"""simple docstring"""







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   {
    '''configuration_big_bird''': ['''BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BigBirdConfig''', '''BigBirdOnnxConfig'''],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Dict   =   ['''BigBirdTokenizer''']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		List[str]   =   ['''BigBirdTokenizerFast''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   [
         '''BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''BigBirdForCausalLM''',
         '''BigBirdForMaskedLM''',
         '''BigBirdForMultipleChoice''',
         '''BigBirdForPreTraining''',
         '''BigBirdForQuestionAnswering''',
         '''BigBirdForSequenceClassification''',
         '''BigBirdForTokenClassification''',
         '''BigBirdLayer''',
         '''BigBirdModel''',
         '''BigBirdPreTrainedModel''',
         '''load_tf_weights_in_big_bird''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE	:		Any   =   [
         '''FlaxBigBirdForCausalLM''',
         '''FlaxBigBirdForMaskedLM''',
         '''FlaxBigBirdForMultipleChoice''',
         '''FlaxBigBirdForPreTraining''',
         '''FlaxBigBirdForQuestionAnswering''',
         '''FlaxBigBirdForSequenceClassification''',
         '''FlaxBigBirdForTokenClassification''',
         '''FlaxBigBirdModel''',
         '''FlaxBigBirdPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird import BigBirdTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_big_bird_fast import BigBirdTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_big_bird import (
              BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
              BigBirdForCausalLM,
              BigBirdForMaskedLM,
              BigBirdForMultipleChoice,
              BigBirdForPreTraining,
              BigBirdForQuestionAnswering,
              BigBirdForSequenceClassification,
              BigBirdForTokenClassification,
              BigBirdLayer,
              BigBirdModel,
              BigBirdPreTrainedModel,
              load_tf_weights_in_big_bird,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_big_bird import (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
              FlaxBigBirdModel,
              FlaxBigBirdPreTrainedModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE	:		str   =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)














from collections import namedtuple

__UpperCAmelCase      =				namedtuple("from_to", "from_ to")

__UpperCAmelCase      =				{
    "millimeter": "mm",
    "centimeter": "cm",
    "meter": "m",
    "kilometer": "km",
    "inch": "in",
    "inche": "in",  # Trailing 's' has been stripped off
    "feet": "ft",
    "foot": "ft",
    "yard": "yd",
    "mile": "mi",
}

__UpperCAmelCase      =				{
    "mm": from_to(0.001, 1_0_0_0),
    "cm": from_to(0.01, 1_0_0),
    "m": from_to(1, 1),
    "km": from_to(1_0_0_0, 0.001),
    "in": from_to(0.0_254, 39.3_701),
    "ft": from_to(0.3_048, 3.28_084),
    "yd": from_to(0.9_144, 1.09_361),
    "mi": from_to(1_609.34, 0.000_621_371),
}





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_			)				->float:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		from_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 SCREAMING_SNAKE_CASE				    =		to_type.lower().rstrip('s'			)
 SCREAMING_SNAKE_CASE				    =		TYPE_CONVERSION.get(lowercase_	,  lowercase_			)
 if new_from not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'from_type\' value: {from_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 if new_to not in METRIC_CONVERSION:
  SCREAMING_SNAKE_CASE				    =		(
      f'''Invalid \'to_type\' value: {to_type!r}.\n'''
      f'''Conversion abbreviations are: {', '.join(lowercase_			)}'''
  )
  raise ValueError(lowercase_			)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
      import doctest

      doctest.testmod()




