code: 
  
  
  
  
  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
  
style_context: 
  
  
  
  
  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)))
  