
from sklearn.metrics import mean_absolute_error

import datasets


lowercase__						:List[Any]					    =       "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"

lowercase__						:str					    =       "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


lowercase__						:Optional[Any]					    =       "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n"







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




							def 					A__ (							self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(self._get_feature_types())  ,reference_urls=[
												        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
												    ]  ,)




							def 					A__ (							self):





												if self.config_name == "multilist":
																	return {
																	    "predictions": datasets.Sequence(datasets.Value('''float''')),
																	    "references": datasets.Sequence(datasets.Value('''float''')),
																	}
												else:
																	return {
																	    "predictions": datasets.Value('''float'''),
																	    "references": datasets.Value('''float'''),
																	}





							def 					A__ (							self  ,A__  ,A__  ,A__=None  ,A__="uniform_average"):
												lowercase		      =		mean_absolute_error(A__  ,A__  ,sample_weight=A__  ,multioutput=A__)

												return {"mae": mae_score}









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


__lowerCamelCase								=      logging.getLogger(__name__)


@dataclass
class 	snake_case_			:




       """simple docstring"""






       _lowerCamelCase           =					field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
       _lowerCamelCase           =					field(default=lowercase__			,			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.
       _lowerCamelCase           =					field(
           default=lowercase__			,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,			)







def  _snake_case			(							)  ->  Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_	:						Union[str, Any]					   =		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.
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Any					   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
      else:
            UpperCAmelCase_   ,   UpperCAmelCase_   ,   UpperCAmelCase_	:						Union[str, Any]					   =		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"       ,	__snake_case     )

      # 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.

      UpperCAmelCase_	:						Any					   =		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       ,	)
      UpperCAmelCase_	:						List[str]					   =		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=__snake_case       ,	)
      UpperCAmelCase_	:						Optional[int]					   =		AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path       ,	from_tf=bool(".ckpt" in model_args.model_name_or_path     )       ,	config=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)

      # Get datasets
      UpperCAmelCase_	:						str					   =		hasattr(model.config       ,	"lang2id"     )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir     )
          if training_args.do_train
          else None
      )
      UpperCAmelCase_	:						Optional[Any]					   =		(
          SquadDataset(
              __snake_case       ,	tokenizer=__snake_case       ,	mode="dev"       ,	is_language_sensitive=__snake_case       ,	cache_dir=model_args.cache_dir       ,	)
          if training_args.do_eval
          else None
      )

      # Data collator
      UpperCAmelCase_	:						Optional[Any]					   =		DataCollatorWithPadding(__snake_case       ,	pad_to_multiple_of=8     ) if training_args.fpaa else None

      # Initialize our Trainer
      UpperCAmelCase_	:						Any					   =		Trainer(
          model=__snake_case       ,	args=__snake_case       ,	train_dataset=__snake_case       ,	eval_dataset=__snake_case       ,	data_collator=__snake_case       ,	)

      # 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  _snake_case			(							__snake_case     )  ->  List[str]:






      '''simple docstring'''







      main()


if __name__ == "__main__":
   main()
