

"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE							:	Tuple										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			{
    """google/mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"""
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE_      =    'mobilebert'






			def __init__(				self  :       Tuple    ,	lowercase_  :       List[Any]=3_0522    ,	lowercase_  :       Tuple=512    ,	lowercase_  :       List[str]=24    ,	lowercase_  :       Optional[int]=4    ,	lowercase_  :       Union[str, Any]=512    ,	lowercase_  :       str="relu"    ,	lowercase_  :       List[Any]=0.0    ,	lowercase_  :       List[Any]=0.1    ,	lowercase_  :       List[Any]=512    ,	lowercase_  :       int=2    ,	lowercase_  :       Optional[int]=0.0_2    ,	lowercase_  :       Dict=1e-12    ,	lowercase_  :       Optional[Any]=0    ,	lowercase_  :       Any=128    ,	lowercase_  :       List[Any]=True    ,	lowercase_  :       Any=True    ,	lowercase_  :       Tuple=128    ,	lowercase_  :       List[Any]=False    ,	lowercase_  :       Dict=True    ,	lowercase_  :       Tuple=4    ,	lowercase_  :       Optional[int]="no_norm"    ,	lowercase_  :       Optional[int]=True    ,	lowercase_  :       List[Any]=None    ,	**lowercase_  :       Dict    ,	):
								super().__init__(pad_token_id=lowercase_    ,	**lowercase_    )

								UpperCamelCase__		:     List[str]								=vocab_size
								UpperCamelCase__		:     Optional[Any]								=hidden_size
								UpperCamelCase__		:     Tuple								=num_hidden_layers
								UpperCamelCase__		:     Tuple								=num_attention_heads
								UpperCamelCase__		:     str								=hidden_act
								UpperCamelCase__		:     Optional[Any]								=intermediate_size
								UpperCamelCase__		:     List[Any]								=hidden_dropout_prob
								UpperCamelCase__		:     Dict								=attention_probs_dropout_prob
								UpperCamelCase__		:     Optional[Any]								=max_position_embeddings
								UpperCamelCase__		:     List[str]								=type_vocab_size
								UpperCamelCase__		:     Union[str, Any]								=initializer_range
								UpperCamelCase__		:     str								=layer_norm_eps
								UpperCamelCase__		:     str								=embedding_size
								UpperCamelCase__		:     Any								=trigram_input
								UpperCamelCase__		:     Optional[int]								=use_bottleneck
								UpperCamelCase__		:     Union[str, Any]								=intra_bottleneck_size
								UpperCamelCase__		:     List[str]								=use_bottleneck_attention
								UpperCamelCase__		:     List[Any]								=key_query_shared_bottleneck
								UpperCamelCase__		:     Optional[Any]								=num_feedforward_networks
								UpperCamelCase__		:     Any								=normalization_type
								UpperCamelCase__		:     Dict								=classifier_activation

								if self.use_bottleneck:
													UpperCamelCase__		:     int								=intra_bottleneck_size
								else:
													UpperCamelCase__		:     Optional[int]								=hidden_size

								UpperCamelCase__		:     str								=classifier_dropout



class      __a (   snake_case__     ):

			"""simple docstring"""

			@property
			def  _lowerCAmelCase      (				self  :       Optional[Any]    ):
								if self.task == "multiple-choice":
													UpperCamelCase__		:     Union[str, Any]								={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
													UpperCamelCase__		:     int								={0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ('''input_ids''', dynamic_axis),
								        ('''attention_mask''', dynamic_axis),
								        ('''token_type_ids''', dynamic_axis),
								    ]    )





"""simple docstring"""





import argparse
import json
import re
import string
import sys

import numpy as np


_SCREAMING_SNAKE_CASE							:	str										=			0.5



def 							_lowerCAmelCase   (    UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Optional[int]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=['''.''', ''',''', ''';''', ''':''']

					for token in remove_tokens:
										UpperCamelCase__		:     Optional[int]								=ground_truth.replace(UpperCAmelCase   ,				''''''					)
										UpperCamelCase__		:     List[Any]								=prediction.replace(UpperCAmelCase   ,				''''''					)

					UpperCamelCase__	,      UpperCamelCase__		:     List[Any]								=ground_truth.lower(), prediction.lower()
					UpperCamelCase__	,      UpperCamelCase__		:     Dict								=ground_truth.replace('''/'''   ,				''' '''					), prediction.replace('''/'''   ,				''' '''					)
					UpperCamelCase__	,      UpperCamelCase__		:     Any								=set(ground_truth.split(''' '''					)					), set(prediction.split(''' '''					)					)

					UpperCamelCase__		:     Optional[Any]								=ground_truth.intersection(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[Any]								=ground_truth.union(UpperCAmelCase					)
					UpperCamelCase__		:     str								=len(UpperCAmelCase					) / len(UpperCAmelCase					)
					return jaccard



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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

					def white_space_fix(UpperCAmelCase  :				Union[str, Any]					):
										return " ".join(text.split()					)

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

					def lower(UpperCAmelCase  :				Optional[Any]					):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase					)					)					)					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






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

					UpperCamelCase__		:     int								='''Parties''' in qa_id

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

															if not match_found:
																				fp += 1

					UpperCamelCase__		:     Union[str, Any]								=tp / (tp + fp) if tp + fp > 0 else np.nan
					UpperCamelCase__		:     Optional[int]								=tp / (tp + fn) if tp + fn > 0 else np.nan

					return precision, recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[Any]								=precisions[::-1]
					for i in range(1   ,				len(UpperCAmelCase					)					):
										UpperCamelCase__		:     int								=max(precision_best[i - 1]   ,				precision_best[i]					)
					UpperCamelCase__		:     str								=precision_best[::-1]
					return precisions



def 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__		:     int								=process_precisions(UpperCAmelCase					)
					UpperCamelCase__		:     str								=np.trapz(UpperCAmelCase   ,				UpperCAmelCase					)
					if np.isnan(UpperCAmelCase					):
										return 0
					return aupr



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[Any]								=process_precisions(UpperCAmelCase					)
					UpperCamelCase__		:     Optional[int]								=0
					for prec, recall in zip(UpperCAmelCase   ,				UpperCAmelCase					):
										if recall >= recall_thresh:
															UpperCamelCase__		:     Any								=prec
															break
					return prec_at_recall



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any   ,				UpperCAmelCase  :				Dict					):


					'''simple docstring'''






					return normalize_answer(UpperCAmelCase					) == normalize_answer(UpperCAmelCase					)



def 							_lowerCAmelCase   (    UpperCAmelCase  :				int   ,				UpperCAmelCase  :				Dict   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     List[str]								=0
					for pred in predictions:
										for ground_truth in ground_truths:
															UpperCamelCase__		:     str								=metric_fn(UpperCAmelCase   ,				UpperCAmelCase					)
															if score == 1:  # break the loop when one prediction matches the ground truth
																				break
										if score == 1:
															break
					return score



def 							_lowerCAmelCase   (    UpperCAmelCase  :				Optional[int]   ,				UpperCAmelCase  :				str					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=0
					UpperCamelCase__		:     List[Any]								=[]
					UpperCamelCase__		:     Optional[Any]								=[]
					for article in dataset:
										for paragraph in article["paragraphs"]:
															for qa in paragraph["qas"]:
																				total += 1
																				if qa["id"] not in predictions:
																									UpperCamelCase__		:     Union[str, Any]								='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																									print(UpperCAmelCase   ,				file=sys.stderr					)
																									continue
																				UpperCamelCase__		:     Union[str, Any]								=[x['''text'''] for x in qa['''answers''']]
																				UpperCamelCase__		:     Any								=predictions[qa['''id''']]
																				UpperCamelCase__	,      UpperCamelCase__		:     int								=compute_precision_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				qa['''id''']					)

																				precisions.append(UpperCAmelCase					)
																				recalls.append(UpperCAmelCase					)

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

																				exact_match += metric_max_over_ground_truths(UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase					)

					UpperCamelCase__		:     Union[str, Any]								=[x for _, x in sorted(zip(UpperCAmelCase   ,				UpperCAmelCase					)					)]
					recalls.sort()

					UpperCamelCase__		:     Optional[int]								=100.0 * fa / total
					UpperCamelCase__		:     List[Any]								=100.0 * exact_match / total
					UpperCamelCase__		:     Dict								=get_aupr(UpperCAmelCase   ,				UpperCAmelCase					)

					UpperCamelCase__		:     Optional[Any]								=get_prec_at_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				recall_thresh=0.9					)
					UpperCamelCase__		:     Dict								=get_prec_at_recall(UpperCAmelCase   ,				UpperCAmelCase   ,				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							:	str										=			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							:	Dict										=			parser.parse_args()
				with open(args.dataset_file) as dataset_file:
								_SCREAMING_SNAKE_CASE							:	str										=			json.load(dataset_file)
								_SCREAMING_SNAKE_CASE							:	Tuple										=			dataset_json["""data"""]
				with open(args.prediction_file) as prediction_file:
								_SCREAMING_SNAKE_CASE							:	Dict										=			json.load(prediction_file)
				print(json.dumps(evaluate(dataset, predictions)))


