

import argparse
import json
import re
import string
import sys
from collections import Counter



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str] )     -> Union[str, Any]:




				'''simple docstring'''





				def remove_articles(SCREAMING_SNAKE_CASE_:							List[str] ):
								return re.sub(R"\b(a|an|the)\b"		,    " "		,    _UpperCamelCase )

				def white_space_fix(SCREAMING_SNAKE_CASE_:							Any ):
								return " ".join(text.split() )

				def remove_punc(SCREAMING_SNAKE_CASE_:							List[Any] ):
								A__            =     set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(SCREAMING_SNAKE_CASE_:							Union[str, Any] ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase ) ) ) )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any] )     -> Dict:




				'''simple docstring'''





				A__            =     normalize_answer(_UpperCamelCase ).split()
				A__            =     normalize_answer(_UpperCamelCase ).split()
				A__            =     Counter(_UpperCamelCase ) & Counter(_UpperCamelCase )
				A__            =     sum(common.values() )
				if num_same == 0:
								return 0
				A__            =     1.0 * num_same / len(_UpperCamelCase )
				A__            =     1.0 * num_same / len(_UpperCamelCase )
				A__            =     (2 * precision * recall) / (precision + recall)
				return fa



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]		,    SCREAMING_SNAKE_CASE_:							Union[str, Any] )     -> Union[str, Any]:




				'''simple docstring'''





				return normalize_answer(_UpperCamelCase ) == normalize_answer(_UpperCamelCase )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple		,    SCREAMING_SNAKE_CASE_:							Union[str, Any]		,    SCREAMING_SNAKE_CASE_:							Optional[int] )     -> Optional[int]:




				'''simple docstring'''





				A__            =     []
				for ground_truth in ground_truths:
								A__            =     metric_fn(_UpperCamelCase		,    _UpperCamelCase )
								scores_for_ground_truths.append(_UpperCamelCase )
				return max(_UpperCamelCase )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,    SCREAMING_SNAKE_CASE_:							str )     -> Dict:




				'''simple docstring'''





				A__            =     0
				A__            =     []
				for passage in dataset:
								for qa in passage["qas"]:
												total += 1
												if qa["id"] not in predictions:
																A__            =     F'Unanswered question {qa["id"]} will receive score 0.'
																print(_UpperCamelCase		,    file=sys.stderr )
																continue

												A__            =     [x["text"] for x in qa["answers"]]
												A__            =     predictions[qa["id"]]

												A__            =     metric_max_over_ground_truths(_UpperCamelCase		,    _UpperCamelCase		,    _UpperCamelCase )
												if int(_exact_match ) == 1:
																correct_ids.append(qa["id"] )
												exact_match += _exact_match

												fa += metric_max_over_ground_truths(_UpperCamelCase		,    _UpperCamelCase		,    _UpperCamelCase )

				A__            =     exact_match / total
				A__            =     fa / total

				return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
	lowerCAmelCase__						=	"""1.0"""
	lowerCAmelCase__						=	argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
	parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
	parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
	parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
	lowerCAmelCase__						=	parser.parse_args()

	with open(args.data_file) as data_file:
		lowerCAmelCase__						=	json.load(data_file)
		if dataset_json["version"] != expected_version:
			print(
			    f"""Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}""",
			    file=sys.stderr,
			)
		lowerCAmelCase__						=	dataset_json["""data"""]

	with open(args.pred_file) as pred_file:
		lowerCAmelCase__						=	json.load(pred_file)

	lowerCAmelCase__				,       lowerCAmelCase__						=	evaluate(dataset, predictions)

	if args.output_correct_ids:
		print(f"""Output {len(correct_ids)} correctly answered question IDs.""")
		with open("""correct_ids.json""", """w""") as f:
			json.dump(correct_ids, f)




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (DDIMScheduler,)
			__lowerCamelCase	    =    (('eta', 0.0), ('num_inference_steps', 50))


			def        UpperCamelCase						(      self , **lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**lowercase    )
							return config


			def        UpperCamelCase						(      self , **lowercase    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config(**lowercase    )
							A__            =     scheduler_class(**lowercase    )

							A__      ,			A__            =     10, 0.0

							A__            =     self.dummy_model()
							A__            =     self.dummy_sample_deter

							scheduler.set_timesteps(lowercase    )

							for t in scheduler.timesteps:
											A__            =     model(lowercase , lowercase    )
											A__            =     scheduler.step(lowercase , lowercase , lowercase , lowercase    ).prev_sample

							return sample


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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

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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=lowercase , beta_end=lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config()
							A__            =     scheduler_class(**lowercase    )

							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_4771    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980 , 960    ) - 0.3_2460    )    ) < 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_0979    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 , 998    ) - 0.02    )    ) < 1e-5


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     self.full_loop()

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 172.0067    ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     self.full_loop(prediction_type="v_prediction"    )

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 52.5302    ) < 1e-2
							assert abs(result_mean.item() - 0.0684    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.8295    ) < 1e-2
							assert abs(result_mean.item() - 0.1951    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.0784    ) < 1e-2
							assert abs(result_mean.item() - 0.1941    ) < 1e-3

