





"""simple docstring"""


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





def 							a__     (					_UpperCAmelCase		)      ->					Dict:

 '''simple docstring'''






 def remove_articles(_UpperCAmelCase		):
  return re.sub(R"\b(a|an|the)\b"		,       " "		,       lowercase_		)

 def white_space_fix(_UpperCAmelCase		):
  return " ".join(text.split()		)

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

 def lower(_UpperCAmelCase		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(lowercase_		)		)		)		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Dict:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					List[str]:

 '''simple docstring'''






 return normalize_answer(lowercase_		) == normalize_answer(lowercase_		)





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		)      ->					Union[str, Any]:

 '''simple docstring'''






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





def 							a__     (					_UpperCAmelCase		,       _UpperCAmelCase		)      ->					Any:

 '''simple docstring'''






 snake_case__      							=			snake_case__      							=			snake_case__      							=			0
 for article in dataset:
  for paragraph in article["paragraphs"]:
   for qa in paragraph["qas"]:
    total += 1
    if qa["id"] not in predictions:
     snake_case__      							=			"Unanswered question " + qa["id"] + " will receive score 0."
     print(lowercase_		,       file=sys.stderr		)
     continue
    snake_case__      							=			[x["text"] for x in qa["answers"]]
    snake_case__      							=			predictions[qa["id"]]
    exact_match += metric_max_over_ground_truths(lowercase_		,       lowercase_		,       lowercase_		)
    fa += metric_max_over_ground_truths(lowercase_		,       lowercase_		,       lowercase_		)

 snake_case__      							=			100.0 * exact_match / total
 snake_case__      							=			100.0 * fa / total

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


if __name__ == "__main__":
   a__   :       int   						=			'1.1'
   a__   :       Dict   						=			argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
   parser.add_argument("""dataset_file""", help="""Dataset file""")
   parser.add_argument("""prediction_file""", help="""Prediction File""")
   a__   :       Optional[Any]   						=			parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      a__   :       Optional[int]   						=			json.load(dataset_file)
      if dataset_json["version"] != expected_version:
         print(
             """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
             file=sys.stderr,
         )
      a__   :       Union[str, Any]   						=			dataset_json['data']
   with open(args.prediction_file) as prediction_file:
      a__   :       List[str]   						=			json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

