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

import datasets


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

_A       =					"\\nFrugalScore 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.\n"


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







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





     def 						UpperCAmelCase(self :       Any  )		->		List[str]:
         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 						UpperCAmelCase(self :       Optional[Any]							,       _A :       str  )		->		Dict:
         if self.config_name == "default":
             snake_case						=   "moussaKam/frugalscore_tiny_bert-base_bert-score"
         else:
             snake_case						=   self.config_name
         snake_case						=   AutoModelForSequenceClassification.from_pretrained(_A  )
         snake_case						=   AutoTokenizer.from_pretrained(_A  )





     def 						UpperCAmelCase(self :       Union[str, Any]							,       _A :       int							,       _A :       Dict							,       _A :       str=3_2							,       _A :       Tuple=1_2_8							,       _A :       List[str]=None							,       )		->		List[str]:
         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:
             snake_case						=   "gpu" if torch.cuda.is_available() else "cpu"
         snake_case						=   TrainingArguments(
             "trainer"							,       fpaa=(device == "gpu")							,       per_device_eval_batch_size=_A							,       report_to="all"							,       no_cuda=(device == "cpu")							,       log_level="warning"							,       )
         snake_case						=   {"sentence1": predictions, "sentence2": references}
         snake_case						=   datasets.Dataset.from_dict(_A  )

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

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



import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir						)
        and os.listdir(training_args.output_dir						)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."						)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,						datefmt="%m/%d/%Y %H:%M:%S"					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1						)					,						training_args.fpaa					,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank						):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"					,						A__						)

    # Prepare Question-Answering task
    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case						=   AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
    snake_case						=   AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

    # Get datasets
    snake_case						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # Data collator
    snake_case						=   DataCollatorWithPadding(A__					,						pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

