


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


UpperCAmelCase     =       logging.getLogger(__name__)







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def 			__lowerCAmelCase       ()->    Dict:




      """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'''   ,   lowercase_   )

      # 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=lowercase_   ,   )
      snake_case_			= AutoModelForQuestionAnswering.from_pretrained(
          model_args.model_name_or_path   ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path   )   ,   config=lowercase_   ,   cache_dir=model_args.cache_dir   ,   )

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

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

      # Initialize our Trainer
      snake_case_			= Trainer(
          model=lowercase_   ,   args=lowercase_   ,   train_dataset=lowercase_   ,   eval_dataset=lowercase_   ,   data_collator=lowercase_   ,   )

      # 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 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    int:




      """simple docstring"""

      main()


if __name__ == "__main__":
   main()





'''simple docstring'''







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


_lowerCamelCase				:		str				      =    '\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

_lowerCamelCase				:		Union[str, Any]				      =    '\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

_lowerCamelCase				:		List[Any]				      =    '\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'





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




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return datasets.MetricInfo(
							    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"					),
							                "prediction_text": datasets.Value("string"					),
							                "no_answer_probability": datasets.Value("float32"					),
							            },
							            "references": {
							                "id": datasets.Value("string"					),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"					), "answer_start": datasets.Value("int32"					)}					),
							            },
							        }					),    codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"],    )






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase=1.0					):
							_snake_case      			=			{p["id"]: p["no_answer_probability"] for p in predictions}
							_snake_case      			=			[{"paragraphs": [{"qas": references}]}]
							_snake_case      			=			{p["id"]: p["prediction_text"] for p in predictions}

							_snake_case      			=			make_qid_to_has_ans(__UpperCamelCase					)  # maps qid to True/False
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if v]
							_snake_case      			=			[k for k, v in qid_to_has_ans.items() if not v]

							_snake_case			,       _snake_case      			=			get_raw_scores(__UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			apply_no_ans_threshold(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase					)

							if has_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "HasAns"					)
							if no_ans_qids:
									_snake_case      			=			make_eval_dict(__UpperCamelCase,    __UpperCamelCase,    qid_list=__UpperCamelCase					)
									merge_eval(__UpperCamelCase,    __UpperCamelCase,    "NoAns"					)
							find_all_best_thresh(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					)
							return dict(__UpperCamelCase					)





