code: 
  
  
  
  
  from copy import deepcopy
  from unittest.case import TestCase
  
  import pytest
  
  from datasets.arrow_dataset import Dataset
  from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
  from datasets.info import DatasetInfo
  from datasets.tasks import (
      AudioClassification,
      AutomaticSpeechRecognition,
      ImageClassification,
      LanguageModeling,
      QuestionAnsweringExtractive,
      Summarization,
      TextClassification,
      task_template_from_dict,
  )
  from datasets.utils.py_utils import asdict
  
  
  SCREAMING_SNAKE_CASE__    :    Any								=					{
      "id": "5733be284776f41900661182",
      "title": "University_of_Notre_Dame",
      "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
      "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
      "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [5_15]},
  }
  
  @pytest.mark.parametrize(
      'task_cls'					,						[
          AudioClassification,
          AutomaticSpeechRecognition,
          ImageClassification,
          LanguageModeling,
          QuestionAnsweringExtractive,
          Summarization,
          TextClassification,
      ]					,						)
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	task_cls()
  			SCREAMING_SNAKE_CASE_			=	asdict(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	task_template_from_dict(SCREAMING_SNAKE_CASE   )
  			assert task == reloaded
  
  
  
  class 			a_					:
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	LanguageModeling(text_column='input_text'			)
  				assert {"input_text": "text"} == task.column_mapping
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'text': Value('string'			)}			)
  				SCREAMING_SNAKE_CASE_			=	{'text_column': 'input_text'}
  				SCREAMING_SNAKE_CASE_			=	LanguageModeling.from_dict(UpperCamelCase_			)
  				assert "language-modeling" == task.task
  				assert input_schema == task.input_schema
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	sorted(['pos', 'neg']			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TextClassification(text_column='input_text'			,    label_column='input_label'			)
  				self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'text': Value('string'			)}			)
  				# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
  				SCREAMING_SNAKE_CASE_			=	Features({'labels': ClassLabel}			)
  				SCREAMING_SNAKE_CASE_			=	{'text_column': 'input_text', 'label_column': 'input_labels'}
  				SCREAMING_SNAKE_CASE_			=	TextClassification.from_dict(UpperCamelCase_			)
  				self.assertEqual('text-classification'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TextClassification(text_column='input_text'			,    label_column='input_label'			)
  				self.assertEqual(task.label_schema['labels']			,    UpperCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	task.align_with_features(Features({'input_label': ClassLabel(names=self.labels			)}			)			)
  				self.assertEqual(task.label_schema['labels']			,    ClassLabel(names=self.labels			)			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	QuestionAnsweringExtractive(
  				    context_column='input_context'			,    question_column='input_question'			,    answers_column='input_answers'			)
  				self.assertDictEqual(
  				    {'input_context': 'context', 'input_question': 'question', 'input_answers': 'answers'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'question': Value('string'			), 'context': Value('string'			)}			)
  				SCREAMING_SNAKE_CASE_			=	Features(
  				    {
  				        'answers': Sequence(
  				            {
  				                'text': Value('string'			),
  				                'answer_start': Value('int32'			),
  				            }			)
  				    }			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'context_column': 'input_input_context',
  				    'question_column': 'input_question',
  				    'answers_column': 'input_answers',
  				}
  				SCREAMING_SNAKE_CASE_			=	QuestionAnsweringExtractive.from_dict(UpperCamelCase_			)
  				self.assertEqual('question-answering-extractive'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Summarization(text_column='input_text'			,    summary_column='input_summary'			)
  				self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'text': Value('string'			)}			)
  				SCREAMING_SNAKE_CASE_			=	Features({'summary': Value('string'			)}			)
  				SCREAMING_SNAKE_CASE_			=	{'text_column': 'input_text', 'summary_column': 'input_summary'}
  				SCREAMING_SNAKE_CASE_			=	Summarization.from_dict(UpperCamelCase_			)
  				self.assertEqual('summarization'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	AutomaticSpeechRecognition(audio_column='input_audio'			,    transcription_column='input_transcription'			)
  				self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'audio': Audio()}			)
  				SCREAMING_SNAKE_CASE_			=	Features({'transcription': Value('string'			)}			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'audio_column': 'input_audio',
  				    'transcription_column': 'input_transcription',
  				}
  				SCREAMING_SNAKE_CASE_			=	AutomaticSpeechRecognition.from_dict(UpperCamelCase_			)
  				self.assertEqual('automatic-speech-recognition'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	sorted(['pos', 'neg']			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	AudioClassification(audio_column='input_audio'			,    label_column='input_label'			)
  				self.assertDictEqual({'input_audio': 'audio', 'input_label': 'labels'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'audio': Audio()}			)
  				SCREAMING_SNAKE_CASE_			=	Features({'labels': ClassLabel}			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'audio_column': 'input_image',
  				    'label_column': 'input_label',
  				}
  				SCREAMING_SNAKE_CASE_			=	AudioClassification.from_dict(UpperCamelCase_			)
  				self.assertEqual('audio-classification'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	AudioClassification(audio_column='input_audio'			,    label_column='input_label'			)
  				self.assertEqual(task.label_schema['labels']			,    UpperCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	task.align_with_features(Features({'input_label': ClassLabel(names=self.labels			)}			)			)
  				self.assertEqual(task.label_schema['labels']			,    ClassLabel(names=self.labels			)			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	sorted(['pos', 'neg']			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	ImageClassification(image_column='input_image'			,    label_column='input_label'			)
  				self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}			,    task.column_mapping			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'image': Image()}			)
  				SCREAMING_SNAKE_CASE_			=	Features({'labels': ClassLabel}			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'image_column': 'input_image',
  				    'label_column': 'input_label',
  				}
  				SCREAMING_SNAKE_CASE_			=	ImageClassification.from_dict(UpperCamelCase_			)
  				self.assertEqual('image-classification'			,    task.task			)
  				self.assertEqual(UpperCamelCase_			,    task.input_schema			)
  				self.assertEqual(UpperCamelCase_			,    task.label_schema			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	ImageClassification(image_column='input_image'			,    label_column='input_label'			)
  				self.assertEqual(task.label_schema['labels']			,    UpperCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	task.align_with_features(Features({'input_label': ClassLabel(names=self.labels			)}			)			)
  				self.assertEqual(task.label_schema['labels']			,    ClassLabel(names=self.labels			)			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
  				SCREAMING_SNAKE_CASE_			=	Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}			,    info=UpperCamelCase_			)
  				assert isinstance(dataset.info.task_templates			,    UpperCamelCase_			)
  				assert len(dataset.info.task_templates			) == 1
  
  				def keep_task(SCREAMING_SNAKE_CASE			):
  							return x
  
  				def dont_keep_task(SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	deepcopy(UpperCamelCase_			)
  							SCREAMING_SNAKE_CASE_			=	0
  							return out
  
  				SCREAMING_SNAKE_CASE_			=	dataset.map(UpperCamelCase_			)
  				assert mapped_dataset.info.task_templates == dataset.info.task_templates
  				# reload from cache
  				SCREAMING_SNAKE_CASE_			=	dataset.map(UpperCamelCase_			)
  				assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
  				SCREAMING_SNAKE_CASE_			=	dataset.map(UpperCamelCase_			)
  				assert mapped_dataset.info.task_templates == []
  				# reload from cache
  				SCREAMING_SNAKE_CASE_			=	dataset.map(UpperCamelCase_			)
  				assert mapped_dataset.info.task_templates == []
  
  
  
  
  
  	def 	A_(       self			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	Features({'text': Value('string'			), 'label': ClassLabel(names=('pos', 'neg')			)}			)
  				SCREAMING_SNAKE_CASE_			=	TextClassification(text_column='text'			,    label_column='label'			)
  				SCREAMING_SNAKE_CASE_			=	DatasetInfo(features=UpperCamelCase_			,    task_templates=UpperCamelCase_			)
  				SCREAMING_SNAKE_CASE_			=	Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}			,    info=UpperCamelCase_			)
  
  				def process(SCREAMING_SNAKE_CASE			):
  							return example
  
  				SCREAMING_SNAKE_CASE_			=	dataset.remove_columns('label'			)
  				SCREAMING_SNAKE_CASE_			=	modified_dataset.map(UpperCamelCase_			)
  				assert mapped_dataset.info.task_templates == []
  
  
  
  
  
style_context: 
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  
  import numpy as np
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					0.5
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	['.', ',', ';', ':']
  
  			for token in remove_tokens:
  						SCREAMING_SNAKE_CASE_			=	ground_truth.replace(SCREAMING_SNAKE_CASE					,						''   )
  						SCREAMING_SNAKE_CASE_			=	prediction.replace(SCREAMING_SNAKE_CASE					,						''   )
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	ground_truth.lower(), prediction.lower()
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	ground_truth.replace('/'					,						' '   ), prediction.replace('/'					,						' '   )
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	set(ground_truth.split(' '   )   ), set(prediction.split(' '   )   )
  
  			SCREAMING_SNAKE_CASE_			=	ground_truth.intersection(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	ground_truth.union(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE   ) / len(SCREAMING_SNAKE_CASE   )
  			return jaccard
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			def remove_articles(SCREAMING_SNAKE_CASE   ):
  						return re.sub(r'\b(a|an|the)\b'					,						' '					,						SCREAMING_SNAKE_CASE   )
  
  			def white_space_fix(SCREAMING_SNAKE_CASE   ):
  						return " ".join(text.split()   )
  
  			def remove_punc(SCREAMING_SNAKE_CASE   ):
  						SCREAMING_SNAKE_CASE_			=	set(string.punctuation   )
  						return "".join(ch for ch in text if ch not in exclude   )
  
  			def lower(SCREAMING_SNAKE_CASE   ):
  						return text.lower()
  
  			return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE   )   )   )   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	0, 0, 0
  
  			SCREAMING_SNAKE_CASE_			=	'Parties' in qa_id
  
  			# first check if ground truth is empty
  			if len(SCREAMING_SNAKE_CASE   ) == 0:
  						if len(SCREAMING_SNAKE_CASE   ) > 0:
  									fp += len(SCREAMING_SNAKE_CASE   )  # false positive for each one
  			else:
  						for ground_truth in ground_truths:
  									assert len(SCREAMING_SNAKE_CASE   ) > 0
  									# check if there is a match
  									SCREAMING_SNAKE_CASE_			=	False
  									for pred in predictions:
  												if substr_ok:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH or ground_truth in pred
  												else:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH
  												if is_match:
  															SCREAMING_SNAKE_CASE_			=	True
  
  									if match_found:
  												tp += 1
  									else:
  												fn += 1
  
          # now also get any fps by looping through preds
  						for pred in predictions:
  									# Check if there's a match. if so, don't count (don't want to double count based on the above)
  									# but if there's no match, then this is a false positive.
  									# (Note: we get the true positives in the above loop instead of this loop so that we don't double count
  									# multiple predictions that are matched with the same answer.)
  									SCREAMING_SNAKE_CASE_			=	False
  									for ground_truth in ground_truths:
  												assert len(SCREAMING_SNAKE_CASE   ) > 0
  												if substr_ok:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH or ground_truth in pred
  												else:
  															SCREAMING_SNAKE_CASE_			=	get_jaccard(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) >= IOU_THRESH
  												if is_match:
  															SCREAMING_SNAKE_CASE_			=	True
  
  									if not match_found:
  												fp += 1
  
  			SCREAMING_SNAKE_CASE_			=	tp / (tp + fp) if tp + fp > 0 else np.nan
  			SCREAMING_SNAKE_CASE_			=	tp / (tp + fn) if tp + fn > 0 else np.nan
  
  			return precision, recall
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	precisions[::-1]
  			for i in range(1					,						len(SCREAMING_SNAKE_CASE   )   ):
  						SCREAMING_SNAKE_CASE_			=	max(precision_best[i - 1]					,						precision_best[i]   )
  			SCREAMING_SNAKE_CASE_			=	precision_best[::-1]
  			return precisions
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	process_precisions(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	np.trapz(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			if np.isnan(SCREAMING_SNAKE_CASE   ):
  						return 0
  			return aupr
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Dict:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	process_precisions(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	0
  			for prec, recall in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ):
  						if recall >= recall_thresh:
  									SCREAMING_SNAKE_CASE_			=	prec
  									break
  			return prec_at_recall
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			return normalize_answer(SCREAMING_SNAKE_CASE   ) == normalize_answer(SCREAMING_SNAKE_CASE   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       List[str]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	0
  			for pred in predictions:
  						for ground_truth in ground_truths:
  									SCREAMING_SNAKE_CASE_			=	metric_fn(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  									if score == 1:  # break the loop when one prediction matches the ground truth
  												break
  						if score == 1:
  									break
  			return score
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	SCREAMING_SNAKE_CASE_			=	SCREAMING_SNAKE_CASE_			=	0
  			SCREAMING_SNAKE_CASE_			=	[]
  			SCREAMING_SNAKE_CASE_			=	[]
  			for article in dataset:
  						for paragraph in article["paragraphs"]:
  									for qa in paragraph["qas"]:
  												total += 1
  												if qa["id"] not in predictions:
  															SCREAMING_SNAKE_CASE_			=	'Unanswered question ' + qa['id'] + ' will receive score 0.'
  															print(SCREAMING_SNAKE_CASE					,						file=sys.stderr   )
  															continue
  												SCREAMING_SNAKE_CASE_			=	[x['text'] for x in qa['answers']]
  												SCREAMING_SNAKE_CASE_			=	predictions[qa['id']]
  												SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	compute_precision_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						qa['id']   )
  
  												precisions.append(SCREAMING_SNAKE_CASE   )
  												recalls.append(SCREAMING_SNAKE_CASE   )
  
  												if precision == 0 and recall == 0:
  															fa += 0
  												else:
  															fa += 2 * (precision * recall) / (precision + recall)
  
  												exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	[x for _, x in sorted(zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )   )]
  			recalls.sort()
  
  			SCREAMING_SNAKE_CASE_			=	100.0 * fa / total
  			SCREAMING_SNAKE_CASE_			=	100.0 * exact_match / total
  			SCREAMING_SNAKE_CASE_			=	get_aupr(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						recall_thresh=0.9   )
  			SCREAMING_SNAKE_CASE_			=	get_prec_at_recall(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						recall_thresh=0.8   )
  
  			return {
  			    "exact_match": exact_match,
  			    "f1": fa,
  			    "aupr": aupr,
  			    "prec_at_80_recall": prec_at_aa_recall,
  			    "prec_at_90_recall": prec_at_aa_recall,
  			}
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					argparse.ArgumentParser(description="Evaluation for CUAD")
  				parser.add_argument("dataset_file", help="Dataset file")
  				parser.add_argument("prediction_file", help="Prediction File")
  				SCREAMING_SNAKE_CASE__    :    Any								=					parser.parse_args()
  				with open(args.dataset_file) as dataset_file:
  								SCREAMING_SNAKE_CASE__    :    Any								=					json.load(dataset_file)
  								SCREAMING_SNAKE_CASE__    :    str								=					dataset_json["data"]
  				with open(args.prediction_file) as prediction_file:
  								SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					json.load(prediction_file)
  				print(json.dumps(evaluate(dataset, predictions)))
  