






"""simple docstring"""



import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_UpperCamelCase						:    Dict		=						getLogger(__name__)


_UpperCamelCase						:    int		=						'cuda' if torch.cuda.is_available() else 'cpu'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    str						,   __snake_case     :    str						,   __snake_case     :    int = 8						,   __snake_case     :    str = DEFAULT_DEVICE						,   __snake_case     :    Tuple=False						,   __snake_case     :    Tuple="summarization"						,   __snake_case     :    List[str]=None						,   **__snake_case     :    Dict						,   ):





  '''simple docstring'''





  lowercase				       =			Path(__snake_case       ).open('w'						,   encoding='utf-8'       )
  lowercase				       =			str(__snake_case       )
  lowercase				       =			AutoModelForSeqaSeqLM.from_pretrained(__snake_case       ).to(__snake_case       )
  if fpaa:
    lowercase				       =			model.half()

  lowercase				       =			AutoTokenizer.from_pretrained(__snake_case       )
  logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'       )  # if this is wrong, check config.model_type.

  lowercase				       =			time.time()
  # update config with task specific params
  use_task_specific_params(__snake_case						,   __snake_case       )
  if prefix is None:
    lowercase				       =			prefix or getattr(model.config						,   'prefix'						,   ''       ) or ''
  for examples_chunk in tqdm(list(chunks(__snake_case						,   __snake_case       )       )       ):
    lowercase				       =			[prefix + text for text in examples_chunk]
    lowercase				       =			tokenizer(__snake_case						,   return_tensors='pt'						,   truncation=__snake_case						,   padding='longest'       ).to(__snake_case       )
    lowercase				       =			model.generate(
        input_ids=batch.input_ids						,   attention_mask=batch.attention_mask						,   **__snake_case						,   )
    lowercase				       =			tokenizer.batch_decode(__snake_case						,   skip_special_tokens=__snake_case						,   clean_up_tokenization_spaces=__snake_case       )
    for hypothesis in dec:
      fout.write(hypothesis + '\n'       )
      fout.flush()
  fout.close()
  lowercase				       =			int(time.time() - start_time       )  # seconds
  lowercase				       =			len(__snake_case       )
  return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs						,   4       )}
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''simple docstring'''





  return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any=True       ):





  '''simple docstring'''





  lowercase				       =			argparse.ArgumentParser()
  parser.add_argument('model_name'						,   type=__snake_case						,   help='like facebook/bart-large-cnn,t5-base, etc.'       )
  parser.add_argument('input_path'						,   type=__snake_case						,   help='like cnn_dm/test.source'       )
  parser.add_argument('save_path'						,   type=__snake_case						,   help='where to save summaries'       )
  parser.add_argument('--reference_path'						,   type=__snake_case						,   required=__snake_case						,   help='like cnn_dm/test.target'       )
  parser.add_argument('--score_path'						,   type=__snake_case						,   required=__snake_case						,   default='metrics.json'						,   help='where to save metrics'       )
  parser.add_argument('--device'						,   type=__snake_case						,   required=__snake_case						,   default=__snake_case						,   help='cuda, cuda:1, cpu etc.'       )
  parser.add_argument(
      '--prefix'						,   type=__snake_case						,   required=__snake_case						,   default=__snake_case						,   help='will be added to the begininng of src examples'       )
  parser.add_argument('--task'						,   type=__snake_case						,   default='summarization'						,   help='used for task_specific_params + metrics'       )
  parser.add_argument('--bs'						,   type=__snake_case						,   default=8						,   required=__snake_case						,   help='batch size'       )
  parser.add_argument(
      '--n_obs'						,   type=__snake_case						,   default=-1						,   required=__snake_case						,   help='How many observations. Defaults to all.'       )
  parser.add_argument('--fp16'						,   action='store_true'       )
  parser.add_argument('--dump-args'						,   action='store_true'						,   help='print the custom hparams with the results'       )
  parser.add_argument(
      '--info'						,   nargs='?'						,   type=__snake_case						,   const=datetime_now()						,   help=(
          'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
          ' lang=en-ru. If no value is passed, the current datetime string will be used.'
      )						,   )
  # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
  lowercase       ,	lowercase				       =			parser.parse_known_args()
  lowercase				       =			parse_numeric_n_bool_cl_kwargs(__snake_case       )
  if parsed_args and verbose:
    print(f'parsed the following generate kwargs: {parsed_args}'       )
  with open(args.input_path       ) as f:
    lowercase				       =			[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    lowercase				       =			examples[: args.n_obs]
  Path(args.save_path       ).parent.mkdir(exist_ok=__snake_case       )
  if args.reference_path is None and Path(args.score_path       ).exists():
    warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'       )
  lowercase				       =			generate_summaries_or_translations(
      __snake_case						,   args.save_path						,   args.model_name						,   batch_size=args.bs						,   device=args.device						,   fpaa=args.fpaa						,   task=args.task						,   prefix=args.prefix						,   **__snake_case						,   )

  if args.reference_path is None:
    return {}

  # Compute scores
  lowercase				       =			calculate_bleu if 'translation' in args.task else calculate_rouge
  lowercase				       =			[x.rstrip() for x in open(args.save_path       ).readlines()]
  lowercase				       =			[x.rstrip() for x in open(args.reference_path       ).readlines()][: len(__snake_case       )]
  lowercase				       =			score_fn(__snake_case						,   __snake_case       )
  scores.update(__snake_case       )

  if args.dump_args:
    scores.update(__snake_case       )
  if args.info:
    lowercase				       =			args.info

  if verbose:
    print(__snake_case       )

  if args.score_path is not None:
    json.dump(__snake_case						,   open(args.score_path						,   'w'       )       )

  return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)








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						:							Optional[int]			     =						'\\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						:							str			     =						'\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						:							Optional[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        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							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 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

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

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

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






