



from ..utils import DummyObject, requires_backends


UpperCamelCase_	       =   None







def 				A			(		*__UpperCAmelCase							,					**__UpperCAmelCase	)       ->       List[Any]:



						'''simple docstring'''






						requires_backends(__UpperCAmelCase							,					['''sentencepiece''', '''tokenizers''']	)










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)





