"""simple docstring"""






import argparse
import json
import re
import string
import sys

import numpy as np


lowerCAmelCase__  :  Optional[Any]											=     0.5



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							['.', ',', ';', ':']

				for token in remove_tokens:
								UpperCAmelCase__					=							ground_truth.replace(lowerCamelCase_ ,      ''	)
								UpperCAmelCase__					=							prediction.replace(lowerCamelCase_ ,      ''	)

				UpperCAmelCase__				,     UpperCAmelCase__					=							ground_truth.lower(), prediction.lower()
				UpperCAmelCase__				,     UpperCAmelCase__					=							ground_truth.replace('/' ,      ' '	), prediction.replace('/' ,      ' '	)
				UpperCAmelCase__				,     UpperCAmelCase__					=							set(ground_truth.split(' '	)	), set(prediction.split(' '	)	)

				UpperCAmelCase__					=							ground_truth.intersection(lowerCamelCase_	)
				UpperCAmelCase__					=							ground_truth.union(lowerCamelCase_	)
				UpperCAmelCase__					=							len(lowerCamelCase_	) / len(lowerCamelCase_	)
				return jaccard



def   a_						( lowerCamelCase	):

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

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

				def remove_punc(lowerCamelCase	):
								UpperCAmelCase__					=							set(string.punctuation	)
								return "".join(ch for ch in text if ch not in exclude	)

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

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_	)	)	)	)



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							0, 0, 0

				UpperCAmelCase__					=							'Parties' in qa_id

				# first check if ground truth is empty
				if len(lowerCamelCase_	) == 0:
								if len(lowerCamelCase_	) > 0:
												fp += len(lowerCamelCase_	)  # false positive for each one
				else:
								for ground_truth in ground_truths:
												assert len(lowerCamelCase_	) > 0
												# check if there is a match
												UpperCAmelCase__					=							False
												for pred in predictions:
																if substr_ok:
																				UpperCAmelCase__					=							get_jaccard(lowerCamelCase_ ,      lowerCamelCase_	) >= IOU_THRESH or ground_truth in pred
																else:
																				UpperCAmelCase__					=							get_jaccard(lowerCamelCase_ ,      lowerCamelCase_	) >= IOU_THRESH
																if is_match:
																				UpperCAmelCase__					=							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.)
												UpperCAmelCase__					=							False
												for ground_truth in ground_truths:
																assert len(lowerCamelCase_	) > 0
																if substr_ok:
																				UpperCAmelCase__					=							get_jaccard(lowerCamelCase_ ,      lowerCamelCase_	) >= IOU_THRESH or ground_truth in pred
																else:
																				UpperCAmelCase__					=							get_jaccard(lowerCamelCase_ ,      lowerCamelCase_	) >= IOU_THRESH
																if is_match:
																				UpperCAmelCase__					=							True

												if not match_found:
																fp += 1

				UpperCAmelCase__					=							tp / (tp + fp) if tp + fp > 0 else np.nan
				UpperCAmelCase__					=							tp / (tp + fn) if tp + fn > 0 else np.nan

				return precision, recall



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							precisions[::-1]
				for i in range(1 ,      len(lowerCamelCase_	)	):
								UpperCAmelCase__					=							max(precision_best[i - 1] ,      precision_best[i]	)
				UpperCAmelCase__					=							precision_best[::-1]
				return precisions



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							process_precisions(lowerCamelCase_	)
				UpperCAmelCase__					=							np.trapz(lowerCamelCase_ ,      lowerCamelCase_	)
				if np.isnan(lowerCamelCase_	):
								return 0
				return aupr



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							process_precisions(lowerCamelCase_	)
				UpperCAmelCase__					=							0
				for prec, recall in zip(lowerCamelCase_ ,      lowerCamelCase_	):
								if recall >= recall_thresh:
												UpperCAmelCase__					=							prec
												break
				return prec_at_recall



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				return normalize_answer(lowerCamelCase_	) == normalize_answer(lowerCamelCase_	)



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							0
				for pred in predictions:
								for ground_truth in ground_truths:
												UpperCAmelCase__					=							metric_fn(lowerCamelCase_ ,      lowerCamelCase_	)
												if score == 1:  # break the loop when one prediction matches the ground truth
																break
								if score == 1:
												break
				return score



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							UpperCAmelCase__					=							UpperCAmelCase__					=							0
				UpperCAmelCase__					=							[]
				UpperCAmelCase__					=							[]
				for article in dataset:
								for paragraph in article["paragraphs"]:
												for qa in paragraph["qas"]:
																total += 1
																if qa["id"] not in predictions:
																				UpperCAmelCase__					=							'Unanswered question ' + qa['id'] + ' will receive score 0.'
																				print(lowerCamelCase_ ,      file=sys.stderr	)
																				continue
																UpperCAmelCase__					=							[x['text'] for x in qa['answers']]
																UpperCAmelCase__					=							predictions[qa['id']]
																UpperCAmelCase__				,     UpperCAmelCase__					=							compute_precision_recall(lowerCamelCase_ ,      lowerCamelCase_ ,      qa['id']	)

																precisions.append(lowerCamelCase_	)
																recalls.append(lowerCamelCase_	)

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

																exact_match += metric_max_over_ground_truths(lowerCamelCase_ ,      lowerCamelCase_ ,      lowerCamelCase_	)

				UpperCAmelCase__					=							[x for _, x in sorted(zip(lowerCamelCase_ ,      lowerCamelCase_	)	)]
				recalls.sort()

				UpperCAmelCase__					=							1_0_0.0 * fa / total
				UpperCAmelCase__					=							1_0_0.0 * exact_match / total
				UpperCAmelCase__					=							get_aupr(lowerCamelCase_ ,      lowerCamelCase_	)

				UpperCAmelCase__					=							get_prec_at_recall(lowerCamelCase_ ,      lowerCamelCase_ ,      recall_thresh=0.9	)
				UpperCAmelCase__					=							get_prec_at_recall(lowerCamelCase_ ,      lowerCamelCase_ ,      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__":
						lowerCAmelCase__  :  str											=     argparse.ArgumentParser(description='Evaluation for CUAD')
						parser.add_argument('dataset_file', help='Dataset file')
						parser.add_argument('prediction_file', help='Prediction File')
						lowerCAmelCase__  :  Dict											=     parser.parse_args()
						with open(args.dataset_file) as dataset_file:
												lowerCAmelCase__  :  Dict											=     json.load(dataset_file)
												lowerCAmelCase__  :  Union[str, Any]											=     dataset_json['data']
						with open(args.prediction_file) as prediction_file:
												lowerCAmelCase__  :  List[Any]											=     json.load(prediction_file)
						print(json.dumps(evaluate(dataset, predictions)))




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



