


import argparse
import json
import re
import string
import sys
from collections import Counter
def 	_lowerCamelCase  (		A_  :	Union[str, Any]      )			->			List[Any]:

   '''simple docstring'''






   def remove_articles(A_  :	Union[str, Any]      ):
      return re.sub(R"\b(a|an|the)\b"							,  " "							,  lowerCamelCase__      )

   def white_space_fix(A_  :	int      ):
      return " ".join(text.split()      )

   def remove_punc(A_  :	Tuple      ):
      UpperCamelCase__ :			Any 						=set(string.punctuation      )
      return "".join(ch for ch in text if ch not in exclude      )

   def lower(A_  :	Any      ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase__      )      )      )      )
def 	_lowerCamelCase  (		A_  :	List[Any]							,  A_  :	Any      )			->			Optional[int]:

   '''simple docstring'''






   UpperCamelCase__ :			int 						=normalize_answer(lowerCamelCase__      ).split()
   UpperCamelCase__ :			Optional[int] 						=normalize_answer(lowerCamelCase__      ).split()
   UpperCamelCase__ :			List[str] 						=Counter(lowerCamelCase__      ) & Counter(lowerCamelCase__      )
   UpperCamelCase__ :			int 						=sum(common.values()      )
   if num_same == 0:
      return 0
   UpperCamelCase__ :			List[str] 						=1.0 * num_same / len(lowerCamelCase__      )
   UpperCamelCase__ :			Union[str, Any] 						=1.0 * num_same / len(lowerCamelCase__      )
   UpperCamelCase__ :			Dict 						=(2 * precision * recall) / (precision + recall)
   return fa
def 	_lowerCamelCase  (		A_  :	Optional[Any]							,  A_  :	Optional[int]      )			->			List[Any]:

   '''simple docstring'''






   return normalize_answer(lowerCamelCase__      ) == normalize_answer(lowerCamelCase__      )
def 	_lowerCamelCase  (		A_  :	Optional[Any]							,  A_  :	Dict							,  A_  :	str      )			->			Optional[int]:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=[]
   for ground_truth in ground_truths:
      UpperCamelCase__ :			Any 						=metric_fn(lowerCamelCase__							,  lowerCamelCase__      )
      scores_for_ground_truths.append(lowerCamelCase__      )
   return max(lowerCamelCase__      )
def 	_lowerCamelCase  (		A_  :	str							,  A_  :	Union[str, Any]      )			->			Optional[int]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=0
   UpperCamelCase__ :			Optional[Any] 						=[]
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            UpperCamelCase__ :			int 						=f'''Unanswered question {qa['id']} will receive score 0.'''
            print(lowerCamelCase__							,  file=sys.stderr      )
            continue

         UpperCamelCase__ :			Dict 						=[x['text'] for x in qa['answers']]
         UpperCamelCase__ :			Optional[int] 						=predictions[qa['id']]

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

         fa += metric_max_over_ground_truths(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__      )

   UpperCamelCase__ :			int 						=exact_match / total
   UpperCamelCase__ :			str 						=fa / total

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


if __name__ == "__main__":
     __UpperCAmelCase      			=  """1.0"""
     __UpperCAmelCase      			=  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.""")
     __UpperCAmelCase      			=  parser.parse_args()

     with open(args.data_file) as data_file:
          __UpperCAmelCase      			=  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,
               )
          __UpperCAmelCase      			=  dataset_json["""data"""]

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

     __UpperCAmelCase			,				__UpperCAmelCase      			=  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)









from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 # The first operation in a checkpoint can't be in-place, but it's
 # nice to have in-place addition during inference. Thus...
 if not inplace:
  lowerCamelCase_							:    Tuple							=     ma + ma
 else:
  ma += ma

 return ma




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    Union[str, Any]							=     -1 * len(lowerCamelCase__				)
 lowerCamelCase_							:    Optional[Any]							=     list(range(len(tensor.shape[:zero_index]				)				)				)
 return tensor.permute(first_inds + [zero_index + i for i in inds]				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 return t.reshape(t.shape[:-no_dims] + (-1,)				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 1e-4				)  ->      torch.Tensor:
 lowerCamelCase_							:    Optional[int]							=     mask.expand(*value.shape				)
 return torch.sum(mask * value ,			dim=lowerCamelCase__				) / (eps + torch.sum(lowerCamelCase__ ,			dim=lowerCamelCase__				))




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ = 2.3125 ,			lowerCamelCase__ = 21.6875 ,			lowerCamelCase__ = 64				)  ->      torch.Tensor:
 lowerCamelCase_							:    int							=     torch.linspace(lowerCamelCase__ ,			lowerCamelCase__ ,			no_bins - 1 ,			device=pts.device				)
 lowerCamelCase_							:    Tuple							=     torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2 ,			dim=-1				)				)
 return torch.bucketize(lowerCamelCase__ ,			lowerCamelCase__				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 lowerCamelCase_							:    Optional[Any]							=     dicts[0]
 lowerCamelCase_							:    Optional[Any]							=     {}
 for k, v in first.items():
  lowerCamelCase_							:    Tuple							=     [d[k] for d in dicts]
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[int]							=     dict_multimap(lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    List[Any]							=     fn(lowerCamelCase__				)

 return new_dict




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    str							=     v_bins.view(((1,) * len(x.shape				)) + (len(lowerCamelCase__				),)				)
 lowerCamelCase_							:    List[Any]							=     x[..., None] - reshaped_bins
 lowerCamelCase_							:    List[Any]							=     torch.argmin(torch.abs(lowerCamelCase__				) ,			dim=-1				)
 return nn.functional.one_hot(lowerCamelCase__ ,			num_classes=len(lowerCamelCase__				)				).float()




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 0 ,			lowerCamelCase__ = 0				)  ->      torch.Tensor:
 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     []
 for i, s in enumerate(data.shape[:no_batch_dims]				):
  lowerCamelCase_							:    int							=     torch.arange(lowerCamelCase__				)
  lowerCamelCase_							:    Any							=     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
  ranges.append(lowerCamelCase__				)

 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     [slice(lowerCamelCase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
 lowerCamelCase_							:    Optional[int]							=     inds
 ranges.extend(lowerCamelCase__				)
 # Matt note: Editing this to get around the behaviour of using a list as an array index changing
 # in recent Numpy versions
 return data[tuple(lowerCamelCase__				)]


UpperCamelCase											=      TypeVar('''T''')




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Dict[Any, Union[dict, list, tuple, Any]]:
 lowerCamelCase_							:    Dict[Any, Union[dict, list, tuple, Any]]							=     {}
 for k, v in dic.items():
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[Any]							=     dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    Dict							=     tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)

 return new_dict




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      list:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      tuple:
 ...




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return [tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree]
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return tuple(tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return fn(lowerCamelCase__				)
 else:
  print(type(lowerCamelCase__				)				)
  raise ValueError('Not supported'				)





UpperCamelCase											=      partial(tree_map, leaf_type=torch.Tensor)
