






from typing import TYPE_CHECKING

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


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

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

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

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










def 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''recursive_lucas_number accepts only integer arguments.'''				)
 if n_th_number == 0:
  return 2
 if n_th_number == 1:
  return 1

 return recursive_lucas_number(n_th_number - 1				) + recursive_lucas_number(
     n_th_number - 2				)
def 		_a					(   a				:int				)							->    int:
 if not isinstance(a			,							a				):
  raise TypeError('''dynamic_lucas_number accepts only integer arguments.'''				)
 a						,  a							=   2, 1
 for _ in range(a				):
  a						,  a							=   b, a + b
 return a


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

     testmod()
     UpperCAmelCase__            =	int(input("Enter the number of terms in lucas series:\n").strip())
     print("Using recursive function to calculate lucas series:")
     print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
     print("\nUsing dynamic function to calculate lucas series:")
     print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))


