
import argparse
import json
import re
import string
import sys

import numpy as np


__a        = 0.5




def     a						(							snake_case__: Tuple						,       snake_case__: Optional[Any] ):


				'''simple docstring'''
				lowercase_					  = ['''.''', ''',''', ''';''', ''':''']

				for token in remove_tokens:
								lowercase_					  = ground_truth.replace(snake_case__						,       '''''' )
								lowercase_					  = prediction.replace(snake_case__						,       '''''' )

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

				lowercase_					  = ground_truth.intersection(snake_case__ )
				lowercase_					  = ground_truth.union(snake_case__ )
				lowercase_					  = len(snake_case__ ) / len(snake_case__ )
				return jaccard




def     a						(							snake_case__: Optional[Any] ):


				'''simple docstring'''

				def remove_articles(snake_case__: Union[str, Any] ):
								return re.sub(r'''\b(a|an|the)\b'''						,       ''' '''						,       snake_case__ )

				def white_space_fix(snake_case__: int ):
								return " ".join(text.split() )

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

				def lower(snake_case__: Tuple ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(snake_case__ ) ) ) )




def     a						(							snake_case__: Any						,       snake_case__: Tuple						,       snake_case__: Optional[Any] ):


				'''simple docstring'''
				lowercase_  ,     lowercase_  ,     lowercase_					  = 0, 0, 0

				lowercase_					  = '''Parties''' in qa_id

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

												if not match_found:
																fp += 1

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

				return precision, recall




def     a						(							snake_case__: Dict ):


				'''simple docstring'''
				lowercase_					  = precisions[::-1]
				for i in range(1						,       len(snake_case__ ) ):
								lowercase_					  = max(precision_best[i - 1]						,       precision_best[i] )
				lowercase_					  = precision_best[::-1]
				return precisions




def     a						(							snake_case__: List[str]						,       snake_case__: Optional[int] ):


				'''simple docstring'''
				lowercase_					  = process_precisions(snake_case__ )
				lowercase_					  = np.trapz(snake_case__						,       snake_case__ )
				if np.isnan(snake_case__ ):
								return 0
				return aupr




def     a						(							snake_case__: Union[str, Any]						,       snake_case__: Optional[int]						,       snake_case__: Any ):


				'''simple docstring'''
				lowercase_					  = process_precisions(snake_case__ )
				lowercase_					  = 0
				for prec, recall in zip(snake_case__						,       snake_case__ ):
								if recall >= recall_thresh:
												lowercase_					  = prec
												break
				return prec_at_recall




def     a						(							snake_case__: Any						,       snake_case__: str ):


				'''simple docstring'''
				return normalize_answer(snake_case__ ) == normalize_answer(snake_case__ )




def     a						(							snake_case__: str						,       snake_case__: List[Any]						,       snake_case__: Union[str, Any] ):


				'''simple docstring'''
				lowercase_					  = 0
				for pred in predictions:
								for ground_truth in ground_truths:
												lowercase_					  = metric_fn(snake_case__						,       snake_case__ )
												if score == 1:  # break the loop when one prediction matches the ground truth
																break
								if score == 1:
												break
				return score




def     a						(							snake_case__: Dict						,       snake_case__: Any ):


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

																precisions.append(snake_case__ )
																recalls.append(snake_case__ )

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

																exact_match += metric_max_over_ground_truths(snake_case__						,       snake_case__						,       snake_case__ )

				lowercase_					  = [x for _, x in sorted(zip(snake_case__						,       snake_case__ ) )]
				recalls.sort()

				lowercase_					  = 1_0_0.0 * fa / total
				lowercase_					  = 1_0_0.0 * exact_match / total
				lowercase_					  = get_aupr(snake_case__						,       snake_case__ )

				lowercase_					  = get_prec_at_recall(snake_case__						,       snake_case__						,       recall_thresh=0.9 )
				lowercase_					  = get_prec_at_recall(snake_case__						,       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__":
						__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)))






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a        = {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2Text2ForCausalLM',
						    'Speech2Text2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
						from .processing_speech_to_text_a import SpeechaTextaProcessor
						from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text_a import (
												    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextaForCausalLM,
												    SpeechaTextaPreTrainedModel,
												)

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




