




'''simple docstring'''





import argparse
import json
import re
import string
import sys

import numpy as np


_A							    =	0.5
def 						lowerCAmelCase_       (	__a	,	__a )       ->			List[str]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[Any]	     =['''.''', ''',''', ''';''', ''':''']

							for token in remove_tokens:
														SCREAMING_SNAKE_CASE			: List[str]	     =ground_truth.replace(__a	,	'''''' )
														SCREAMING_SNAKE_CASE			: Optional[int]	     =prediction.replace(__a	,	'''''' )

							SCREAMING_SNAKE_CASE			: str	     =ground_truth.lower(), prediction.lower()
							SCREAMING_SNAKE_CASE			: List[Any]	     =ground_truth.replace('''/'''	,	''' ''' ), prediction.replace('''/'''	,	''' ''' )
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =set(ground_truth.split(''' ''' ) ), set(prediction.split(''' ''' ) )

							SCREAMING_SNAKE_CASE			: Optional[int]	     =ground_truth.intersection(__a )
							SCREAMING_SNAKE_CASE			: List[str]	     =ground_truth.union(__a )
							SCREAMING_SNAKE_CASE			: Tuple	     =len(__a ) / len(__a )
							return jaccard
def 						lowerCAmelCase_       (	__a )       ->			List[str]:





							"""simple docstring"""





							def remove_articles(__a ):
														return re.sub(r'''\b(a|an|the)\b'''	,	''' '''	,	__a )

							def white_space_fix(__a ):
														return " ".join(text.split() )

							def remove_punc(__a ):
														SCREAMING_SNAKE_CASE			: Optional[int]	     =set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(__a ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(__a ) ) ) )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			List[str]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[Any]	     =0, 0, 0

							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ='''Parties''' in qa_id

							# first check if ground truth is empty
							if len(__a ) == 0:
														if len(__a ) > 0:
																					fp += len(__a )  # false positive for each one
							else:
														for ground_truth in ground_truths:
																					assert len(__a ) > 0
																					# check if there is a match
																					SCREAMING_SNAKE_CASE			: Dict	     =False
																					for pred in predictions:
																												if substr_ok:
																																			SCREAMING_SNAKE_CASE			: Tuple	     =get_jaccard(__a	,	__a ) >= IOU_THRESH or ground_truth in pred
																												else:
																																			SCREAMING_SNAKE_CASE			: Dict	     =get_jaccard(__a	,	__a ) >= IOU_THRESH
																												if is_match:
																																			SCREAMING_SNAKE_CASE			: str	     =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			: Any	     =False
																					for ground_truth in ground_truths:
																												assert len(__a ) > 0
																												if substr_ok:
																																			SCREAMING_SNAKE_CASE			: int	     =get_jaccard(__a	,	__a ) >= IOU_THRESH or ground_truth in pred
																												else:
																																			SCREAMING_SNAKE_CASE			: Optional[Any]	     =get_jaccard(__a	,	__a ) >= IOU_THRESH
																												if is_match:
																																			SCREAMING_SNAKE_CASE			: Any	     =True

																					if not match_found:
																												fp += 1

							SCREAMING_SNAKE_CASE			: int	     =tp / (tp + fp) if tp + fp > 0 else np.nan
							SCREAMING_SNAKE_CASE			: List[Any]	     =tp / (tp + fn) if tp + fn > 0 else np.nan

							return precision, recall
def 						lowerCAmelCase_       (	__a )       ->			List[str]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =precisions[::-1]
							for i in range(1	,	len(__a ) ):
														SCREAMING_SNAKE_CASE			: int	     =max(precision_best[i - 1]	,	precision_best[i] )
							SCREAMING_SNAKE_CASE			: List[Any]	     =precision_best[::-1]
							return precisions
def 						lowerCAmelCase_       (	__a	,	__a )       ->			Optional[Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[Any]	     =process_precisions(__a )
							SCREAMING_SNAKE_CASE			: Optional[int]	     =np.trapz(__a	,	__a )
							if np.isnan(__a ):
														return 0
							return aupr
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Dict:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[str]	     =process_precisions(__a )
							SCREAMING_SNAKE_CASE			: List[Any]	     =0
							for prec, recall in zip(__a	,	__a ):
														if recall >= recall_thresh:
																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =prec
																					break
							return prec_at_recall
def 						lowerCAmelCase_       (	__a	,	__a )       ->			Union[str, Any]:





							"""simple docstring"""





							return normalize_answer(__a ) == normalize_answer(__a )
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Any:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Tuple	     =0
							for pred in predictions:
														for ground_truth in ground_truths:
																					SCREAMING_SNAKE_CASE			: int	     =metric_fn(__a	,	__a )
																					if score == 1:  # break the loop when one prediction matches the ground truth
																												break
														if score == 1:
																					break
							return score
def 						lowerCAmelCase_       (	__a	,	__a )       ->			str:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Any	     =0
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[]
							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			: List[Any]	     ='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																																			print(__a	,	file=sys.stderr )
																																			continue
																												SCREAMING_SNAKE_CASE			: Any	     =[x['''text'''] for x in qa['''answers''']]
																												SCREAMING_SNAKE_CASE			: Optional[int]	     =predictions[qa['''id''']]
																												SCREAMING_SNAKE_CASE			: Optional[int]	     =compute_precision_recall(__a	,	__a	,	qa['''id'''] )

																												precisions.append(__a )
																												recalls.append(__a )

																												if precision == 0 and recall == 0:
																																			fa += 0
																												else:
																																			fa += 2 * (precision * recall) / (precision + recall)

																												exact_match += metric_max_over_ground_truths(__a	,	__a	,	__a )

							SCREAMING_SNAKE_CASE			: Tuple	     =[x for _, x in sorted(zip(__a	,	__a ) )]
							recalls.sort()

							SCREAMING_SNAKE_CASE			: int	     =100.0 * fa / total
							SCREAMING_SNAKE_CASE			: List[str]	     =100.0 * exact_match / total
							SCREAMING_SNAKE_CASE			: List[Any]	     =get_aupr(__a	,	__a )

							SCREAMING_SNAKE_CASE			: Optional[Any]	     =get_prec_at_recall(__a	,	__a	,	recall_thresh=0.9 )
							SCREAMING_SNAKE_CASE			: int	     =get_prec_at_recall(__a	,	__a	,	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__":
							_A							    =	argparse.ArgumentParser(description="""Evaluation for CUAD""")
							parser.add_argument("""dataset_file""", help="""Dataset file""")
							parser.add_argument("""prediction_file""", help="""Prediction File""")
							_A							    =	parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														_A							    =	json.load(dataset_file)
														_A							    =	dataset_json["""data"""]
							with open(args.prediction_file) as prediction_file:
														_A							    =	json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))












import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_A							    =	logging.get_logger(__name__)

_A							    =	{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A							    =	{
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):




				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=False		,		snake_case_=True		,		snake_case_=False		,		snake_case_="<s>"		,		snake_case_="</s>"		,		snake_case_="<unk>"		,		snake_case_="<sep>"		,		snake_case_="<pad>"		,		snake_case_="<cls>"		,		snake_case_="<mask>"		,		snake_case_=["<eop>", "<eod>"]		,		**snake_case_		,		)    ->      str:
											SCREAMING_SNAKE_CASE			: Optional[int]	     =AddedToken(snake_case_		,		lstrip=snake_case_		,		rstrip=snake_case_							) if isinstance(snake_case_		,		snake_case_							) else mask_token

											super().__init__(
											    vocab_file=snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		remove_space=snake_case_		,		keep_accents=snake_case_		,		bos_token=snake_case_		,		eos_token=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		additional_special_tokens=snake_case_		,		**snake_case_		,		)

											SCREAMING_SNAKE_CASE			: Dict	     =3
											SCREAMING_SNAKE_CASE			: Dict	     =do_lower_case
											SCREAMING_SNAKE_CASE			: int	     =remove_space
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =keep_accents
											SCREAMING_SNAKE_CASE			: Tuple	     =vocab_file
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =False if not self.vocab_file else True

											try:
																		import jieba
											except ModuleNotFoundError as error:
																		raise error.__class__(
																		    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
																		    '''See https://pypi.org/project/jieba/ for installation.'''							)
											SCREAMING_SNAKE_CASE			: str	     =jieba
											SCREAMING_SNAKE_CASE			: Dict	     =str.maketrans(''' \n'''		,		'''\u2582\u2583'''							)




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Dict	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Any	     =[self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[2]

											if token_ids_a is None:
																		return len(token_ids_a + sep							) * [0] + cls_segment_id
											return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''							)

											if not os.path.isdir(snake_case_							):
																		logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																		return
											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(
											    snake_case_		,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

											if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																		copyfile(self.vocab_file		,		snake_case_							)

											return (out_vocab_file,)




				def     __a		( self		,		snake_case_		,		*snake_case_		,		**snake_case_							)    ->      Optional[Any]:
											SCREAMING_SNAKE_CASE			: int	     =[
											    ''' '''.join([x.translate(self.translator							) for x in self.jieba.cut(snake_case_		,		cut_all=snake_case_							)]							)
											    for text in batch_text_or_text_pairs
											]
											return super()._batch_encode_plus(snake_case_		,		*snake_case_		,		**snake_case_							)







				def     __a		( self		,		*snake_case_		,		**snake_case_							)    ->      str:
											SCREAMING_SNAKE_CASE			: List[Any]	     =super()._decode(*snake_case_		,		**snake_case_							)
											SCREAMING_SNAKE_CASE			: List[Any]	     =text.replace(''' '''		,		''''''							).replace('''\u2582'''		,		''' '''							).replace('''\u2583'''		,		'''\n'''							)
											return text






