
'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):
				_snake_case		    = '''timm_backbone'''




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				int=3,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=True,    SCREAMING_SNAKE_CASE_	:				Any=True,    SCREAMING_SNAKE_CASE_	:				int=None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
										super().__init__(**SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    backbone
										snake_case       :     Tuple =    num_channels
										snake_case       :     Union[str, Any] =    features_only
										snake_case       :     List[Any] =    use_pretrained_backbone
										snake_case       :     Any =    True
										snake_case       :     Tuple =    out_indices if out_indices is not None else (-1,)



'''simple docstring'''





import os
import sys

UpperCAmelCase    					=   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,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

