


import os
import sys

__UpperCamelCase				:  List[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,
)


__UpperCamelCase				:  Dict      					=							[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]
@add_start_docstrings(AutoConfig.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoConfig.from_pretrained(*__lowercase			,       **__lowercase       )
@add_start_docstrings(AutoTokenizer.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoTokenizer.from_pretrained(*__lowercase			,       **__lowercase       )
@add_start_docstrings(AutoModel.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoModel.from_pretrained(*__lowercase			,       **__lowercase       )
@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoModelForCausalLM.from_pretrained(*__lowercase			,       **__lowercase       )
@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoModelForMaskedLM.from_pretrained(*__lowercase			,       **__lowercase       )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoModelForSequenceClassification.from_pretrained(*__lowercase			,       **__lowercase       )


@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      snake_case_						( *__lowercase			,       **__lowercase       ):
   return AutoModelForQuestionAnswering.from_pretrained(*__lowercase			,       **__lowercase       )



import fire

from utils import calculate_rouge, save_json
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase=None			,       **__lowercase       ):
   UpperCAmelCase_							:      Tuple						=   [x.strip() for x in open(__lowercase       ).readlines()]
   UpperCAmelCase_							:      Dict						=   [x.strip() for x in open(__lowercase       ).readlines()][: len(__lowercase       )]
   UpperCAmelCase_							:      int						=   calculate_rouge(__lowercase			,       __lowercase			,       **__lowercase       )
   if save_path is not None:
      save_json(__lowercase			,       __lowercase			,       indent=__lowercase       )
   return metrics  # these print nicely


if __name__ == "__main__":
       fire.Fire(calculate_rouge_path)