





'''simple docstring'''

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


def 		__UpperCamelCase		(       _UpperCAmelCase   ):

		def remove_articles(_UpperCAmelCase   ):
				return re.sub(R"\b(a|an|the)\b",    " ",    _UpperCamelCase   )

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

		def remove_punc(_UpperCAmelCase   ):
				__UpperCAmelCase     :				Optional[int]					   =			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 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				Any					   =			normalize_answer(_UpperCamelCase   ).split()
		__UpperCAmelCase     :				Tuple					   =			normalize_answer(_UpperCamelCase   ).split()
		__UpperCAmelCase     :				List[Any]					   =			Counter(_UpperCamelCase   ) & Counter(_UpperCamelCase   )
		__UpperCAmelCase     :				List[Any]					   =			sum(common.values()   )
		if num_same == 0:
				return 0
		__UpperCAmelCase     :				str					   =			1.0 * num_same / len(_UpperCamelCase   )
		__UpperCAmelCase     :				Optional[Any]					   =			1.0 * num_same / len(_UpperCamelCase   )
		__UpperCAmelCase     :				List[str]					   =			(2 * precision * recall) / (precision + recall)
		return fa


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		return normalize_answer(_UpperCamelCase   ) == normalize_answer(_UpperCamelCase   )


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				Union[str, Any]					   =			[]
		for ground_truth in ground_truths:
				__UpperCAmelCase     :				int					   =			metric_fn(_UpperCamelCase,    _UpperCamelCase   )
				scores_for_ground_truths.append(_UpperCamelCase   )
		return max(_UpperCamelCase   )


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				int					   =			0
		__UpperCAmelCase     :				Dict					   =			[]
		for passage in dataset:
				for qa in passage["qas"]:
						total += 1
						if qa["id"] not in predictions:
								__UpperCAmelCase     :				Union[str, Any]					   =			F"Unanswered question {qa['id']} will receive score 0."
								print(_UpperCamelCase,    file=sys.stderr   )
								continue

						__UpperCAmelCase     :				str					   =			[x["text"] for x in qa["answers"]]
						__UpperCAmelCase     :				Any					   =			predictions[qa["id"]]

						__UpperCAmelCase     :				Optional[Any]					   =			metric_max_over_ground_truths(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase   )
						if int(_exact_match   ) == 1:
								correct_ids.append(qa["id"]   )
						exact_match += _exact_match

						fa += metric_max_over_ground_truths(_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase   )

		__UpperCAmelCase     :				Optional[int]					   =			exact_match / total
		__UpperCAmelCase     :				Union[str, Any]					   =			fa / total

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


if __name__ == "__main__":
			lowerCAmelCase__	:					Any	     =       '1.0'
			lowerCAmelCase__	:					Optional[int]	     =       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.")
			lowerCAmelCase__	:					Optional[int]	     =       parser.parse_args()

			with open(args.data_file) as data_file:
						lowerCAmelCase__	:					List[Any]	     =       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,
									)
						lowerCAmelCase__	:					List[str]	     =       dataset_json['data']

			with open(args.pred_file) as pred_file:
						lowerCAmelCase__	:					Optional[Any]	     =       json.load(pred_file)

			lowerCAmelCase__	:					Tuple	     =       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)





'''simple docstring'''




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int   )	->      Union[str, Any]:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    A_     =   sorted(_UpperCamelCase   )
    A_     =   [bisect.bisect_right(_UpperCamelCase,   _UpperCamelCase   ) for y in x]
    return quantized





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple=0   )	->      str:
    A_     =   np.arange(start=3,   stop=_UpperCamelCase,   step=4   ).tolist() if k > 0 else [10]
    A_     =   _quantize(_UpperCamelCase,   _UpperCamelCase   )
    # count number of elements per group
    A_     =   np.unique(_UpperCamelCase,   return_counts=_UpperCamelCase   )[1]
    A_     =   [0] + bins + [np.inf]
    logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCamelCase   )   )
    logger.info('''Count of instances per bin: {}'''.format(_UpperCamelCase   )   )
    return groups




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           if not isinstance(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE ):
               raise ValueError(
                   '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(_SCREAMING_SNAKE_CASE ) )
           A_     =   sampler
           A_     =   group_ids
           A_     =   batch_size

       def __iter__(      self )		->     Tuple:
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )

           A_     =   0
           for idx in self.sampler:
               A_     =   self.group_ids[idx]
               buffer_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               samples_per_group[group_id].append(_SCREAMING_SNAKE_CASE )
               if len(buffer_per_group[group_id] ) == self.batch_size:
                   yield buffer_per_group[group_id]  # TODO
                   num_batches += 1
                   del buffer_per_group[group_id]
               assert len(buffer_per_group[group_id] ) < self.batch_size

           # now we have run out of elements that satisfy
           # the group criteria, let's return the remaining
           # elements so that the size of the sampler is
           # deterministic
           A_     =   len(self )
           A_     =   expected_num_batches - num_batches
           if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               A_     =   []
               for group_id, idxs in sorted(buffer_per_group.items()    ,     key=lambda _SCREAMING_SNAKE_CASE : x[0] ):
                   batch_idx.extend(_SCREAMING_SNAKE_CASE )
                   if len(_SCREAMING_SNAKE_CASE ) >= self.batch_size:
                       yield batch_idx[: self.batch_size]
                       A_     =   batch_idx[self.batch_size :]
                       num_remaining -= 1
               if len(_SCREAMING_SNAKE_CASE ) > 0:
                   yield batch_idx
                   num_remaining -= 1
           assert num_remaining == 0

       def __len__(      self )		->     Optional[int]:
           return (len(self.sampler ) + self.batch_size - 1) // self.batch_size






