




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    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Dict:

				"""simple docstring"""


				if not inplace:
								__UpperCamelCase											=  ma + ma
				else:
								ma += ma

				return ma




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

				"""simple docstring"""


				__UpperCamelCase											=  -1 * len(_lowerCAmelCase )
				__UpperCamelCase											=  list(range(len(tensor.shape[:zero_index] ) ) )
				return tensor.permute(first_inds + [zero_index + i for i in inds] )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		List[str]:

				"""simple docstring"""


				return t.reshape(t.shape[:-no_dims] + (-1,) )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ = 1E-4 )    ->		List[str]:

				"""simple docstring"""


				__UpperCamelCase											=  mask.expand(*value.shape )
				return torch.sum(mask * value   ,       dim=_lowerCAmelCase ) / (eps + torch.sum(_lowerCAmelCase   ,       dim=_lowerCAmelCase ))




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = 2.3125   ,       lowercase_ = 21.6875   ,       lowercase_ = 64 )    ->		List[str]:

				"""simple docstring"""


				__UpperCamelCase											=  torch.linspace(_lowerCAmelCase   ,       _lowerCAmelCase   ,       no_bins - 1   ,       device=pts.device )
				__UpperCamelCase											=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2   ,       dim=-1 ) )
				return torch.bucketize(_lowerCAmelCase   ,       _lowerCAmelCase )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		int:

				"""simple docstring"""


				__UpperCamelCase											=  dicts[0]
				__UpperCamelCase											=  {}
				for k, v in first.items():
								__UpperCamelCase											=  [d[k] for d in dicts]
								if isinstance(_lowerCAmelCase   ,       _lowerCAmelCase ):
												__UpperCamelCase											=  dict_multimap(_lowerCAmelCase   ,       _lowerCAmelCase )
								else:
												__UpperCamelCase											=  fn(_lowerCAmelCase )

				return new_dict




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Dict:

				"""simple docstring"""


				__UpperCamelCase											=  v_bins.view(((1,) * len(x.shape )) + (len(_lowerCAmelCase ),) )
				__UpperCamelCase											=  x[..., None] - reshaped_bins
				__UpperCamelCase											=  torch.argmin(torch.abs(_lowerCAmelCase )   ,       dim=-1 )
				return nn.functional.one_hot(_lowerCAmelCase   ,       num_classes=len(_lowerCAmelCase ) ).float()




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ = 0   ,       lowercase_ = 0 )    ->		Union[str, Any]:

				"""simple docstring"""


				__UpperCamelCase											=  []
				for i, s in enumerate(data.shape[:no_batch_dims] ):
								__UpperCamelCase											=  torch.arange(_lowerCAmelCase )
								__UpperCamelCase											=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
								ranges.append(_lowerCAmelCase )

				__UpperCamelCase											=  [slice(_lowerCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
				__UpperCamelCase											=  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 )]


a_										=       TypeVar("T")




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

				"""simple docstring"""


				__UpperCamelCase											=  {}
				for k, v in dic.items():
								if isinstance(_lowerCAmelCase   ,       _lowerCAmelCase ):
												__UpperCamelCase											=  dict_map(_lowerCAmelCase   ,       _lowerCAmelCase   ,       _lowerCAmelCase )
								else:
												__UpperCamelCase											=  tree_map(_lowerCAmelCase   ,       _lowerCAmelCase   ,       _lowerCAmelCase )

				return new_dict




@overload
def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Union[str, Any]:

				"""simple docstring"""


				...




@overload
def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Tuple:

				"""simple docstring"""


				...




@overload
def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Dict:

				"""simple docstring"""


				...




@overload
def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		Optional[int]:

				"""simple docstring"""


				...




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

				"""simple docstring"""


				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''' )


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












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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


    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_ ):
        __UpperCamelCase											=  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_ )    ->		List[str]:

    """simple docstring"""


    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  normalize_answer(lowercase_ ).split()
    __UpperCamelCase											=  Counter(lowercase_ ) & Counter(lowercase_ )
    __UpperCamelCase											=  sum(common.values() )
    if num_same == 0:
        return 0
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  1.0 * num_same / len(lowercase_ )
    __UpperCamelCase											=  (2 * precision * recall) / (precision + recall)
    return fa




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Any:

    """simple docstring"""


    return normalize_answer(lowercase_ ) == normalize_answer(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  []
    for ground_truth in ground_truths:
        __UpperCamelCase											=  metric_fn(lowercase_   ,       lowercase_ )
        scores_for_ground_truths.append(lowercase_ )
    return max(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Optional[int]:

    """simple docstring"""


    __UpperCamelCase											=  __UpperCamelCase											=  __UpperCamelCase											=  0
    __UpperCamelCase											=  []
    for passage in dataset:
        for qa in passage["qas"]:
            total += 1
            if qa["id"] not in predictions:
                __UpperCamelCase											=  F"Unanswered question {qa['id']} will receive score 0."
                print(lowercase_   ,       file=sys.stderr )
                continue

            __UpperCamelCase											=  [x['''text'''] for x in qa['''answers''']]
            __UpperCamelCase											=  predictions[qa['''id''']]

            __UpperCamelCase											=  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_ )

    __UpperCamelCase											=  exact_match / total
    __UpperCamelCase											=  fa / total

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


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

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

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

 a_						,      a_										=       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)






