


from datetime import datetime as dt
import os

from github import Github


__lowerCamelCase        =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]



def  _a			(						):
     a_    :     List[str]										=    Github(os.environ["""GITHUB_TOKEN"""] )
     a_    :     str										=    g.get_repo("""huggingface/transformers""" )
     a_    :     List[str]										=    repo.get_issues(state="""open""" )

     for issue in open_issues:
          a_    :     Optional[Any]										=    sorted([comment for comment in issue.get_comments()]  ,  key=lambda __UpperCamelCase : i.created_at  ,  reverse=__UpperCamelCase )
          a_    :     List[Any]										=    comments[0] if len(__UpperCamelCase ) > 0 else None
          if (
              last_comment is not None
              and last_comment.user.login == "github-actions[bot]"
              and (dt.utcnow() - issue.updated_at).days > 7
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
               issue.edit(state="""closed""" )
          elif (
              (dt.utcnow() - issue.updated_at).days > 2_3
              and (dt.utcnow() - issue.created_at).days >= 3_0
              and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
          ):
               # print(f"Would add stale comment to {issue.number}")
               issue.create_comment(
                   """This issue has been automatically marked as stale because it has not had """
                   """recent activity. If you think this still needs to be addressed """
                   """please comment on this thread.\n\nPlease note that issues that do not follow the """
                   """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                   """are likely to be ignored.""" )


if __name__ == "__main__":
       main()







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

import numpy as np


__lowerCamelCase        =     re.compile(R'''\b(a|an|the)\b''', re.UNICODE)

__lowerCamelCase        =     None



def  _a			(						):
     a_    :     Tuple										=    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=__UpperCamelCase  ,  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=__UpperCamelCase  ,  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  _a			(						__UpperCamelCase ):
     a_    :     Union[str, Any]										=    {}
     for article in dataset:
          for p in article["paragraphs"]:
               for qa in p["qas"]:
                    a_    :     Union[str, Any]										=    bool(qa["""answers"""]["""text"""] )
     return qid_to_has_ans



def  _a			(						__UpperCamelCase ):

     def remove_articles(__UpperCamelCase ):
          return ARTICLES_REGEX.sub(""" """  ,  __UpperCamelCase )

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

     def remove_punc(__UpperCamelCase ):
          a_    :     List[str]										=    set(string.punctuation )
          return "".join(ch for ch in text if ch not in exclude )

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

     return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )



def  _a			(						__UpperCamelCase ):
     if not s:
          return []
     return normalize_answer(__UpperCamelCase ).split()



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     return int(normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase ) )



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[Any]										=    get_tokens(__UpperCamelCase )
     a_    :     str										=    get_tokens(__UpperCamelCase )
     a_    :     Tuple										=    collections.Counter(__UpperCamelCase ) & collections.Counter(__UpperCamelCase )
     a_    :     str										=    sum(common.values() )
     if len(__UpperCamelCase ) == 0 or len(__UpperCamelCase ) == 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
     a_    :     Any										=    1.0 * num_same / len(__UpperCamelCase )
     a_    :     Optional[int]										=    1.0 * num_same / len(__UpperCamelCase )
     a_    :     List[Any]										=    (2 * precision * recall) / (precision + recall)
     return fa



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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Optional[Any]										=    {}
     for qid, s in scores.items():
          a_    :     Optional[int]										=    na_probs[qid] > na_prob_thresh
          if pred_na:
               a_    :     List[Any]										=    float(not qid_to_has_ans[qid] )
          else:
               a_    :     List[Any]										=    s
     return new_scores



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None ):
     if not qid_list:
          a_    :     List[Any]										=    len(__UpperCamelCase )
          return collections.OrderedDict(
              [
                  ("""exact""", 1_00.0 * sum(exact_scores.values() ) / total),
                  ("""f1""", 1_00.0 * sum(fa_scores.values() ) / total),
                  ("""total""", total),
              ] )
     else:
          a_    :     Tuple										=    len(__UpperCamelCase )
          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  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     for k in new_eval:
          a_    :     Any										=    new_eval[k]



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     plt.step(__UpperCamelCase  ,  __UpperCamelCase  ,  color="""b"""  ,  alpha=0.2  ,  where="""post""" )
     plt.fill_between(__UpperCamelCase  ,  __UpperCamelCase  ,  step="""post"""  ,  alpha=0.2  ,  color="""b""" )
     plt.xlabel("""Recall""" )
     plt.ylabel("""Precision""" )
     plt.xlim([0.0, 1.05] )
     plt.ylim([0.0, 1.05] )
     plt.title(__UpperCamelCase )
     plt.savefig(__UpperCamelCase )
     plt.clf()



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None ):
     a_    :     List[Any]										=    sorted(__UpperCamelCase  ,  key=lambda __UpperCamelCase : na_probs[k] )
     a_    :     int										=    0.0
     a_    :     Tuple										=    1.0
     a_    :     Union[str, Any]										=    0.0
     a_    :     List[str]										=    [1.0]
     a_    :     int										=    [0.0]
     a_    :     Optional[int]										=    0.0
     for i, qid in enumerate(__UpperCamelCase ):
          if qid_to_has_ans[qid]:
               true_pos += scores[qid]
          a_    :     Union[str, Any]										=    true_pos / float(i + 1 )
          a_    :     Tuple										=    true_pos / float(__UpperCamelCase )
          if i == len(__UpperCamelCase ) - 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(__UpperCamelCase )
               recalls.append(__UpperCamelCase )
     if out_image:
          plot_pr_curve(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
     return {"ap": 1_00.0 * avg_prec}



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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     if not qid_list:
          return
     a_    :     List[Any]										=    [na_probs[k] for k in qid_list]
     a_    :     List[Any]										=    np.ones_like(__UpperCamelCase ) / float(len(__UpperCamelCase ) )
     plt.hist(__UpperCamelCase  ,  weights=__UpperCamelCase  ,  bins=2_0  ,  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(__UpperCamelCase  ,  F'''na_prob_hist_{name}.png''' ) )
     plt.clf()



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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_			,			a_    :     Optional[int]										=    find_best_thresh(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
     a_			,			a_    :     Optional[Any]										=    find_best_thresh(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
     a_    :     Dict										=    best_exact
     a_    :     Optional[Any]										=    exact_thresh
     a_    :     Optional[Any]										=    best_fa
     a_    :     Union[str, Any]										=    fa_thresh



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


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

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



