


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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Tuple  ):







	'''simple docstring'''



	def remove_articles(__lowerCamelCase:		List[str]  ):
		return re.sub(r"\b(a|an|the)\b"  ,			" "  ,			lowerCAmelCase_  )

	def white_space_fix(__lowerCamelCase:		str  ):
		return " ".join(text.split()  )

	def remove_punc(__lowerCamelCase:		Optional[Any]  ):
		lowercase_			 = 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(lowerCAmelCase_  )  )  )  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[Any]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	return normalize_answer(lowerCAmelCase_  ) == normalize_answer(lowerCAmelCase_  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Any  ,			__lowerCamelCase:		Union[str, Any]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_			 = lowercase_			 = lowercase_			 = 0
	for article in dataset:
		for paragraph in article["paragraphs"]:
			for qa in paragraph["qas"]:
				total += 1
				if qa["id"] not in predictions:
					lowercase_			 = "Unanswered question " + qa["id"] + " will receive score 0."
					print(lowerCAmelCase_  ,			file=sys.stderr  )
					continue
				lowercase_			 = [x["text"] for x in qa["answers"]]
				lowercase_			 = predictions[qa["id"]]
				exact_match += metric_max_over_ground_truths(lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  )
				fa += metric_max_over_ground_truths(lowerCAmelCase_  ,			lowerCAmelCase_  ,			lowerCAmelCase_  )

	lowercase_			 = 100.0 * exact_match / total
	lowercase_			 = 100.0 * fa / total

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				'1.1'
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
							parser.add_argument("""dataset_file""", help="""Dataset file""")
							parser.add_argument("""prediction_file""", help="""Prediction File""")
							SCREAMING_SNAKE_CASE__       			=				parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														SCREAMING_SNAKE_CASE__       			=				json.load(dataset_file)
														if dataset_json["version"] != expected_version:
																					print(
																					    """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
																					    file=sys.stderr,
																					)
														SCREAMING_SNAKE_CASE__       			=				dataset_json['data']
							with open(args.prediction_file) as prediction_file:
														SCREAMING_SNAKE_CASE__       			=				json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))






'''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     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      copy.deepcopy(lowerCAmelCase_						)
    UpperCamelCase__	     =      sorted(lowerCAmelCase_						)
    UpperCamelCase__	     =      [bisect.bisect_right(lowerCAmelCase_       ,	lowerCAmelCase_						) for y in x]
    return quantized





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





class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       if not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__UpperCamelCase			)			)
       UpperCamelCase__	     =      sampler
       UpperCamelCase__	     =      group_ids
       UpperCamelCase__	     =      batch_size


   def __iter__(	self			):
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)

       UpperCamelCase__	     =      0
       for idx in self.sampler:
           UpperCamelCase__	     =      self.group_ids[idx]
           buffer_per_group[group_id].append(__UpperCamelCase			)
           samples_per_group[group_id].append(__UpperCamelCase			)
           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
       UpperCamelCase__	     =      len(self			)
       UpperCamelCase__	     =      expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           UpperCamelCase__	     =      []
           for group_id, idxs in sorted(buffer_per_group.items()  ,    key=lambda __UpperCamelCase			: x[0]			):
               batch_idx.extend(__UpperCamelCase			)
               if len(__UpperCamelCase			) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   UpperCamelCase__	     =      batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(__UpperCamelCase			) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0







   def __len__(	self			):
       return (len(self.sampler			) + self.batch_size - 1) // self.batch_size




