import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


UpperCamelCase    				=		threading.Lock()
UpperCamelCase    				=		None

UpperCamelCase    				=		{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

UpperCamelCase    				=		logging.WARNING

UpperCamelCase    				=		True

def      lowercase_							(     ):
    lowercase__					:  Tuple          =							os.getenv("DIFFUSERS_VERBOSITY"						,      _SCREAMING_SNAKE_CASE)
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                f'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
                f'''has to be one of: { ", ".join(log_levels.keys()) }''')
    return _default_log_level

def      lowercase_							(     ):
    return __name__.split(".")[0]

def      lowercase_							(     ):
    return logging.getLogger(_get_library_name())

def      lowercase_							(     ):
    global _default_handler

    with _lock:
        if _default_handler:
            # This library has already configured the library root logger.
            return
        lowercase__					:  Optional[Any]          =							logging.StreamHandler()  # Set sys.stderr as stream.
        lowercase__					:  List[str]          =							sys.stderr.flush

        # Apply our default configuration to the library root logger.
        lowercase__					:  List[Any]          =							_get_library_root_logger()
        library_root_logger.addHandler(_default_handler)
        library_root_logger.setLevel(_get_default_logging_level())
        lowercase__					:  Tuple          =							False

def      lowercase_							(     ):
    global _default_handler

    with _lock:
        if not _default_handler:
            return

        lowercase__					:  Tuple          =							_get_library_root_logger()
        library_root_logger.removeHandler(_default_handler)
        library_root_logger.setLevel(logging.NOTSET)
        lowercase__					:  int          =							None

def      lowercase_							(     ):
    return log_levels

def      lowercase_							(     _lowerCamelCase				:			str = None):


    if name is None:
        lowercase__					:  Tuple          =							_get_library_name()

    _configure_library_root_logger()
    return logging.getLogger(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()

def      lowercase_							(     _lowerCamelCase				:			int):
    _configure_library_root_logger()
    _get_library_root_logger().setLevel(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    return set_verbosity(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    return set_verbosity(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    return set_verbosity(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    return set_verbosity(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler)

def      lowercase_							(     ):
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler)

def      lowercase_							(     _lowerCamelCase				:			List[str]):
    _configure_library_root_logger()

    assert handler is not None
    _get_library_root_logger().addHandler(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     _lowerCamelCase				:			Dict):
    _configure_library_root_logger()

    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    _configure_library_root_logger()
    lowercase__					:  int          =							False

def      lowercase_							(     ):
    _configure_library_root_logger()
    lowercase__					:  Optional[int]          =							True

def      lowercase_							(     ):
    lowercase__					:  int          =							_get_library_root_logger().handlers

    for handler in handlers:
        lowercase__					:  Optional[Any]          =							logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s")
        handler.setFormatter(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     ):
    lowercase__					:  Dict          =							_get_library_root_logger().handlers

    for handler in handlers:
        handler.setFormatter(_SCREAMING_SNAKE_CASE)

def      lowercase_							(     self				:			str						,      *_lowerCamelCase				:			Optional[int]						,      **_lowerCamelCase				:			List[str]):
    lowercase__					:  Tuple          =							os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS"						,      _SCREAMING_SNAKE_CASE)
    if no_advisory_warnings:
        return
    self.warning(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE)


UpperCamelCase    				=		warning_advice



class     snake_case_			:






   def __init__( self							:	Dict      ,		*lowercase_							:	List[Any]      ,		**lowercase_							:	Optional[int]    )   ->		Tuple:  # pylint: disable=unused-argument
       lowercase__					:  Union[str, Any]          =							args[0] if args else None






   def __iter__( self							:	str    )   ->		Optional[int]:
       return iter(self._iterator    )






   def __getattr__( self							:	Optional[Any]      ,		lowercase_							:	str    )   ->		Dict:

       def empty_fn(*lowercase_							:	Tuple      ,		**lowercase_							:	int    ):  # pylint: disable=unused-argument
           return

       return empty_fn






   def __enter__( self							:	Dict    )   ->		Dict:
       return self







   def __exit__( self							:	Any      ,		lowercase_							:	int      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Tuple    )   ->		Optional[int]:
       return



class     snake_case_			:






   def __call__( self							:	Tuple      ,		*lowercase_							:	List[Any]      ,		**lowercase_							:	Any    )   ->		List[Any]:
       if _tqdm_active:
           return tqdm_lib.tqdm(*__a      ,		**__a    )
       else:
           return EmptyTqdm(*__a      ,		**__a    )






   def  __UpperCamelCase	( self							:	Any      ,		*lowercase_							:	Any      ,		**lowercase_							:	int    )   ->		str:
       lowercase__					:  str          =							None
       if _tqdm_active:
           return tqdm_lib.tqdm.set_lock(*__a      ,		**__a    )







   def  __UpperCamelCase	( self							:	Any    )   ->		Optional[int]:
       if _tqdm_active:
           return tqdm_lib.tqdm.get_lock()



UpperCamelCase    				=		_tqdm_cls()

def      lowercase_							(     ):
    global _tqdm_active
    return bool(_tqdm_active)

def      lowercase_							(     ):
    global _tqdm_active
    lowercase__					:  Any          =							True

def      lowercase_							(     ):
    global _tqdm_active
    lowercase__					:  Optional[Any]          =							False










"""simple docstring"""





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCAmelCase__        =      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

lowerCAmelCase__        =      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


lowerCAmelCase__        =      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,      _KWARGS_DESCRIPTION			)
class 							_lowerCamelCase   (							datasets.Metric			):


						def      snake_case_ (self		)			-> Tuple:
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,				citation=_CITATION ,				inputs_description=_KWARGS_DESCRIPTION ,				features=datasets.Features(
								        {
								            "predictions": datasets.Value("string"		),
								            "references": datasets.Value("string"		),
								        }		) ,				homepage="https://github.com/moussaKam/FrugalScore" ,				)


						def      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "predictions and references should have the same number of sentences."
								if device is not None:
										assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
								else:
										UpperCamelCase =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

								def tokenize_function(__a		):
										return self.tokenizer(
										    data["sentence1"] ,				data["sentence2"] ,				max_length=__a ,				truncation=__a ,				padding=__a		)

								UpperCamelCase =						raw_datasets.map(__a ,				batched=__a		)
								tokenized_datasets.remove_columns(["sentence1", "sentence2"]		)
								UpperCamelCase =						Trainer(self.model ,				__a ,				tokenizer=self.tokenizer		)
								UpperCamelCase =						trainer.predict(__a		)
								return {"scores": list(predictions.predictions.squeeze(-1		)		)}






