"""simple docstring"""







import os
import sys

SCREAMING_SNAKE_CASE									=						os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


SCREAMING_SNAKE_CASE									=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]


@add_start_docstrings(AutoConfig.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoConfig.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)


@add_start_docstrings(AutoTokenizer.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoTokenizer.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)


@add_start_docstrings(AutoModel.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoModel.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)


@add_start_docstrings(AutoModelForCausalLM.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoModelForCausalLM.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)


@add_start_docstrings(AutoModelForMaskedLM.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoModelForMaskedLM.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoModelForSequenceClassification.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__			)
def      __lowerCAmelCase(		*__UpperCAmelCase     ,**__UpperCAmelCase			):






	"""simple docstring"""





	return AutoModelForQuestionAnswering.from_pretrained(*lowerCamelCase_     ,**lowerCamelCase_			)




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

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



