


def    snake_case_     (  snake_case			,							snake_case     )  ->				int:
       while a != 0:
              lowercase__			,						lowercase__:   Dict  			=   b % a, a
       return b




def    snake_case_     (  snake_case			,							snake_case     )  ->				int:
       if gcd(snake_case			,							snake_case     ) != 1:
              lowercase__:   int  			=   f'mod inverse of {a!r} and {m!r} does not exist'
              raise ValueError(snake_case     )
       lowercase__			,						lowercase__			,						lowercase__:   int  			=   1, 0, a
       lowercase__			,						lowercase__			,						lowercase__:   Union[str, Any]  			=   0, 1, m
       while va != 0:
              lowercase__:   Optional[Any]  			=   ua // va
              lowercase__			,						lowercase__			,						lowercase__			,						lowercase__			,						lowercase__			,						lowercase__:   Tuple  			=   (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
       return ua % m






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase		       =							{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''CanineForMultipleChoice''',
     '''CanineForQuestionAnswering''',
     '''CanineForSequenceClassification''',
     '''CanineForTokenClassification''',
     '''CanineLayer''',
     '''CanineModel''',
     '''CaninePreTrainedModel''',
     '''load_tf_weights_in_canine''',
 ]


if TYPE_CHECKING:
 from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
 from .tokenization_canine import CanineTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_canine import (
      CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
      CanineForMultipleChoice,
      CanineForQuestionAnswering,
      CanineForSequenceClassification,
      CanineForTokenClassification,
      CanineLayer,
      CanineModel,
      CaninePreTrainedModel,
      load_tf_weights_in_canine,
  )


else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


