import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  (DDPMParallelScheduler,)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				{
               '''num_train_timesteps''': 10_00,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**A_						)
           return config



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           for timesteps in [1, 5, 1_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """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=A_				,				beta_end=A_						)



     def 		UpperCamelCase_						(					self						) ->				Any:






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



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=A_						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=A_						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






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



     def 		UpperCamelCase_						(					self						) ->				str:






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



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           for t in [0, 5_00, 9_99]:
                 self.check_over_forward(time_step=A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.00979						)						) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1E-5



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

           _lowerCamelCase			    =				self.dummy_model()
           _lowerCamelCase			    =				self.dummy_sample_deter
           _lowerCamelCase			    =				self.dummy_sample_deter + 0.1
           _lowerCamelCase			    =				self.dummy_sample_deter - 0.1

           _lowerCamelCase			    =				samplea.shape[0]
           _lowerCamelCase			    =				torch.stack([samplea, samplea, samplea]				,				dim=0						)
           _lowerCamelCase			    =				torch.arange(A_						)[0:3, None].repeat(1				,				A_						)

           _lowerCamelCase			    =				model(samples.flatten(0				,				1						)				,				timesteps.flatten(0				,				1						)						)
           _lowerCamelCase			    =				scheduler.batch_step_no_noise(A_				,				timesteps.flatten(0				,				1						)				,				samples.flatten(0				,				1						)						)

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

           assert abs(result_sum.item() - 1153.1833						) < 1E-2
           assert abs(result_mean.item() - 0.5005						) < 1E-3



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

           _lowerCamelCase			    =				self.dummy_model()
           _lowerCamelCase			    =				self.dummy_sample_deter
           _lowerCamelCase			    =				torch.manual_seed(0						)

           for t in reversed(range(A_						)						):
                 # 1. predict noise residual
                 _lowerCamelCase			    =				model(A_				,				A_						)

                 # 2. predict previous mean of sample x_t-1
                 _lowerCamelCase			    =				scheduler.step(A_				,				A_				,				A_				,				generator=A_						).prev_sample

                 _lowerCamelCase			    =				pred_prev_sample

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

           assert abs(result_sum.item() - 258.9606						) < 1E-2
           assert abs(result_mean.item() - 0.3372						) < 1E-3



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config(prediction_type='''v_prediction'''						)
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				len(A_						)

           _lowerCamelCase			    =				self.dummy_model()
           _lowerCamelCase			    =				self.dummy_sample_deter
           _lowerCamelCase			    =				torch.manual_seed(0						)

           for t in reversed(range(A_						)						):
                 # 1. predict noise residual
                 _lowerCamelCase			    =				model(A_				,				A_						)

                 # 2. predict previous mean of sample x_t-1
                 _lowerCamelCase			    =				scheduler.step(A_				,				A_				,				A_				,				generator=A_						).prev_sample

                 _lowerCamelCase			    =				pred_prev_sample

           _lowerCamelCase			    =				torch.sum(torch.abs(A_						)						)
           _lowerCamelCase			    =				torch.mean(torch.abs(A_						)						)

           assert abs(result_sum.item() - 202.0296						) < 1E-2
           assert abs(result_mean.item() - 0.2631						) < 1E-3



     def 		UpperCamelCase_						(					self						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=A_						)

           _lowerCamelCase			    =				scheduler.timesteps

           for i, timestep in enumerate(A_						):
                 if i == len(A_						) - 1:
                       _lowerCamelCase			    =				-1
                 else:
                       _lowerCamelCase			    =				timesteps[i + 1]

                 _lowerCamelCase			    =				scheduler.previous_timestep(A_						)
                 _lowerCamelCase			    =				prev_t.item()

                 self.assertEqual(A_				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 51, 0]

           with self.assertRaises(A_				,				msg='''`custom_timesteps` must be in descending order.'''						):
                 scheduler.set_timesteps(timesteps=A_						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[1_00, 87, 50, 1, 0]
           _lowerCamelCase			    =				len(A_						)

           with self.assertRaises(A_				,				msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''						):
                 scheduler.set_timesteps(num_inference_steps=A_				,				timesteps=A_						)





     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				self.scheduler_classes[0]
           _lowerCamelCase			    =				self.get_scheduler_config()
           _lowerCamelCase			    =				scheduler_class(**A_						)

           _lowerCamelCase			    =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               A_				,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,				):
                 scheduler.set_timesteps(timesteps=A_						)
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				parser.parse_args()

      with open(args.src_path ,						'''r'''		) as src_file, open(args.evaluation_set ,						'''w'''		) as eval_file, open(
          args.gold_data_path ,						'''w'''		) as gold_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()