


"""simple docstring"""





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






def 	__UpperCAmelCase					(       snake_case_  :  List[Any]   )							->	Dict:






   """simple docstring"""


   def remove_articles(snake_case_  :  str   ):
      return re.sub(r"""\b(a|an|the)\b"""   ,	""" """   ,	snake_case_   )

   def white_space_fix(snake_case_  :  Optional[int]   ):
      return " ".join(text.split()   )

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

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

   return white_space_fix(remove_articles(remove_punc(lower(snake_case_   )   )   )   )






def 	__UpperCAmelCase					(       snake_case_  :  Dict   ,	snake_case_  :  Tuple   )							->	Optional[Any]:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  Union[str, Any]   )							->	int:






   """simple docstring"""


   return normalize_answer(snake_case_   ) == normalize_answer(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  int   ,	snake_case_  :  Tuple   ,	snake_case_  :  Dict   )							->	List[Any]:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Dict:






   """simple docstring"""


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

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

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

         fa += metric_max_over_ground_truths(snake_case_   ,	snake_case_   ,	snake_case_   )

   _lowerCAmelCase					     =		exact_match / total
   _lowerCAmelCase					     =		fa / total

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	'''1.0'''
  SCREAMING_SNAKE_CASE				:     Dict          =	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.''')
  SCREAMING_SNAKE_CASE				:     str          =	parser.parse_args()

  with open(args.data_file) as data_file:
    SCREAMING_SNAKE_CASE				:     Optional[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,
      )
    SCREAMING_SNAKE_CASE				:     Union[str, Any]          =	dataset_json['''data''']

  with open(args.pred_file) as pred_file:
    SCREAMING_SNAKE_CASE				:     List[str]          =	json.load(pred_file)

  SCREAMING_SNAKE_CASE				:     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)




"""simple docstring"""





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   (HeunDiscreteScheduler,)
   __UpperCamelCase									=   10







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		{
          """num_train_timesteps""": 1_100,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
      }

      config.update(**lowerCamelCase				)
      return config







   def 							A__						(self				):




      '''simple docstring'''




      for timesteps in [10, 50, 100, 1_000]:
         self.check_over_configs(num_train_timesteps=lowerCamelCase				)







   def 							A__						(self				):




      '''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=lowerCamelCase       ,	beta_end=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self				):




      '''simple docstring'''




      for clip_sample_range in [1.0, 2.0, 3.0]:
         self.check_over_configs(clip_sample_range=lowerCamelCase       ,	clip_sample=lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config(prediction_type="""v_prediction"""				)
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for i, t in enumerate(scheduler.timesteps				):
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07				) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10				) < 1e-3
      else:
         # CUDA
         assert abs(result_sum.item() - 4.693428650170972e-07				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      if str(lowerCamelCase				).startswith("""cpu"""				):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3
      elif str(lowerCamelCase				).startswith("""mps"""				):
         # Larger tolerance on mps
         assert abs(result_mean.item() - 0.0002				) < 1e-2
      else:
         # CUDA
         assert abs(result_sum.item() - 0.1233				) < 1e-2
         assert abs(result_mean.item() - 0.0002				) < 1e-3







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.scheduler_classes[0]
      _lowerCAmelCase					     =		self.get_scheduler_config()
      _lowerCAmelCase					     =		scheduler_class(**lowerCamelCase       ,	use_karras_sigmas=lowerCamelCase				)

      scheduler.set_timesteps(self.num_inference_steps       ,	device=lowerCamelCase				)

      _lowerCAmelCase					     =		self.dummy_model()
      _lowerCAmelCase					     =		self.dummy_sample_deter.to(lowerCamelCase				) * scheduler.init_noise_sigma
      _lowerCAmelCase					     =		sample.to(lowerCamelCase				)

      for t in scheduler.timesteps:
         _lowerCAmelCase					     =		scheduler.scale_model_input(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		model(lowerCamelCase       ,	lowerCamelCase				)

         _lowerCAmelCase					     =		scheduler.step(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		output.prev_sample

      _lowerCAmelCase					     =		torch.sum(torch.abs(lowerCamelCase				)				)
      _lowerCAmelCase					     =		torch.mean(torch.abs(lowerCamelCase				)				)

      assert abs(result_sum.item() - 0.0_0015				) < 1e-2
      assert abs(result_mean.item() - 1.9869554535034695e-07				) < 1e-2