

import os
import sys

__lowercase     : Union[str, Any]       =						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,
)


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







@add_start_docstrings(AutoConfig.__doc__		)
def       lowercase					(	*__A :					Optional[int] ,    **__A :					Union[str, Any]		)				->			Dict:





    '''simple docstring'''




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







@add_start_docstrings(AutoTokenizer.__doc__		)
def       lowercase					(	*__A :					List[Any] ,    **__A :					str		)				->			List[Any]:





    '''simple docstring'''




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







@add_start_docstrings(AutoModel.__doc__		)
def       lowercase					(	*__A :					Tuple ,    **__A :					Optional[Any]		)				->			int:





    '''simple docstring'''




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







@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def       lowercase					(	*__A :					Any ,    **__A :					Union[str, Any]		)				->			Union[str, Any]:





    '''simple docstring'''




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







@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def       lowercase					(	*__A :					Tuple ,    **__A :					Dict		)				->			Union[str, Any]:





    '''simple docstring'''




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







@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def       lowercase					(	*__A :					Optional[Any] ,    **__A :					Tuple		)				->			Optional[int]:





    '''simple docstring'''




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







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def       lowercase					(	*__A :					Union[str, Any] ,    **__A :					int		)				->			int:





    '''simple docstring'''




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




'''simple docstring'''


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						  =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """FocalNetForImageClassification""",
           """FocalNetForMaskedImageModeling""",
           """FocalNetBackbone""",
           """FocalNetModel""",
           """FocalNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       __snake_case						  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

