'''simple docstring'''

import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


UpperCamelCase__     :	Union[str, Any]				=     re.compile(r'\b(a|an|the)\b', re.UNICODE)

UpperCamelCase__     :	List[Any]				=     None







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''    )
   parser.add_argument('''data_file'''  ,			metavar='''data.json'''  ,			help='''Input data JSON file.'''    )
   parser.add_argument('''pred_file'''  ,			metavar='''pred.json'''  ,			help='''Model predictions.'''    )
   parser.add_argument(
       '''--out-file'''  ,			'''-o'''  ,			metavar='''eval.json'''  ,			help='''Write accuracy metrics to file (default is stdout).'''    )
   parser.add_argument(
       '''--na-prob-file'''  ,			'''-n'''  ,			metavar='''na_prob.json'''  ,			help='''Model estimates of probability of no answer.'''    )
   parser.add_argument(
       '''--na-prob-thresh'''  ,			'''-t'''  ,			type=_A  ,			default=1.0  ,			help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''  ,			)
   parser.add_argument(
       '''--out-image-dir'''  ,			'''-p'''  ,			metavar='''out_images'''  ,			default=_A  ,			help='''Save precision-recall curves to directory.'''    )
   parser.add_argument('''--verbose'''  ,			'''-v'''  ,			action='''store_true'''    )
   if len(sys.argv    ) == 1:
      parser.print_help()
      sys.exit(1    )
   return parser.parse_args()







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				Union[str, Any]   				=  bool(qa['''answers''']['''text''']    )
   return qid_to_has_ans







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''

   def remove_articles(_A					:				Union[str, Any]    ):
      return ARTICLES_REGEX.sub(''' '''  ,			_A    )

   def white_space_fix(_A					:				Optional[int]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Optional[Any]    ):
      UpperCAmelCase__							:				Any   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Union[str, Any]    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   if not s:
      return []
   return normalize_answer(_A    ).split()







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str    ):
   '''simple docstring'''
   return int(normalize_answer(_A    ) == normalize_answer(_A    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Tuple   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Any   				=  collections.Counter(_A    ) & collections.Counter(_A    )
   UpperCAmelCase__							:				List[Any]   				=  sum(common.values()    )
   if len(_A    ) == 0 or len(_A    ) == 0:
      # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
      return int(gold_toks == pred_toks    )
   if num_same == 0:
      return 0
   UpperCAmelCase__							:				Optional[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Tuple   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  {}
   UpperCAmelCase__							:				Any   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				str   				=  qa['''id''']
            UpperCAmelCase__							:				List[Any]   				=  [t for t in qa['''answers''']['''text'''] if normalize_answer(_A    )]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               UpperCAmelCase__							:				Tuple   				=  ['''''']
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''    )
               continue
            UpperCAmelCase__							:				Union[str, Any]   				=  preds[qid]
            # Take max over all gold answers
            UpperCAmelCase__							:				List[str]   				=  max(compute_exact(_A  ,			_A    ) for a in gold_answers    )
            UpperCAmelCase__							:				List[str]   				=  max(compute_fa(_A  ,			_A    ) for a in gold_answers    )
   return exact_scores, fa_scores







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {}
   for qid, s in scores.items():
      UpperCAmelCase__							:				Dict   				=  na_probs[qid] > na_prob_thresh
      if pred_na:
         UpperCAmelCase__							:				Any   				=  float(not qid_to_has_ans[qid]    )
      else:
         UpperCAmelCase__							:				List[str]   				=  s
   return new_scores







def __UpperCamelCase(   _A					:				str  ,			_A					:				Optional[Any]  ,			_A					:				Any=None    ):
   '''simple docstring'''
   if not qid_list:
      UpperCAmelCase__							:				List[Any]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_00.0 * sum(exact_scores.values()    ) / total),
              ('''f1''', 1_00.0 * sum(fa_scores.values()    ) / total),
              ('''total''', total),
          ]    )
   else:
      UpperCAmelCase__							:				List[str]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_00.0 * sum(exact_scores[k] for k in qid_list    ) / total),
              ('''f1''', 1_00.0 * sum(fa_scores[k] for k in qid_list    ) / total),
              ('''total''', total),
          ]    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   for k in new_eval:
      UpperCAmelCase__							:				List[str]   				=  new_eval[k]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				int    ):
   '''simple docstring'''
   plt.step(_A  ,			_A  ,			color='''b'''  ,			alpha=0.2  ,			where='''post'''    )
   plt.fill_between(_A  ,			_A  ,			step='''post'''  ,			alpha=0.2  ,			color='''b'''    )
   plt.xlabel('''Recall'''    )
   plt.ylabel('''Precision'''    )
   plt.xlim([0.0, 1.0_5]    )
   plt.ylim([0.0, 1.0_5]    )
   plt.title(_A    )
   plt.savefig(_A    )
   plt.clf()







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Tuple  ,			_A					:				Any  ,			_A					:				Any  ,			_A					:				Tuple=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   UpperCAmelCase__							:				Tuple   				=  0.0
   UpperCAmelCase__							:				Any   				=  1.0
   UpperCAmelCase__							:				Any   				=  0.0
   UpperCAmelCase__							:				Union[str, Any]   				=  [1.0]
   UpperCAmelCase__							:				int   				=  [0.0]
   UpperCAmelCase__							:				Optional[Any]   				=  0.0
   for i, qid in enumerate(_A    ):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      UpperCAmelCase__							:				Optional[Any]   				=  true_pos / float(i + 1    )
      UpperCAmelCase__							:				int   				=  true_pos / float(_A    )
      if i == len(_A    ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
         # i.e., if we can put a threshold after this point
         avg_prec += cur_p * (cur_r - recalls[-1])
         precisions.append(_A    )
         recalls.append(_A    )
   if out_image:
      plot_pr_curve(_A  ,			_A  ,			_A  ,			_A    )
   return {"ap": 1_00.0 * avg_prec}







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Any  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   if out_image_dir and not os.path.exists(_A    ):
      os.makedirs(_A    )
   UpperCAmelCase__							:				List[str]   				=  sum(1 for v in qid_to_has_ans.values() if v    )
   if num_true_pos == 0:
      return
   UpperCAmelCase__							:				Dict   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_exact.png'''    )  ,			title='''Precision-Recall curve for Exact Match score'''  ,			)
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_f1.png'''    )  ,			title='''Precision-Recall curve for F1 score'''  ,			)
   UpperCAmelCase__							:				Tuple   				=  {k: float(_A    ) for k, v in qid_to_has_ans.items()}
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_oracle.png'''    )  ,			title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''  ,			)
   merge_eval(_A  ,			_A  ,			'''pr_exact'''    )
   merge_eval(_A  ,			_A  ,			'''pr_f1'''    )
   merge_eval(_A  ,			_A  ,			'''pr_oracle'''    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict  ,			_A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   if not qid_list:
      return
   UpperCAmelCase__							:				Optional[Any]   				=  [na_probs[k] for k in qid_list]
   UpperCAmelCase__							:				Union[str, Any]   				=  np.ones_like(_A    ) / float(len(_A    )    )
   plt.hist(_A  ,			weights=_A  ,			bins=20  ,			range=(0.0, 1.0)    )
   plt.xlabel('''Model probability of no-answer'''    )
   plt.ylabel('''Proportion of dataset'''    )
   plt.title(F'''Histogram of no-answer probability: {name}'''    )
   plt.savefig(os.path.join(_A  ,			F'''na_prob_hist_{name}.png'''    )    )
   plt.clf()







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[str]  ,			_A					:				Optional[int]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
   UpperCAmelCase__							:				List[str]   				=  num_no_ans
   UpperCAmelCase__							:				Any   				=  cur_score
   UpperCAmelCase__							:				List[str]   				=  0.0
   UpperCAmelCase__							:				Dict   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   for i, qid in enumerate(_A    ):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         UpperCAmelCase__							:				int   				=  scores[qid]
      else:
         if preds[qid]:
            UpperCAmelCase__							:				Any   				=  -1
         else:
            UpperCAmelCase__							:				Dict   				=  0
      cur_score += diff
      if cur_score > best_score:
         UpperCAmelCase__							:				Optional[Any]   				=  cur_score
         UpperCAmelCase__							:				Tuple   				=  na_probs[qid]
   return 1_00.0 * best_score / len(_A    ), best_thresh







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				int  ,			_A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  best_exact
   UpperCAmelCase__							:				Any   				=  exact_thresh
   UpperCAmelCase__							:				Dict   				=  best_fa
   UpperCAmelCase__							:				Dict   				=  fa_thresh







def __UpperCamelCase(   ):
   '''simple docstring'''
   with open(OPTS.data_file    ) as f:
      UpperCAmelCase__							:				Dict   				=  json.load(_A    )
      UpperCAmelCase__							:				str   				=  dataset_json['''data''']
   with open(OPTS.pred_file    ) as f:
      UpperCAmelCase__							:				Any   				=  json.load(_A    )
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file    ) as f:
         UpperCAmelCase__							:				Optional[Any]   				=  json.load(_A    )
   else:
      UpperCAmelCase__							:				Dict   				=  {k: 0.0 for k in preds}
   UpperCAmelCase__							:				int   				=  make_qid_to_has_ans(_A    )  # maps qid to True/False
   UpperCAmelCase__							:				Any   				=  [k for k, v in qid_to_has_ans.items() if v]
   UpperCAmelCase__							:				Optional[Any]   				=  [k for k, v in qid_to_has_ans.items() if not v]
   UpperCAmelCase__							:				Union[str, Any]   				=  get_raw_scores(_A  ,			_A    )
   UpperCAmelCase__							:				Optional[int]   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Dict   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Union[str, Any]   				=  make_eval_dict(_A  ,			_A    )
   if has_ans_qids:
      UpperCAmelCase__							:				Optional[int]   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''HasAns'''    )
   if no_ans_qids:
      UpperCAmelCase__							:				Dict   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''NoAns'''    )
   if OPTS.na_prob_file:
      find_all_best_thresh(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			_A    )
   if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			OPTS.out_image_dir    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''hasAns'''    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''noAns'''    )
   if OPTS.out_file:
      with open(OPTS.out_file  ,			'''w'''    ) as f:
         json.dump(_A  ,			_A    )
   else:
      print(json.dumps(_A  ,			indent=2    )    )


if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=     parse_args()
  if OPTS.out_image_dir:
    import matplotlib

    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
  main()





'''simple docstring'''

import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


UpperCamelCase__     :	Union[str, Any]				=			re.compile(r'\b(a|an|the)\b', re.UNICODE)

UpperCamelCase__     :	List[Any]				=			None







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''    )
   parser.add_argument('''data_file'''  ,			metavar='''data.json'''  ,			help='''Input data JSON file.'''    )
   parser.add_argument('''pred_file'''  ,			metavar='''pred.json'''  ,			help='''Model predictions.'''    )
   parser.add_argument(
       '''--out-file'''  ,			'''-o'''  ,			metavar='''eval.json'''  ,			help='''Write accuracy metrics to file (default is stdout).'''    )
   parser.add_argument(
       '''--na-prob-file'''  ,			'''-n'''  ,			metavar='''na_prob.json'''  ,			help='''Model estimates of probability of no answer.'''    )
   parser.add_argument(
       '''--na-prob-thresh'''  ,			'''-t'''  ,			type=_A  ,			default=1.0  ,			help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''  ,			)
   parser.add_argument(
       '''--out-image-dir'''  ,			'''-p'''  ,			metavar='''out_images'''  ,			default=_A  ,			help='''Save precision-recall curves to directory.'''    )
   parser.add_argument('''--verbose'''  ,			'''-v'''  ,			action='''store_true'''    )
   if len(sys.argv    ) == 1:
      parser.print_help()
      sys.exit(1    )
   return parser.parse_args()







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				Union[str, Any]   				=  bool(qa['''answers''']['''text''']    )
   return qid_to_has_ans







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''

   def remove_articles(_A					:				Union[str, Any]    ):
      return ARTICLES_REGEX.sub(''' '''  ,			_A    )

   def white_space_fix(_A					:				Optional[int]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Optional[Any]    ):
      UpperCAmelCase__							:				Any   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Union[str, Any]    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   if not s:
      return []
   return normalize_answer(_A    ).split()







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str    ):
   '''simple docstring'''
   return int(normalize_answer(_A    ) == normalize_answer(_A    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Tuple   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Any   				=  collections.Counter(_A    ) & collections.Counter(_A    )
   UpperCAmelCase__							:				List[Any]   				=  sum(common.values()    )
   if len(_A    ) == 0 or len(_A    ) == 0:
      # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
      return int(gold_toks == pred_toks    )
   if num_same == 0:
      return 0
   UpperCAmelCase__							:				Optional[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Tuple   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  {}
   UpperCAmelCase__							:				Any   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				str   				=  qa['''id''']
            UpperCAmelCase__							:				List[Any]   				=  [t for t in qa['''answers''']['''text'''] if normalize_answer(_A    )]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               UpperCAmelCase__							:				Tuple   				=  ['''''']
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''    )
               continue
            UpperCAmelCase__							:				Union[str, Any]   				=  preds[qid]
            # Take max over all gold answers
            UpperCAmelCase__							:				List[str]   				=  max(compute_exact(_A  ,			_A    ) for a in gold_answers    )
            UpperCAmelCase__							:				List[str]   				=  max(compute_fa(_A  ,			_A    ) for a in gold_answers    )
   return exact_scores, fa_scores







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {}
   for qid, s in scores.items():
      UpperCAmelCase__							:				Dict   				=  na_probs[qid] > na_prob_thresh
      if pred_na:
         UpperCAmelCase__							:				Any   				=  float(not qid_to_has_ans[qid]    )
      else:
         UpperCAmelCase__							:				List[str]   				=  s
   return new_scores







def __UpperCamelCase(   _A					:				str  ,			_A					:				Optional[Any]  ,			_A					:				Any=None    ):
   '''simple docstring'''
   if not qid_list:
      UpperCAmelCase__							:				List[Any]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores.values()    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores.values()    ) / total),
              ('''total''', total),
          ]    )
   else:
      UpperCAmelCase__							:				List[str]   				=  len(_A    )
      return collections.OrderedDict(
          [
              ('''exact''', 1_0_0.0 * sum(exact_scores[k] for k in qid_list    ) / total),
              ('''f1''', 1_0_0.0 * sum(fa_scores[k] for k in qid_list    ) / total),
              ('''total''', total),
          ]    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   for k in new_eval:
      UpperCAmelCase__							:				List[str]   				=  new_eval[k]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				int    ):
   '''simple docstring'''
   plt.step(_A  ,			_A  ,			color='''b'''  ,			alpha=0.2  ,			where='''post'''    )
   plt.fill_between(_A  ,			_A  ,			step='''post'''  ,			alpha=0.2  ,			color='''b'''    )
   plt.xlabel('''Recall'''    )
   plt.ylabel('''Precision'''    )
   plt.xlim([0.0, 1.0_5]    )
   plt.ylim([0.0, 1.0_5]    )
   plt.title(_A    )
   plt.savefig(_A    )
   plt.clf()







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Tuple  ,			_A					:				Any  ,			_A					:				Any  ,			_A					:				Tuple=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   UpperCAmelCase__							:				Tuple   				=  0.0
   UpperCAmelCase__							:				Any   				=  1.0
   UpperCAmelCase__							:				Any   				=  0.0
   UpperCAmelCase__							:				Union[str, Any]   				=  [1.0]
   UpperCAmelCase__							:				int   				=  [0.0]
   UpperCAmelCase__							:				Optional[Any]   				=  0.0
   for i, qid in enumerate(_A    ):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      UpperCAmelCase__							:				Optional[Any]   				=  true_pos / float(i + 1    )
      UpperCAmelCase__							:				int   				=  true_pos / float(_A    )
      if i == len(_A    ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
         # i.e., if we can put a threshold after this point
         avg_prec += cur_p * (cur_r - recalls[-1])
         precisions.append(_A    )
         recalls.append(_A    )
   if out_image:
      plot_pr_curve(_A  ,			_A  ,			_A  ,			_A    )
   return {"ap": 1_0_0.0 * avg_prec}







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Any  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   if out_image_dir and not os.path.exists(_A    ):
      os.makedirs(_A    )
   UpperCAmelCase__							:				List[str]   				=  sum(1 for v in qid_to_has_ans.values() if v    )
   if num_true_pos == 0:
      return
   UpperCAmelCase__							:				Dict   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_exact.png'''    )  ,			title='''Precision-Recall curve for Exact Match score'''  ,			)
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_f1.png'''    )  ,			title='''Precision-Recall curve for F1 score'''  ,			)
   UpperCAmelCase__							:				Tuple   				=  {k: float(_A    ) for k, v in qid_to_has_ans.items()}
   UpperCAmelCase__							:				Any   				=  make_precision_recall_eval(
       _A  ,			_A  ,			_A  ,			_A  ,			out_image=os.path.join(_A  ,			'''pr_oracle.png'''    )  ,			title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''  ,			)
   merge_eval(_A  ,			_A  ,			'''pr_exact'''    )
   merge_eval(_A  ,			_A  ,			'''pr_f1'''    )
   merge_eval(_A  ,			_A  ,			'''pr_oracle'''    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict  ,			_A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   if not qid_list:
      return
   UpperCAmelCase__							:				Optional[Any]   				=  [na_probs[k] for k in qid_list]
   UpperCAmelCase__							:				Union[str, Any]   				=  np.ones_like(_A    ) / float(len(_A    )    )
   plt.hist(_A  ,			weights=_A  ,			bins=20  ,			range=(0.0, 1.0)    )
   plt.xlabel('''Model probability of no-answer'''    )
   plt.ylabel('''Proportion of dataset'''    )
   plt.title(F'''Histogram of no-answer probability: {name}'''    )
   plt.savefig(os.path.join(_A  ,			F'''na_prob_hist_{name}.png'''    )    )
   plt.clf()







def __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[str]  ,			_A					:				Optional[int]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
   UpperCAmelCase__							:				List[str]   				=  num_no_ans
   UpperCAmelCase__							:				Any   				=  cur_score
   UpperCAmelCase__							:				List[str]   				=  0.0
   UpperCAmelCase__							:				Dict   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   for i, qid in enumerate(_A    ):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         UpperCAmelCase__							:				int   				=  scores[qid]
      else:
         if preds[qid]:
            UpperCAmelCase__							:				Any   				=  -1
         else:
            UpperCAmelCase__							:				Dict   				=  0
      cur_score += diff
      if cur_score > best_score:
         UpperCAmelCase__							:				Optional[Any]   				=  cur_score
         UpperCAmelCase__							:				Tuple   				=  na_probs[qid]
   return 1_0_0.0 * best_score / len(_A    ), best_thresh







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				int  ,			_A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  best_exact
   UpperCAmelCase__							:				Any   				=  exact_thresh
   UpperCAmelCase__							:				Dict   				=  best_fa
   UpperCAmelCase__							:				Dict   				=  fa_thresh







def __UpperCamelCase(   ):
   '''simple docstring'''
   with open(OPTS.data_file    ) as f:
      UpperCAmelCase__							:				Dict   				=  json.load(_A    )
      UpperCAmelCase__							:				str   				=  dataset_json['''data''']
   with open(OPTS.pred_file    ) as f:
      UpperCAmelCase__							:				Any   				=  json.load(_A    )
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file    ) as f:
         UpperCAmelCase__							:				Optional[Any]   				=  json.load(_A    )
   else:
      UpperCAmelCase__							:				Dict   				=  {k: 0.0 for k in preds}
   UpperCAmelCase__							:				int   				=  make_qid_to_has_ans(_A    )  # maps qid to True/False
   UpperCAmelCase__							:				Any   				=  [k for k, v in qid_to_has_ans.items() if v]
   UpperCAmelCase__							:				Optional[Any]   				=  [k for k, v in qid_to_has_ans.items() if not v]
   UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  get_raw_scores(_A  ,			_A    )
   UpperCAmelCase__							:				Optional[int]   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Dict   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Union[str, Any]   				=  make_eval_dict(_A  ,			_A    )
   if has_ans_qids:
      UpperCAmelCase__							:				Optional[int]   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''HasAns'''    )
   if no_ans_qids:
      UpperCAmelCase__							:				Dict   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''NoAns'''    )
   if OPTS.na_prob_file:
      find_all_best_thresh(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			_A    )
   if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(_A  ,			_A  ,			_A  ,			_A  ,			_A  ,			OPTS.out_image_dir    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''hasAns'''    )
      histogram_na_prob(_A  ,			_A  ,			OPTS.out_image_dir  ,			'''noAns'''    )
   if OPTS.out_file:
      with open(OPTS.out_file  ,			'''w'''    ) as f:
         json.dump(_A  ,			_A    )
   else:
      print(json.dumps(_A  ,			indent=2    )    )


if __name__ == "__main__":
  UpperCamelCase__     :	Tuple				=			parse_args()
  if OPTS.out_image_dir:
    import matplotlib

    matplotlib.use('Agg')
    import matplotlib.pyplot as plt
  main()




