




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :				str						=				{
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Union[str, Any]						=				[
      "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EsmForMaskedLM",
      "EsmForSequenceClassification",
      "EsmForTokenClassification",
      "EsmModel",
      "EsmPreTrainedModel",
  ]
  _lowerCAmelCase  :				Tuple						=				["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				str						=				[
      "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEsmForMaskedLM",
      "TFEsmForSequenceClassification",
      "TFEsmForTokenClassification",
      "TFEsmModel",
      "TFEsmPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  from .tokenization_esm import EsmTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmForMaskedLM,
        EsmForSequenceClassification,
        EsmForTokenClassification,
        EsmModel,
        EsmPreTrainedModel,
    )
    from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_esm import (
        TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEsmForMaskedLM,
        TFEsmForSequenceClassification,
        TFEsmForTokenClassification,
        TFEsmModel,
        TFEsmPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase  :				List[Any]						=				_LazyModule(__name__, globals()["__file__"], _import_structure)











_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, 3),
    "milligram": pow(1_0, 6),
    "metric-ton": pow(1_0, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_0_0_0,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

_lowerCAmelCase  :				dict[str, float]						=				{
    "kilogram": 1,
    "gram": pow(1_0, -3),
    "milligram": pow(1_0, -6),
    "metric-ton": pow(1_0, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				float		):




 """simple docstring"""







 if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
  UpperCAmelCase__								=  (
      F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
  )
  raise ValueError(_lowerCAmelCase		)
 return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





