
"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""RoFormerTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RoFormerForCausalLM""",
          """RoFormerForMaskedLM""",
          """RoFormerForMultipleChoice""",
          """RoFormerForQuestionAnswering""",
          """RoFormerForSequenceClassification""",
          """RoFormerForTokenClassification""",
          """RoFormerLayer""",
          """RoFormerModel""",
          """RoFormerPreTrainedModel""",
          """load_tf_weights_in_roformer""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFRoFormerForCausalLM""",
          """TFRoFormerForMaskedLM""",
          """TFRoFormerForMultipleChoice""",
          """TFRoFormerForQuestionAnswering""",
          """TFRoFormerForSequenceClassification""",
          """TFRoFormerForTokenClassification""",
          """TFRoFormerLayer""",
          """TFRoFormerModel""",
          """TFRoFormerPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """FlaxRoFormerForMaskedLM""",
          """FlaxRoFormerForMultipleChoice""",
          """FlaxRoFormerForQuestionAnswering""",
          """FlaxRoFormerForSequenceClassification""",
          """FlaxRoFormerForTokenClassification""",
          """FlaxRoFormerModel""",
          """FlaxRoFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







"""simple docstring"""


SCREAMING_SNAKE_CASE      =     {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE      =     {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							float:







       """simple docstring"""
       UpperCamelCase					 =		from_type.lower().strip("s"				)
       UpperCamelCase					 =		to_type.lower().strip("s"				)

       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)

       if from_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'from_type' value: {from_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       if to_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'to_type' value: {to_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       UpperCamelCase					 =		METRIC_CONVERSION[from_sanitized]
       UpperCamelCase					 =		METRIC_CONVERSION[to_sanitized]
       UpperCamelCase					 =		1

       if from_exponent > to_exponent:
              UpperCamelCase					 =		from_exponent - to_exponent
       else:
              UpperCamelCase					 =		-(to_exponent - from_exponent)

       return value * pow(10		,      UpperCAmelCase_				)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





