





'''simple docstring'''

from __future__ import annotations

import sys





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->Any:







    if n < 0:
        raise ValueError("""Negative arguments are not supported"""   )
    return _fib(__lowerCamelCase   )[0]





def lowerCamelCase						(					__lowerCamelCase			:   int   )						->Optional[Any]:







    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _SCREAMING_SNAKE_CASE     =      _fib(n // 2   )
    _SCREAMING_SNAKE_CASE     =      a * (b * 2 - a)
    _SCREAMING_SNAKE_CASE     =      a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
      lowercase_         =			int(sys.argv[1])
      print(f"""fibonacci({n}) is {fibonacci(n)}""")











'''simple docstring'''

from typing import TYPE_CHECKING

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


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

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

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """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

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



