


'''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    _snake_case  (	*A					,       **A			)		->       Optional[int]:
							return AutoConfig.from_pretrained(*A					,       **A			)


@add_start_docstrings(AutoTokenizer.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       Optional[int]:
							return AutoTokenizer.from_pretrained(*A					,       **A			)


@add_start_docstrings(AutoModel.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       List[Any]:
							return AutoModel.from_pretrained(*A					,       **A			)


@add_start_docstrings(AutoModelForCausalLM.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       Any:
							return AutoModelForCausalLM.from_pretrained(*A					,       **A			)


@add_start_docstrings(AutoModelForMaskedLM.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       Union[str, Any]:
							return AutoModelForMaskedLM.from_pretrained(*A					,       **A			)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       int:
							return AutoModelForSequenceClassification.from_pretrained(*A					,       **A			)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__			)
def    _snake_case  (	*A					,       **A			)		->       Optional[Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*A					,       **A			)




'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							__lowerCAmelCase		     =  True
except (ImportError, ModuleNotFoundError):
							__lowerCAmelCase		     =  False

if NLTK_AVAILABLE:
							with FileLock('''.lock''') as lock:
														nltk.download('''punkt''', quiet=True)



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		re.sub('<n>'		,     ''		,     lowerCAmelCase_    )  # remove pegasus newline char
		assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
		return "\n".join(nltk.sent_tokenize(lowerCAmelCase_    )    )




