code: 
  
  
  
  
  import unittest
  
  from transformers import load_tool
  
  from .test_tools_common import ToolTesterMixin
  
  
  SCREAMING_SNAKE_CASE__    :    Tuple								=					"\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"
  
  
  
  class 			a_					(    unittest.TestCase     ,						SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	load_tool('summarization'			)
  				self.tool.setup()
  				SCREAMING_SNAKE_CASE_			=	load_tool('summarization'			,    remote=SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.tool(SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.remote_tool(SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.tool(text=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.remote_tool(text=SCREAMING_SNAKE_CASE			)
  				self.assertEqual(
  				    SCREAMING_SNAKE_CASE			,    'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'			,    )
  
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)))
  