




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(''			)










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)))
