
'''simple docstring'''

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

import numpy as np


UpperCamelCase_   	=				re.compile(r"""\b(a|an|the)\b""", re.UNICODE)

UpperCamelCase_   	=				None


def 		_UpperCAmelCase (     )			->  Union[str, Any]:
  _lowerCAmelCase				:     List[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 (     _lowerCamelCase  :				Any						)			->  List[str]:
  _lowerCAmelCase				:     Union[str, Any]       =    {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        _lowerCAmelCase				:     Optional[Any]       =    bool(qa["""answers"""]["""text"""]						)
  return qid_to_has_ans


def 		_UpperCAmelCase (     _lowerCamelCase  :				Union[str, Any]						)			->  Any:



  def remove_articles(_lowerCamelCase  :				List[Any]						):
    return ARTICLES_REGEX.sub(""" """     ,					A__						)

  def white_space_fix(_lowerCamelCase  :				List[Any]						):
    return " ".join(text.split()						)

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

  def lower(_lowerCamelCase  :				Dict						):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(A__						)						)						)						)


def 		_UpperCAmelCase (     _lowerCamelCase  :				Union[str, Any]						)			->  Union[str, Any]:



  if not s:
    return []
  return normalize_answer(A__						).split()


def 		_UpperCAmelCase (     _lowerCamelCase  :				int     ,					_lowerCamelCase  :				Any						)			->  Optional[int]:
  return int(normalize_answer(A__						) == normalize_answer(A__						)						)


def 		_UpperCAmelCase (     _lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				str						)			->  Tuple:
  _lowerCAmelCase				:     str       =    get_tokens(A__						)
  _lowerCAmelCase				:     Tuple       =    get_tokens(A__						)
  _lowerCAmelCase				:     List[str]       =    collections.Counter(A__						) & collections.Counter(A__						)
  _lowerCAmelCase				:     Dict       =    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
  _lowerCAmelCase				:     Any       =    1.0 * num_same / len(A__						)
  _lowerCAmelCase				:     Union[str, Any]       =    1.0 * num_same / len(A__						)
  _lowerCAmelCase				:     Any       =    (2 * precision * recall) / (precision + recall)
  return fa


def 		_UpperCAmelCase (     _lowerCamelCase  :				Optional[Any]     ,					_lowerCamelCase  :				Dict						)			->  Tuple:
  _lowerCAmelCase				:     Any       =    {}
  _lowerCAmelCase				:     Any       =    {}
  for article in dataset:
    for p in article["paragraphs"]:
      for qa in p["qas"]:
        _lowerCAmelCase				:     List[str]       =    qa["""id"""]
        _lowerCAmelCase				:     Optional[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
          _lowerCAmelCase				:     List[str]       =    [""""""]
        if qid not in preds:
          print(f'Missing prediction for {qid}'						)
          continue
        _lowerCAmelCase				:     List[str]       =    preds[qid]
        # Take max over all gold answers
        _lowerCAmelCase				:     List[str]       =    max(compute_exact(A__     ,					A__						) for a in gold_answers						)
        _lowerCAmelCase				:     Any       =    max(compute_fa(A__     ,					A__						) for a in gold_answers						)
  return exact_scores, fa_scores


def 		_UpperCAmelCase (     _lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				str     ,					_lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				Any						)			->  Union[str, Any]:
  _lowerCAmelCase				:     int       =    {}
  for qid, s in scores.items():
    _lowerCAmelCase				:     int       =    na_probs[qid] > na_prob_thresh
    if pred_na:
      _lowerCAmelCase				:     Union[str, Any]       =    float(not qid_to_has_ans[qid]						)
    else:
      _lowerCAmelCase				:     int       =    s
  return new_scores


def 		_UpperCAmelCase (     _lowerCamelCase  :				Optional[int]     ,					_lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				str=None						)			->  int:



  if not qid_list:
    _lowerCAmelCase				:     Optional[Any]       =    len(A__						)
    return collections.OrderedDict(
        [
            ("""exact""", 100.0 * sum(exact_scores.values()						) / total),
            ("""f1""", 100.0 * sum(fa_scores.values()						) / total),
            ("""total""", total),
        ]						)
  else:
    _lowerCAmelCase				:     Union[str, Any]       =    len(A__						)
    return collections.OrderedDict(
        [
            ("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list						) / total),
            ("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list						) / total),
            ("""total""", total),
        ]						)


def 		_UpperCAmelCase (     _lowerCamelCase  :				List[Any]     ,					_lowerCamelCase  :				Union[str, Any]     ,					_lowerCamelCase  :				Tuple						)			->  int:



  for k in new_eval:
    _lowerCAmelCase				:     Tuple       =    new_eval[k]


def 		_UpperCAmelCase (     _lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				Optional[int]						)			->  List[str]:
  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, 1.05]						)
  plt.ylim([0.0, 1.05]						)
  plt.title(A__						)
  plt.savefig(A__						)
  plt.clf()


def 		_UpperCAmelCase (     _lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				List[str]     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				Dict     ,					_lowerCamelCase  :				Any=None     ,					_lowerCamelCase  :				Optional[Any]=None						)			->  str:
  _lowerCAmelCase				:     Optional[int]       =    sorted(A__     ,					key=lambda _lowerCamelCase						: na_probs[k]						)
  _lowerCAmelCase				:     List[str]       =    0.0
  _lowerCAmelCase				:     str       =    1.0
  _lowerCAmelCase				:     Optional[int]       =    0.0
  _lowerCAmelCase				:     Union[str, Any]       =    [1.0]
  _lowerCAmelCase				:     str       =    [0.0]
  _lowerCAmelCase				:     Tuple       =    0.0
  for i, qid in enumerate(A__						):
    if qid_to_has_ans[qid]:
      true_pos += scores[qid]
    _lowerCAmelCase				:     Union[str, Any]       =    true_pos / float(i + 1						)
    _lowerCAmelCase				:     Any       =    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": 100.0 * avg_prec}


def 		_UpperCAmelCase (     _lowerCamelCase  :				str     ,					_lowerCamelCase  :				Union[str, Any]     ,					_lowerCamelCase  :				Optional[int]     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				Optional[Any]						)			->  Optional[Any]:



  if out_image_dir and not os.path.exists(A__						):
    os.makedirs(A__						)
  _lowerCAmelCase				:     List[str]       =    sum(1 for v in qid_to_has_ans.values() if v						)
  if num_true_pos == 0:
    return
  _lowerCAmelCase				:     List[str]       =    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"""     ,					)
  _lowerCAmelCase				:     Dict       =    make_precision_recall_eval(
      A__     ,					A__     ,					A__     ,					A__     ,					out_image=os.path.join(A__     ,					"""pr_f1.png"""						)     ,					title="""Precision-Recall curve for F1 score"""     ,					)
  _lowerCAmelCase				:     Union[str, Any]       =    {k: float(A__						) for k, v in qid_to_has_ans.items()}
  _lowerCAmelCase				:     str       =    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 (     _lowerCamelCase  :				Optional[Any]     ,					_lowerCamelCase  :				Union[str, Any]     ,					_lowerCamelCase  :				int     ,					_lowerCamelCase  :				int						)			->  List[Any]:



  if not qid_list:
    return
  _lowerCAmelCase				:     Any       =    [na_probs[k] for k in qid_list]
  _lowerCAmelCase				:     int       =    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 (     _lowerCamelCase  :				Union[str, Any]     ,					_lowerCamelCase  :				Optional[int]     ,					_lowerCamelCase  :				Tuple     ,					_lowerCamelCase  :				int						)			->  Union[str, Any]:
  _lowerCAmelCase				:     Optional[Any]       =    sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]						)
  _lowerCAmelCase				:     int       =    num_no_ans
  _lowerCAmelCase				:     Tuple       =    cur_score
  _lowerCAmelCase				:     int       =    0.0
  _lowerCAmelCase				:     Tuple       =    sorted(A__     ,					key=lambda _lowerCamelCase						: na_probs[k]						)
  for i, qid in enumerate(A__						):
    if qid not in scores:
      continue
    if qid_to_has_ans[qid]:
      _lowerCAmelCase				:     int       =    scores[qid]
    else:
      if preds[qid]:
        _lowerCAmelCase				:     Optional[int]       =    -1
      else:
        _lowerCAmelCase				:     Optional[int]       =    0
    cur_score += diff
    if cur_score > best_score:
      _lowerCAmelCase				:     List[Any]       =    cur_score
      _lowerCAmelCase				:     str       =    na_probs[qid]
  return 100.0 * best_score / len(A__						), best_thresh


def 		_UpperCAmelCase (     _lowerCamelCase  :				str     ,					_lowerCamelCase  :				List[str]     ,					_lowerCamelCase  :				Optional[Any]     ,					_lowerCamelCase  :				Any     ,					_lowerCamelCase  :				str     ,					_lowerCamelCase  :				int						)			->  Any:
  _lowerCAmelCase ,    _lowerCAmelCase				:     Dict       =    find_best_thresh(A__     ,					A__     ,					A__     ,					A__						)
  _lowerCAmelCase ,    _lowerCAmelCase				:     List[str]       =    find_best_thresh(A__     ,					A__     ,					A__     ,					A__						)
  _lowerCAmelCase				:     Union[str, Any]       =    best_exact
  _lowerCAmelCase				:     List[str]       =    exact_thresh
  _lowerCAmelCase				:     Optional[int]       =    best_fa
  _lowerCAmelCase				:     Dict       =    fa_thresh


def 		_UpperCAmelCase (     )			->  List[Any]:



  with open(OPTS.data_file						) as f:
    _lowerCAmelCase				:     Union[str, Any]       =    json.load(A__						)
    _lowerCAmelCase				:     Tuple       =    dataset_json["""data"""]
  with open(OPTS.pred_file						) as f:
    _lowerCAmelCase				:     Optional[int]       =    json.load(A__						)
  if OPTS.na_prob_file:
    with open(OPTS.na_prob_file						) as f:
      _lowerCAmelCase				:     Union[str, Any]       =    json.load(A__						)
  else:
    _lowerCAmelCase				:     Union[str, Any]       =    {k: 0.0 for k in preds}
  _lowerCAmelCase				:     Dict       =    make_qid_to_has_ans(A__						)  # maps qid to True/False
  _lowerCAmelCase				:     Dict       =    [k for k, v in qid_to_has_ans.items() if v]
  _lowerCAmelCase				:     List[str]       =    [k for k, v in qid_to_has_ans.items() if not v]
  _lowerCAmelCase ,    _lowerCAmelCase				:     Optional[int]       =    get_raw_scores(A__     ,					A__						)
  _lowerCAmelCase				:     Tuple       =    apply_no_ans_threshold(A__     ,					A__     ,					A__     ,					OPTS.na_prob_thresh						)
  _lowerCAmelCase				:     Tuple       =    apply_no_ans_threshold(A__     ,					A__     ,					A__     ,					OPTS.na_prob_thresh						)
  _lowerCAmelCase				:     int       =    make_eval_dict(A__     ,					A__						)
  if has_ans_qids:
    _lowerCAmelCase				:     str       =    make_eval_dict(A__     ,					A__     ,					qid_list=A__						)
    merge_eval(A__     ,					A__     ,					"""HasAns"""						)
  if no_ans_qids:
    _lowerCAmelCase				:     List[str]       =    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_   	=				parse_args()
     if OPTS.out_image_dir:
          import matplotlib

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








from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass





@dataclass
class     lowerCamelCase__:
      UpperCAmelCase__      :	int
      UpperCAmelCase__      :	TreeNode | None    		=   None
      UpperCAmelCase__      :	TreeNode | None    		=   None



UpperCAmelCase_									=	namedtuple('CoinsDistribResult', 'moves excess')




def 					lowerCamelCase__       (						A__       :					TreeNode | None	):





       '''simple docstring'''



       if root is None:
              return 0

       # Validation
       def count_nodes(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left	) + count_nodes(node.right	) + 1

       def count_coins(A__       :					TreeNode | None	) -> int:
              if node is None:
                     return 0

              return count_coins(node.left	) + count_coins(node.right	) + node.data

       if count_nodes(A__	) != count_coins(A__	):
              raise ValueError("""The nodes number should be same as the number of coins"""	)

       # Main calculation
       def get_distrib(A__       :					TreeNode | None	) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0  ,			1	)

              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.left	)
              __lowerCamelCase,   __lowerCamelCase   =						get_distrib(node.right	)

              __lowerCamelCase   =						1 - left_distrib_excess
              __lowerCamelCase   =						1 - right_distrib_excess

              __lowerCamelCase   =						(
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(A__	)
                  + abs(A__	)
              )
              __lowerCamelCase   =						node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(A__  ,			A__	)

       return get_distrib(A__	)[0]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

