




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   (DDIMScheduler,)
	A						       =   (('''eta''', 0.0), ('''num_inference_steps''', 50))





	def 	A_(       self			,    **SCREAMING_SNAKE_CASE			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	{
				    'num_train_timesteps': 1000,
				    'beta_start': 0.0_0_0_1,
				    'beta_end': 0.0_2,
				    'beta_schedule': 'linear',
				    'clip_sample': True,
				}

				config.update(**SCREAMING_SNAKE_CASE			)
				return config





	def 	A_(       self			,    **SCREAMING_SNAKE_CASE			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config(**SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	scheduler_class(**SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	10, 0.0

				SCREAMING_SNAKE_CASE_			=	self.dummy_model()
				SCREAMING_SNAKE_CASE_			=	self.dummy_sample_deter

				scheduler.set_timesteps(SCREAMING_SNAKE_CASE			)

				for t in scheduler.timesteps:
							SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE_			=	scheduler.step(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			).prev_sample

				return sample





	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				for timesteps in [100, 500, 1000]:
							self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config(steps_offset=1			)
				SCREAMING_SNAKE_CASE_			=	scheduler_class(**SCREAMING_SNAKE_CASE			)
				scheduler.set_timesteps(5			)
				assert torch.equal(scheduler.timesteps			,    torch.LongTensor([801, 601, 401, 201, 1]			)			)





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]			,    [0.0_0_2, 0.0_2, 0.2, 2]			):
							self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE			,    beta_end=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				List[Any]:




				"""simple docstring"""



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





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				List[Any]:




				"""simple docstring"""



				for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				Optional[int]:




				"""simple docstring"""



				self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE			)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(
										    thresholding=SCREAMING_SNAKE_CASE			,    prediction_type=SCREAMING_SNAKE_CASE			,    sample_max_value=SCREAMING_SNAKE_CASE			,    )





	def 	A_(       self			)     ->				List[Any]:




				"""simple docstring"""



				for t in [1, 10, 49]:
							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				for t, num_inference_steps in zip([1, 10, 50]			,    [10, 50, 500]			):
							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE			,    num_inference_steps=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				for t, eta in zip([1, 10, 49]			,    [0.0, 0.5, 1.0]			):
							self.check_over_forward(time_step=SCREAMING_SNAKE_CASE			,    eta=SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.scheduler_classes[0]
				SCREAMING_SNAKE_CASE_			=	self.get_scheduler_config()
				SCREAMING_SNAKE_CASE_			=	scheduler_class(**SCREAMING_SNAKE_CASE			)

				assert torch.sum(torch.abs(scheduler._get_variance(0			,    0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(420			,    400			) - 0.1_4_7_7_1			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(980			,    960			) - 0.3_2_4_6_0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(0			,    0			) - 0.0			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487			,    486			) - 0.0_0_9_7_9			)			) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999			,    998			) - 0.0_2			)			) < 1e-5





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.full_loop()

				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE			)			)
				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE			)			)

				assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
				assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.full_loop(prediction_type='v_prediction'			)

				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE			)			)
				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE			)			)

				assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
				assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3





	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE			,    beta_start=0.0_1			)
				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE			)			)
				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE			)			)

				assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
				assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE			,    beta_start=0.0_1			)
				SCREAMING_SNAKE_CASE_			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE			)			)
				SCREAMING_SNAKE_CASE_			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE			)			)

				assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
				assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3






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)))
