code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import tempfile
  
  import torch
  
  from diffusers import (
      DEISMultistepScheduler,
      DPMSolverMultistepScheduler,
      DPMSolverSinglestepScheduler,
      UniPCMultistepScheduler,
  )
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				(DPMSolverMultistepScheduler,)
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				(('num_inference_steps', 2_5),)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			**A  :			Tuple		):
             _UpperCAmelCase    :			str              =					{
                 "num_train_timesteps": 1_0_0_0,
                 "beta_start": 0.0_001,
                 "beta_end": 0.02,
                 "beta_schedule": "linear",
                 "solver_order": 2,
                 "prediction_type": "epsilon",
                 "thresholding": False,
                 "sample_max_value": 1.0,
                 "algorithm_type": "dpmsolver++",
                 "solver_type": "midpoint",
                 "lower_order_final": False,
                 "lambda_min_clipped": -float("inf"		),
                 "variance_type": None,
             }
  
             config.update(**A		)
             return config
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			int=0  ,			**A  :			Tuple		):
             _UpperCAmelCase    :			List[Any]              =					dict(self.forward_default_kwargs		)
             _UpperCAmelCase    :			Tuple              =					kwargs.pop("num_inference_steps"  ,			A		)
             _UpperCAmelCase    :			Union[str, Any]              =					self.dummy_sample
             _UpperCAmelCase    :			Union[str, Any]              =					0.1 * sample
             _UpperCAmelCase    :			Tuple              =					[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Dict              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			int              =					scheduler_class(**A		)
                  scheduler.set_timesteps(A		)
                  # copy over dummy past residuals
                  _UpperCAmelCase    :			str              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A		)
                       _UpperCAmelCase    :			Tuple              =					scheduler_class.from_pretrained(A		)
                       new_scheduler.set_timesteps(A		)
                       # copy over dummy past residuals
                       _UpperCAmelCase    :			Tuple              =					dummy_past_residuals[: new_scheduler.config.solver_order]
  
                  _UpperCAmelCase       , _UpperCAmelCase    :			Tuple              =					sample, sample
                  for t in range(A  ,			time_step + scheduler.config.solver_order + 1		):
                       _UpperCAmelCase    :			int              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                       _UpperCAmelCase    :			str              =					new_scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                       assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             pass
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			str=0  ,			**A  :			Optional[Any]		):
             _UpperCAmelCase    :			Any              =					dict(self.forward_default_kwargs		)
             _UpperCAmelCase    :			Optional[Any]              =					kwargs.pop("num_inference_steps"  ,			A		)
             _UpperCAmelCase    :			str              =					self.dummy_sample
             _UpperCAmelCase    :			List[Any]              =					0.1 * sample
             _UpperCAmelCase    :			Optional[int]              =					[residual + 0.2, residual + 0.15, residual + 0.10]
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Optional[Any]              =					self.get_scheduler_config()
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler_class(**A		)
                  scheduler.set_timesteps(A		)
  
                  # copy over dummy past residuals (must be after setting timesteps)
                  _UpperCAmelCase    :			Any              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A		)
                       _UpperCAmelCase    :			Any              =					scheduler_class.from_pretrained(A		)
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A		)
  
                       # copy over dummy past residual (must be after setting timesteps)
                       _UpperCAmelCase    :			Any              =					dummy_past_residuals[: new_scheduler.config.solver_order]
  
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                  _UpperCAmelCase    :			List[Any]              =					new_scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                  assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			int=None  ,			**A  :			Union[str, Any]		):
             if scheduler is None:
                  _UpperCAmelCase    :			Optional[int]              =					self.scheduler_classes[0]
                  _UpperCAmelCase    :			List[Any]              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			str              =					scheduler_class(**A		)
  
             _UpperCAmelCase    :			List[Any]              =					1_0
             _UpperCAmelCase    :			Union[str, Any]              =					self.dummy_model()
             _UpperCAmelCase    :			List[str]              =					self.dummy_sample_deter
             scheduler.set_timesteps(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Any              =					model(A  ,			A		)
                  _UpperCAmelCase    :			List[str]              =					scheduler.step(A  ,			A  ,			A		).prev_sample
  
             return sample
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Optional[Any]              =					dict(self.forward_default_kwargs		)
  
             _UpperCAmelCase    :			str              =					kwargs.pop("num_inference_steps"  ,			A		)
  
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			int              =					self.get_scheduler_config()
                  _UpperCAmelCase    :			Any              =					scheduler_class(**A		)
  
                  _UpperCAmelCase    :			List[str]              =					self.dummy_sample
                  _UpperCAmelCase    :			int              =					0.1 * sample
  
                  if num_inference_steps is not None and hasattr(A  ,			"set_timesteps"		):
                       scheduler.set_timesteps(A		)
                  elif num_inference_steps is not None and not hasattr(A  ,			"set_timesteps"		):
                       _UpperCAmelCase    :			Union[str, Any]              =					num_inference_steps
  
                  # copy over dummy past residuals (must be done after set_timesteps)
                  _UpperCAmelCase    :			Optional[Any]              =					[residual + 0.2, residual + 0.15, residual + 0.10]
                  _UpperCAmelCase    :			str              =					dummy_past_residuals[: scheduler.config.solver_order]
  
                  _UpperCAmelCase    :			Optional[Any]              =					scheduler.timesteps[5]
                  _UpperCAmelCase    :			str              =					scheduler.timesteps[6]
  
                  _UpperCAmelCase    :			Any              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
                  _UpperCAmelCase    :			Dict              =					scheduler.step(A  ,			A  ,			A  ,			**A		).prev_sample
  
                  self.assertEqual(output_a.shape  ,			sample.shape		)
                  self.assertEqual(output_a.shape  ,			output_a.shape		)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             self.check_over_configs(thresholding=A		)
             for order in [1, 2, 3]:
                  for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                 self.check_over_configs(
                                     thresholding=A  ,			prediction_type=A  ,			sample_max_value=A  ,			algorithm_type="dpmsolver++"  ,			solver_order=A  ,			solver_type=A  ,			)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=A		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                  for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                 if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                      if order == 3:
                                           continue
                                 else:
                                      self.check_over_configs(
                                          solver_order=A  ,			solver_type=A  ,			prediction_type=A  ,			algorithm_type=A  ,			)
                                 _UpperCAmelCase    :			Union[str, Any]              =					self.full_loop(
                                     solver_order=A  ,			solver_type=A  ,			prediction_type=A  ,			algorithm_type=A  ,			)
                                 assert not torch.isnan(A		).any(), "Samples have nan numbers"
  
  
  
        def 		snake_case_      (  self  :			int		):
             self.check_over_configs(lower_order_final=A		)
             self.check_over_configs(lower_order_final=A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]		):
             self.check_over_configs(lambda_min_clipped=-float("inf"		)		)
             self.check_over_configs(lambda_min_clipped=-5.1		)
  
  
  
        def 		snake_case_      (  self  :			str		):
             self.check_over_configs(variance_type=A		)
             self.check_over_configs(variance_type="learned_range"		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                  self.check_over_forward(num_inference_steps=A  ,			time_step=0		)
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			List[Any]              =					self.full_loop()
             _UpperCAmelCase    :			Any              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			List[str]              =					self.full_loop(thresholding=A  ,			dynamic_thresholding_ratio=0.87  ,			sample_max_value=0.5		)
             _UpperCAmelCase    :			List[Any]              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 1.1_364		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[Any]              =					self.full_loop(prediction_type="v_prediction"		)
             _UpperCAmelCase    :			Any              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.2_251		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			str              =					self.full_loop(prediction_type="v_prediction"  ,			use_karras_sigmas=A		)
             _UpperCAmelCase    :			Optional[Any]              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.2_096		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             # make sure that iterating over schedulers with same config names gives same results
             # for defaults
             _UpperCAmelCase    :			Union[str, Any]              =					DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
             _UpperCAmelCase    :			Optional[Any]              =					self.full_loop(scheduler=A		)
             _UpperCAmelCase    :			int              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
             _UpperCAmelCase    :			Union[str, Any]              =					DPMSolverSinglestepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			Dict              =					UniPCMultistepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			List[Any]              =					DEISMultistepScheduler.from_config(scheduler.config		)
             _UpperCAmelCase    :			List[Any]              =					DPMSolverMultistepScheduler.from_config(scheduler.config		)
  
             _UpperCAmelCase    :			Any              =					self.full_loop(scheduler=A		)
             _UpperCAmelCase    :			str              =					torch.mean(torch.abs(A		)		)
  
             assert abs(result_mean.item() - 0.3_301		) < 1e-3
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Union[str, Any]              =					self.scheduler_classes[0]
             _UpperCAmelCase    :			Optional[int]              =					self.get_scheduler_config(thresholding=A  ,			dynamic_thresholding_ratio=0		)
             _UpperCAmelCase    :			List[Any]              =					scheduler_class(**A		)
  
             _UpperCAmelCase    :			int              =					1_0
             _UpperCAmelCase    :			Tuple              =					self.dummy_model()
             _UpperCAmelCase    :			List[Any]              =					self.dummy_sample_deter.half()
             scheduler.set_timesteps(A		)
  
             for i, t in enumerate(scheduler.timesteps		):
                  _UpperCAmelCase    :			Union[str, Any]              =					model(A  ,			A		)
                  _UpperCAmelCase    :			Tuple              =					scheduler.step(A  ,			A  ,			A		).prev_sample
  
             assert sample.dtype == torch.floataa
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]  ,			**A  :			str		):
             for scheduler_class in self.scheduler_classes:
                  _UpperCAmelCase    :			Any              =					self.get_scheduler_config(**A		)
                  _UpperCAmelCase    :			Union[str, Any]              =					scheduler_class(**A		)
  
                  scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
                  assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  import json
  import re
  import string
  import sys
  from collections import Counter
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]       )						->     Union[str, Any]:
  
  
  
  
  
       '''simple docstring'''
  
       def remove_articles(SCREAMING_SNAKE_CASE__			:		Tuple       ):
            return re.sub(R"\b(a|an|the)\b"		,						" "		,						SCREAMING_SNAKE_CASE__       )
  
       def white_space_fix(SCREAMING_SNAKE_CASE__			:		Any       ):
            return " ".join(text.split()       )
  
       def remove_punc(SCREAMING_SNAKE_CASE__			:		Optional[int]       ):
            _UpperCAmelCase    :			List[str]              =					set(string.punctuation       )
            return "".join(ch for ch in text if ch not in exclude       )
  
       def lower(SCREAMING_SNAKE_CASE__			:		List[str]       ):
            return text.lower()
  
       return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__       )       )       )       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		str       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[Any]              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			Tuple              =					normalize_answer(SCREAMING_SNAKE_CASE__       ).split()
       _UpperCAmelCase    :			List[Any]              =					Counter(SCREAMING_SNAKE_CASE__       ) & Counter(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			List[str]              =					sum(common.values()       )
       if num_same == 0:
            return 0
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					1.0 * num_same / len(SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			Union[str, Any]              =					(2 * precision * recall) / (precision + recall)
       return fa
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Dict       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       return normalize_answer(SCREAMING_SNAKE_CASE__       ) == normalize_answer(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		List[Any]		,						SCREAMING_SNAKE_CASE__			:		Dict		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			int              =					[]
       for ground_truth in ground_truths:
            _UpperCAmelCase    :			Dict              =					metric_fn(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__       )
       return max(SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[int]		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Tuple:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			str              =					0
       _UpperCAmelCase    :			int              =					[]
       for passage in dataset:
            for qa in passage["qas"]:
                 total += 1
                 if qa["id"] not in predictions:
                      _UpperCAmelCase    :			List[Any]              =					f'Unanswered question {qa["id"]} will receive score 0.'
                      print(SCREAMING_SNAKE_CASE__		,						file=sys.stderr       )
                      continue
  
                 _UpperCAmelCase    :			Union[str, Any]              =					[x["text"] for x in qa["answers"]]
                 _UpperCAmelCase    :			Optional[int]              =					predictions[qa["id"]]
  
                 _UpperCAmelCase    :			int              =					metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
                 if int(_exact_match       ) == 1:
                      correct_ids.append(qa["id"]       )
                 exact_match += _exact_match
  
                 fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       _UpperCAmelCase    :			List[str]              =					exact_match / total
       _UpperCAmelCase    :			str              =					fa / total
  
       return {"exact_match": exact_match, "f1": fa}, correct_ids
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      Any							=							"1.0"
   _lowerCAmelCase       :      Any							=							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       :      Tuple							=							parser.parse_args()
  
   with open(args.data_file) as data_file:
    _lowerCAmelCase       :      int							=							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       :      List[str]							=							dataset_json["data"]
  
   with open(args.pred_file) as pred_file:
    _lowerCAmelCase       :      str							=							json.load(pred_file)
  
   _lowerCAmelCase,		_lowerCAmelCase       :      List[Any]							=							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)
  