code: 
  
  
  
  
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	parser.add_parser('login'			,    help='Log in using the same credentials as on huggingface.co'			)
  				login_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE			: LoginCommand(SCREAMING_SNAKE_CASE			)			)
  				SCREAMING_SNAKE_CASE_			=	parser.add_parser('whoami'			,    help='Find out which huggingface.co account you are logged in as.'			)
  				whoami_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE			: WhoamiCommand(SCREAMING_SNAKE_CASE			)			)
  				SCREAMING_SNAKE_CASE_			=	parser.add_parser('logout'			,    help='Log out'			)
  				logout_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE			: LogoutCommand(SCREAMING_SNAKE_CASE			)			)
  
  				# new system: git-based repo system
  				SCREAMING_SNAKE_CASE_			=	parser.add_parser(
  				    'repo'			,    help='Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'			,    )
  				SCREAMING_SNAKE_CASE_			=	repo_parser.add_subparsers(
  				    help='Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands'			)
  				SCREAMING_SNAKE_CASE_			=	repo_subparsers.add_parser(
  				    'create'			,    help='Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co'			)
  				repo_create_parser.add_argument(
  				    'name'			,    type=SCREAMING_SNAKE_CASE			,    help='Name for your model\'s repo. Will be namespaced under your username to build the model id.'			,    )
  				repo_create_parser.add_argument('--organization'			,    type=SCREAMING_SNAKE_CASE			,    help='Optional: organization namespace.'			)
  				repo_create_parser.add_argument('-y'			,    '--yes'			,    action='store_true'			,    help='Optional: answer Yes to the prompt'			)
  				repo_create_parser.set_defaults(func=lambda SCREAMING_SNAKE_CASE			: RepoCreateCommand(SCREAMING_SNAKE_CASE			)			)
  
  
  
  
  class 			a_					:
  	A						       =   '''\u001b[1m'''
  	A						       =   '''\u001b[31m'''
  	A						       =   '''\u001b[90m'''
  	A						       =   '''\u001b[0m'''
  
  
  
  
  
  	@classmethod
  	def 	A_(       cls			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return f'{cls._bold}{s}{cls._reset}'
  
  
  
  
  
  	@classmethod
  	def 	A_(       cls			,    SCREAMING_SNAKE_CASE			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return f'{cls._bold}{cls._red}{s}{cls._reset}'
  
  
  
  
  
  	@classmethod
  	def 	A_(       cls			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return f'{cls._gray}{s}{cls._reset}'
  
  
  
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       str:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[max(len(str(SCREAMING_SNAKE_CASE   )   ) for x in col   ) for col in zip(*SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )]
  			SCREAMING_SNAKE_CASE_			=	('{{:{}}} ' * len(SCREAMING_SNAKE_CASE   )).format(*SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	[]
  			lines.append(row_format.format(*SCREAMING_SNAKE_CASE   )   )
  			lines.append(row_format.format(*['-' * w for w in col_widths]   )   )
  			for row in rows:
  						lines.append(row_format.format(*SCREAMING_SNAKE_CASE   )   )
  			return "\n".join(SCREAMING_SNAKE_CASE   )
  
  
  
  class 			a_					:
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	args
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				print(
  				    ANSI.red(
  				        'ERROR! `huggingface-cli login` uses an outdated login mechanism '
  				        'that is not compatible with the Hugging Face Hub backend anymore. '
  				        'Please use `huggingface-cli login instead.'			)			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				print(
  				    ANSI.red(
  				        'WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '
  				        '`huggingface-cli whoami` instead.'			)			)
  				SCREAMING_SNAKE_CASE_			=	HfFolder.get_token()
  				if token is None:
  							print('Not logged in'			)
  							exit()
  				try:
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	whoami(SCREAMING_SNAKE_CASE			)
  							print(SCREAMING_SNAKE_CASE			)
  							if orgs:
  										print(ANSI.bold('orgs: '			)			,    ','.join(SCREAMING_SNAKE_CASE			)			)
  				except HTTPError as e:
  							print(SCREAMING_SNAKE_CASE			)
  							print(ANSI.red(e.response.text			)			)
  							exit(1			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				print(
  				    ANSI.red(
  				        'ERROR! `transformers-cli logout` uses an outdated logout mechanism '
  				        'that is not compatible with the Hugging Face Hub backend anymore. '
  				        'Please use `huggingface-cli logout instead.'			)			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	def 	A_(       self			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				print(
  				    ANSI.red(
  				        'WARNING! Managing repositories through transformers-cli is deprecated. '
  				        'Please use `huggingface-cli` instead.'			)			)
  				SCREAMING_SNAKE_CASE_			=	HfFolder.get_token()
  				if token is None:
  							print('Not logged in'			)
  							exit(1			)
  				try:
  							SCREAMING_SNAKE_CASE_			=	subprocess.check_output(['git', '--version']			).decode('utf-8'			)
  							print(ANSI.gray(stdout.strip()			)			)
  				except FileNotFoundError:
  							print('Looks like you do not have git installed, please install.'			)
  
  				try:
  							SCREAMING_SNAKE_CASE_			=	subprocess.check_output(['git-lfs', '--version']			).decode('utf-8'			)
  							print(ANSI.gray(stdout.strip()			)			)
  				except FileNotFoundError:
  							print(
  							    ANSI.red(
  							        'Looks like you do not have git-lfs installed, please install.'
  							        ' You can install from https://git-lfs.github.com/.'
  							        ' Then run `git lfs install` (you only have to do this once).'			)			)
  				print(''			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	whoami(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.args.organization if self.args.organization is not None else user
  				SCREAMING_SNAKE_CASE_			=	f'{namespace}/{self.args.name}'
  				print(f'You are about to create {ANSI.bold(SCREAMING_SNAKE_CASE			)}'			)
  
  				if not self.args.yes:
  							SCREAMING_SNAKE_CASE_			=	input('Proceed? [Y/n] '			).lower()
  							if not (choice == "" or choice == "y" or choice == "yes"):
  										print('Abort'			)
  										exit()
  				try:
  							SCREAMING_SNAKE_CASE_			=	create_repo(SCREAMING_SNAKE_CASE			,    name=self.args.name			,    organization=self.args.organization			)
  				except HTTPError as e:
  							print(SCREAMING_SNAKE_CASE			)
  							print(ANSI.red(e.response.text			)			)
  							exit(1			)
  				print('\nYour repo now lives at:'			)
  				print(f'  {ANSI.bold(SCREAMING_SNAKE_CASE			)}'			)
  				print('\nYou can clone it locally with the command below, and commit/push as usual.'			)
  				print(f'\n  git clone {url}'			)
  				print(''			)
  
  
  
  
  
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)))
  