'''simple docstring'''

import os
import sys

UpperCamelCase__     :	Tuple				=			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__     :	Any				=			[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]







@add_start_docstrings(AutoConfig.__doc__    )
def __UpperCamelCase(   *_A					:				List[Any]  ,			**_A					:				List[Any]    ):
   '''simple docstring'''
   return AutoConfig.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoTokenizer.__doc__    )
def __UpperCamelCase(   *_A					:				Optional[Any]  ,			**_A					:				Dict    ):
   '''simple docstring'''
   return AutoTokenizer.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoModel.__doc__    )
def __UpperCamelCase(   *_A					:				Union[str, Any]  ,			**_A					:				str    ):
   '''simple docstring'''
   return AutoModel.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoModelForCausalLM.__doc__    )
def __UpperCamelCase(   *_A					:				List[str]  ,			**_A					:				List[str]    ):
   '''simple docstring'''
   return AutoModelForCausalLM.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoModelForMaskedLM.__doc__    )
def __UpperCamelCase(   *_A					:				List[Any]  ,			**_A					:				str    ):
   '''simple docstring'''
   return AutoModelForMaskedLM.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoModelForSequenceClassification.__doc__    )
def __UpperCamelCase(   *_A					:				List[Any]  ,			**_A					:				Optional[Any]    ):
   '''simple docstring'''
   return AutoModelForSequenceClassification.from_pretrained(*_A  ,			**_A    )







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__    )
def __UpperCamelCase(   *_A					:				Tuple  ,			**_A					:				Dict    ):
   '''simple docstring'''
   return AutoModelForQuestionAnswering.from_pretrained(*_A  ,			**_A    )





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__     :	Union[str, Any]				=			{'tokenization_byt5': ['ByT5Tokenizer']}


if TYPE_CHECKING:
  from .tokenization_byta import ByTaTokenizer
else:
  import sys

  UpperCamelCase__     :	List[str]				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




