




"""simple docstring"""



import argparse
import json
import re
import string
import sys
from collections import Counter







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Dict:

 def remove_articles(lowercase_		):
  return re.sub(R"\b(a|an|the)\b"    ,				" "    ,				lowercase_		)

 def white_space_fix(lowercase_		):
  return " ".join(text.split()		)

 def remove_punc(lowercase_		):
  A__ =       set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(lowercase_		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowercase_		)		)		)		)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	int:
 A__ =       normalize_answer(lowercase_		).split()
 A__ =       normalize_answer(lowercase_		).split()
 A__ =       Counter(lowercase_		) & Counter(lowercase_		)
 A__ =       sum(common.values()		)
 if num_same == 0:
  return 0
 A__ =       1.0 * num_same / len(lowercase_		)
 A__ =       1.0 * num_same / len(lowercase_		)
 A__ =       (2 * precision * recall) / (precision + recall)
 return fa







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Tuple:
 return normalize_answer(lowercase_		) == normalize_answer(lowercase_		)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	List[str]:
 A__ =       []
 for ground_truth in ground_truths:
  A__ =       metric_fn(lowercase_    ,				lowercase_		)
  scores_for_ground_truths.append(lowercase_		)
 return max(lowercase_		)







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[str]:
 A__ =       A__ =       A__ =       0
 A__ =       []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    A__ =       f"""Unanswered question {qa["id"]} will receive score 0."""
    print(lowercase_    ,				file=sys.stderr		)
    continue

   A__ =       [x["text"] for x in qa["answers"]]
   A__ =       predictions[qa["id"]]

   A__ =       metric_max_over_ground_truths(lowercase_    ,				lowercase_    ,				lowercase_		)
   if int(_exact_match		) == 1:
    correct_ids.append(qa["id"]		)
   exact_match += _exact_match

   fa += metric_max_over_ground_truths(lowercase_    ,				lowercase_    ,				lowercase_		)

 A__ =       exact_match / total
 A__ =       fa / total

 return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE       = "1.0"
      SCREAMING_SNAKE_CASE       = argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
      parser.add_argument("data_file", help="The dataset file in JSON format.")
      parser.add_argument("pred_file", help="The model prediction file in JSON format.")
      parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
      SCREAMING_SNAKE_CASE       = parser.parse_args()

      with open(args.data_file) as data_file:
            SCREAMING_SNAKE_CASE       = json.load(data_file)
            if dataset_json["version"] != expected_version:
                  print(
                      f'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
                      file=sys.stderr,
                  )
            SCREAMING_SNAKE_CASE       = dataset_json["data"]

      with open(args.pred_file) as pred_file:
            SCREAMING_SNAKE_CASE       = json.load(pred_file)

      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE       = evaluate(dataset, predictions)

      if args.output_correct_ids:
            print(f'Output {len(correct_ids)} correctly answered question IDs.')
            with open("correct_ids.json", "w") as f:
                  json.dump(correct_ids, f)











"""simple docstring"""



import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	List[Any]:
 A__ =       filter(lambda lowercase_		: p.requires_grad    ,				model.parameters()		)
 A__ =       sum([np.prod(p.size()		) for p in model_parameters]		)
 return params


SCREAMING_SNAKE_CASE       = logging.getLogger(__name__)




class 			UpperCAmelCase_						(		pl.Callback							):




    def 							__magic_name__							(					self		: List[str]			,				snake_case_		: Optional[Any]			,				snake_case_		: Tuple   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups   )}
     pl_module.logger.log_metrics(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Dict			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule			,				snake_case_		: str			,				snake_case_		: List[str]=True   )   ->							None:




     '''simple docstring'''

     logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""   )
     A__ =       trainer.callback_metrics
     trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}   )
     # Log results
     A__ =       Path(pl_module.hparams.output_dir   )
     if type_path == "test":
      A__ =       od / "test_results.txt"
      A__ =       od / "test_generations.txt"
     else:
      # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
      # If people want this it will be easy enough to add back.
      A__ =       od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
      A__ =       od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
      results_file.parent.mkdir(exist_ok=snake_case_   )
      generations_file.parent.mkdir(exist_ok=snake_case_   )
     with open(snake_case_			,				"a+"   ) as writer:
      for key in sorted(snake_case_   ):
       if key in ["log", "progress_bar", "preds"]:
        continue
       A__ =       metrics[key]
       if isinstance(snake_case_			,				torch.Tensor   ):
        A__ =       val.item()
       A__ =       F"""{key}: {val:.6f}\n"""
       writer.write(snake_case_   )

     if not save_generations:
      return

     if "preds" in metrics:
      A__ =       "\n".join(metrics["preds"]   )
      generations_file.open("w+"   ).write(snake_case_   )




    @rank_zero_only
    def 							__magic_name__							(					self		: str			,				snake_case_		: Tuple			,				snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

     try:
      A__ =       pl_module.model.model.num_parameters()
     except AttributeError:
      A__ =       pl_module.model.num_parameters()

     A__ =       count_trainable_parameters(snake_case_   )
     # mp stands for million parameters
     trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}   )




    @rank_zero_only
    def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: pl.Trainer			,				snake_case_		: pl.LightningModule   )   ->							Union[str, Any]:




     '''simple docstring'''

     save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
     return self._write_logs(snake_case_			,				snake_case_			,				"test"   )




    @rank_zero_only
    def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: pl.Trainer			,				snake_case_		: int   )   ->							Optional[Any]:




     '''simple docstring'''

     save_json(pl_module.metrics			,				pl_module.metrics_save_path   )
     # Uncommenting this will save val generations
     # return self._write_logs(trainer, pl_module, "valid")







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=1    ,				lowercase_=False		)		->	Dict:
 if metric == "rouge2":
  A__ =       "{val_avg_rouge2:.4f}-{step_count}"
 elif metric == "bleu":
  A__ =       "{val_avg_bleu:.4f}-{step_count}"
 elif metric == "loss":
  A__ =       "{val_avg_loss:.4f}-{step_count}"
 else:
  raise NotImplementedError(
      f"""seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"""
      " this function."		)

 A__ =       ModelCheckpoint(
     dirpath=lowercase_    ,				filename=lowercase_    ,				monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				save_top_k=lowercase_    ,				)
 return checkpoint_callback







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	Union[str, Any]:
 return EarlyStopping(
     monitor=f"""val_{metric}"""    ,				mode="min" if "loss" in metric else "max"    ,				patience=lowercase_    ,				verbose=lowercase_    ,				)





