




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


SCREAMING_SNAKE_CASE__    :    int								=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__    :    Any								=					{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__    :    Dict								=					{
    'vocab_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE__    :    List[str]								=					{
    'microsoft/mpnet-base': 5_12,
}

SCREAMING_SNAKE_CASE__    :    Tuple								=					{
    'microsoft/mpnet-base': {'do_lower_case': True},
}



class 			a_					(    UpperCAmelCase_    ):
	A						       =   VOCAB_FILES_NAMES
	A						       =   PRETRAINED_VOCAB_FILES_MAP
	A						       =   PRETRAINED_INIT_CONFIGURATION
	A						       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A						       =   MPNetTokenizer
	A						       =   ['''input_ids''', '''attention_mask''']





	def __init__(       self			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=None			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE="<s>"			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE="</s>"			,    SCREAMING_SNAKE_CASE="<s>"			,    SCREAMING_SNAKE_CASE="[UNK]"			,    SCREAMING_SNAKE_CASE="<pad>"			,    SCREAMING_SNAKE_CASE="<mask>"			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Union[str, Any]:




				"""simple docstring"""



				super().__init__(
				    __UpperCamelCase			,    tokenizer_file=__UpperCamelCase			,    do_lower_case=__UpperCamelCase			,    bos_token=__UpperCamelCase			,    eos_token=__UpperCamelCase			,    sep_token=__UpperCamelCase			,    cls_token=__UpperCamelCase			,    unk_token=__UpperCamelCase			,    pad_token=__UpperCamelCase			,    mask_token=__UpperCamelCase			,    tokenize_chinese_chars=__UpperCamelCase			,    strip_accents=__UpperCamelCase			,    **__UpperCamelCase			,    )

				SCREAMING_SNAKE_CASE_			=	json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
				if (
				    pre_tok_state.get('lowercase'			,    __UpperCamelCase			) != do_lower_case
				    or pre_tok_state.get('strip_accents'			,    __UpperCamelCase			) != strip_accents
				):
							SCREAMING_SNAKE_CASE_			=	getattr(__UpperCamelCase			,    pre_tok_state.pop('type'			)			)
							SCREAMING_SNAKE_CASE_			=	do_lower_case
							SCREAMING_SNAKE_CASE_			=	strip_accents
							SCREAMING_SNAKE_CASE_			=	pre_tok_class(**__UpperCamelCase			)

				SCREAMING_SNAKE_CASE_			=	do_lower_case





	@property
	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				if self._mask_token is None:
							if self.verbose:
										logger.error('Using mask_token, but it is not set yet.'			)
							return None
				return str(self._mask_token			)





	@mask_token.setter
	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	AddedToken(__UpperCamelCase			,    lstrip=__UpperCamelCase			,    rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase			,    __UpperCamelCase			) else value
				SCREAMING_SNAKE_CASE_			=	value





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=None			)     ->				Optional[int]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	[self.sep_token_id]
				SCREAMING_SNAKE_CASE_			=	[self.cls_token_id]

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self._tokenizer.model.save(__UpperCamelCase			,    name=__UpperCamelCase			)
				return tuple(__UpperCamelCase			)










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)))
