
'''simple docstring'''



import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


UpperCamelCase__      :		Any       							=  re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")

@total_ordering
@dataclass
class    _a				:





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  None
       SCREAMING_SNAKE_CASE      =  None
       SCREAMING_SNAKE_CASE      =  None
       SCREAMING_SNAKE_CASE      =  None






       def        UpperCamelCase				(							self      )    ->			Dict:
          _SCREAMING_SNAKE_CASE          =				_str_to_version_tuple(self.version_str      )






       def __repr__(							self      )    ->			int:
          return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"






       @property
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          return self.major, self.minor, self.patch






       def        UpperCamelCase				(							self       ,							A__      )    ->			List[Any]:





          if isinstance(UpperCamelCase_       ,							UpperCamelCase_      ):
             return Version(UpperCamelCase_      )
          elif isinstance(UpperCamelCase_       ,							UpperCamelCase_      ):
             return other
          raise TypeError(F"{other} (type {type(UpperCamelCase_      )}) cannot be compared to version."      )






       def __eq__(							self       ,							A__      )    ->			Optional[int]:





          try:
             _SCREAMING_SNAKE_CASE          =				self._validate_operand(UpperCamelCase_      )
          except (TypeError, ValueError):
             return False
          else:
             return self.tuple == other.tuple






       def __lt__(							self       ,							A__      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE          =				self._validate_operand(UpperCamelCase_      )
          return self.tuple < other.tuple






       def __hash__(							self      )    ->			List[str]:
          return hash(_version_tuple_to_str(self.tuple      )      )






       @classmethod
       def        UpperCamelCase				(							cls       ,							A__      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE          =				{f.name for f in dataclasses.fields(cls      )}
          return cls(**{k: v for k, v in dic.items() if k in field_names}      )






       def        UpperCamelCase				(							self      )    ->			str:
          return self.version_str






def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          =				_VERSION_REG.match(_lowercase						)
   if not res:
      raise ValueError(F"Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits."						)
   return tuple(int(_lowercase						) for v in [res.group("""major"""						), res.group("""minor"""						), res.group("""patch"""						)]						)







def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   return ".".join(str(_lowercase						) for v in version_tuple						)








'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "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

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






