




from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__    :    Union[str, Any]								=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__    :    Any								=					{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   '''rembert'''





	def __init__(       self			,    SCREAMING_SNAKE_CASE=25_0300			,    SCREAMING_SNAKE_CASE=1152			,    SCREAMING_SNAKE_CASE=32			,    SCREAMING_SNAKE_CASE=18			,    SCREAMING_SNAKE_CASE=256			,    SCREAMING_SNAKE_CASE=1664			,    SCREAMING_SNAKE_CASE=4608			,    SCREAMING_SNAKE_CASE="gelu"			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.0			,    SCREAMING_SNAKE_CASE=0.1			,    SCREAMING_SNAKE_CASE=512			,    SCREAMING_SNAKE_CASE=2			,    SCREAMING_SNAKE_CASE=0.0_2			,    SCREAMING_SNAKE_CASE=1e-12			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=0			,    SCREAMING_SNAKE_CASE=312			,    SCREAMING_SNAKE_CASE=313			,    **SCREAMING_SNAKE_CASE			,    )     ->				Any:




				"""simple docstring"""



				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE			,    bos_token_id=SCREAMING_SNAKE_CASE			,    eos_token_id=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	vocab_size
				SCREAMING_SNAKE_CASE_			=	input_embedding_size
				SCREAMING_SNAKE_CASE_			=	output_embedding_size
				SCREAMING_SNAKE_CASE_			=	max_position_embeddings
				SCREAMING_SNAKE_CASE_			=	hidden_size
				SCREAMING_SNAKE_CASE_			=	num_hidden_layers
				SCREAMING_SNAKE_CASE_			=	num_attention_heads
				SCREAMING_SNAKE_CASE_			=	intermediate_size
				SCREAMING_SNAKE_CASE_			=	hidden_act
				SCREAMING_SNAKE_CASE_			=	hidden_dropout_prob
				SCREAMING_SNAKE_CASE_			=	attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_			=	classifier_dropout_prob
				SCREAMING_SNAKE_CASE_			=	initializer_range
				SCREAMING_SNAKE_CASE_			=	type_vocab_size
				SCREAMING_SNAKE_CASE_			=	layer_norm_eps
				SCREAMING_SNAKE_CASE_			=	use_cache
				SCREAMING_SNAKE_CASE_			=	False




class 			a_					(    SCREAMING_SNAKE_CASE__    ):





	@property
	def 	A_(       self			)     ->				Mapping[str, Mapping[int, str]]:




				"""simple docstring"""



				if self.task == "multiple-choice":
							SCREAMING_SNAKE_CASE_			=	{0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
							SCREAMING_SNAKE_CASE_			=	{0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				        ('token_type_ids', dynamic_axis),
				    ]			)





	@property
	def 	A_(       self			)     ->				float:




				"""simple docstring"""



				return 1e-4










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)))
