
import argparse
import json
import re
import string
import sys

import numpy as np


lowercase__    :					Optional[Any]					=						0.5


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					[".", ",", ";", ":"]

	for token in remove_tokens:
		snake_case_		   =					ground_truth.replace(_A					,     ""							)
		snake_case_		   =					prediction.replace(_A					,     ""							)

	snake_case_   ,				snake_case_		   =					ground_truth.lower(), prediction.lower()
	snake_case_   ,				snake_case_		   =					ground_truth.replace("/"					,     " "							), prediction.replace("/"					,     " "							)
	snake_case_   ,				snake_case_		   =					set(ground_truth.split(" "							)							), set(prediction.split(" "							)							)

	snake_case_		   =					ground_truth.intersection(_A							)
	snake_case_		   =					ground_truth.union(_A							)
	snake_case_		   =					len(_A							) / len(_A							)
	return jaccard


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	def remove_articles(_A							):
		return re.sub(R"\b(a|an|the)\b"					,     " "					,     _A							)

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

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

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

	return white_space_fix(remove_articles(remove_punc(lower(_A							)							)							)							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_   ,				snake_case_   ,				snake_case_		   =					0, 0, 0

	snake_case_		   =					"Parties" in qa_id

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

			if not match_found:
				fp += 1

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

	return precision, recall


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					precisions[::-1]
	for i in range(1					,     len(_A							)							):
		snake_case_		   =					max(precision_best[i - 1]					,     precision_best[i]							)
	snake_case_		   =					precision_best[::-1]
	return precisions


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					process_precisions(_A							)
	snake_case_		   =					np.trapz(_A					,     _A							)
	if np.isnan(_A							):
		return 0
	return aupr


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					process_precisions(_A							)
	snake_case_		   =					0
	for prec, recall in zip(_A					,     _A							):
		if recall >= recall_thresh:
			snake_case_		   =					prec
			break
	return prec_at_recall


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





	return normalize_answer(_A							) == normalize_answer(_A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					0
	for pred in predictions:
		for ground_truth in ground_truths:
			snake_case_		   =					metric_fn(_A					,     _A							)
			if score == 1:  # break the loop when one prediction matches the ground truth
				break
		if score == 1:
			break
	return score


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





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

				precisions.append(_A							)
				recalls.append(_A							)

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

				exact_match += metric_max_over_ground_truths(_A					,     _A					,     _A							)

	snake_case_		   =					[x for _, x in sorted(zip(_A					,     _A							)							)]
	recalls.sort()

	snake_case_		   =					100.0 * fa / total
	snake_case_		   =					100.0 * exact_match / total
	snake_case_		   =					get_aupr(_A					,     _A							)

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






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=__lowercase						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
