForCausalLM''',
       '''ElectraForMaskedLM''',
       '''ElectraForMultipleChoice''',
       '''ElectraForPreTraining''',
       '''ElectraForQuestionAnswering''',
       '''ElectraForSequenceClassification''',
       '''ElectraForTokenClassification''',
       '''ElectraModel''',
       '''ElectraPreTrainedModel''',
       '''load_tf_weights_in_electra''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFElectraForMaskedLM''',
       '''TFElectraForMultipleChoice''',
       '''TFElectraForPreTraining''',
       '''TFElectraForQuestionAnswering''',
       '''TFElectraForSequenceClassification''',
       '''TFElectraForTokenClassification''',
       '''TFElectraModel''',
       '''TFElectraPreTrainedModel''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''FlaxElectraForCausalLM''',
       '''FlaxElectraForMaskedLM''',
       '''FlaxElectraForMultipleChoice''',
       '''FlaxElectraForPreTraining''',
       '''FlaxElectraForQuestionAnswering''',
       '''FlaxElectraForSequenceClassification''',
       '''FlaxElectraForTokenClassification''',
       '''FlaxElectraModel''',
       '''FlaxElectraPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
   from .tokenization_electra import ElectraTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_electra_fast import ElectraTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_electra import (
          ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
          ElectraPreTrainedModel,
          load_tf_weights_in_electra,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_electra import (
          TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFElectraForMaskedLM,
          TFElectraForMultipleChoice,
          TFElectraForPreTraining,
          TFElectraForQuestionAnswering,
          TFElectraForSequenceClassification,
          TFElectraForTokenClassification,
          TFElectraModel,
          TFElectraPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_electra import (
          FlaxElectraForCausalLM,
          FlaxElectraForMaskedLM,
          FlaxElectraForMultipleChoice,
          FlaxElectraForPreTraining,
          FlaxElectraForQuestionAnswering,
          FlaxElectraForSequenceClassification,
          FlaxElectraForTokenClassification,
          FlaxElectraModel,
          FlaxElectraPreTrainedModel,
      )

else:
   import sys

   A				:					Tuple    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






