


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Optional[Any]     		=		(DDPMScheduler,)






   def _snake_case    (		self						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				{
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**__A )
     return config






   def _snake_case    (		self ):






     """simple docstring"""





     for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=__A )






   def _snake_case    (		self ):






     """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 _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """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 _snake_case    (		self ):






     """simple docstring"""





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






   def _snake_case    (		self ):






     """simple docstring"""





     for t in [0, 500, 999]:
       self.check_over_forward(time_step=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				self.scheduler_classes[0]
     lowerCamelCase :					Any    =				self.get_scheduler_config()
     lowerCamelCase :					List[Any]    =				scheduler_class(**__A )

     assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00979 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Any    =				self.scheduler_classes[0]
     lowerCamelCase :					List[Any]    =				self.get_scheduler_config()
     lowerCamelCase :					Optional[Any]    =				scheduler_class(**__A )

     lowerCamelCase :					List[str]    =				len(__A )

     lowerCamelCase :					str    =				self.dummy_model()
     lowerCamelCase :					int    =				self.dummy_sample_deter
     lowerCamelCase :					int    =				torch.manual_seed(0 )

     for t in reversed(range(__A ) ):
       # 1. predict noise residual
       lowerCamelCase :					List[str]    =				model(__A						,				__A )

       # 2. predict previous mean of sample x_t-1
       lowerCamelCase :					Optional[Any]    =				scheduler.step(__A						,				__A						,				__A						,				generator=__A ).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       lowerCamelCase :					Dict    =				pred_prev_sample

     lowerCamelCase :					Dict    =				torch.sum(torch.abs(__A ) )
     lowerCamelCase :					int    =				torch.mean(torch.abs(__A ) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					int    =				self.scheduler_classes[0]
     lowerCamelCase :					Optional[Any]    =				self.get_scheduler_config(prediction_type="v_prediction" )
     lowerCamelCase :					Any    =				scheduler_class(**__A )

     lowerCamelCase :					List[str]    =				len(__A )

     lowerCamelCase :					Any    =				self.dummy_model()
     lowerCamelCase :					Tuple    =				self.dummy_sample_deter
     lowerCamelCase :					int    =				torch.manual_seed(0 )

     for t in reversed(range(__A ) ):
       # 1. predict noise residual
       lowerCamelCase :					List[Any]    =				model(__A						,				__A )

       # 2. predict previous mean of sample x_t-1
       lowerCamelCase :					int    =				scheduler.step(__A						,				__A						,				__A						,				generator=__A ).prev_sample

       # if t > 0:
       #     noise = self.dummy_sample_deter
       #     variance = scheduler.get_variance(t) ** (0.5) * noise
       #
       # sample = pred_prev_sample + variance
       lowerCamelCase :					Union[str, Any]    =				pred_prev_sample

     lowerCamelCase :					str    =				torch.sum(torch.abs(__A ) )
     lowerCamelCase :					Dict    =				torch.mean(torch.abs(__A ) )

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.scheduler_classes[0]
     lowerCamelCase :					Optional[int]    =				self.get_scheduler_config()
     lowerCamelCase :					List[Any]    =				scheduler_class(**__A )

     lowerCamelCase :					List[Any]    =				[100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=__A )

     lowerCamelCase :					List[str]    =				scheduler.timesteps

     for i, timestep in enumerate(__A ):
       if i == len(__A ) - 1:
         lowerCamelCase :					Dict    =				-1
       else:
         lowerCamelCase :					Any    =				timesteps[i + 1]

       lowerCamelCase :					str    =				scheduler.previous_timestep(__A )
       lowerCamelCase :					Optional[int]    =				prev_t.item()

       self.assertEqual(__A						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				self.scheduler_classes[0]
     lowerCamelCase :					Union[str, Any]    =				self.get_scheduler_config()
     lowerCamelCase :					Tuple    =				scheduler_class(**__A )

     lowerCamelCase :					Optional[int]    =				[100, 87, 50, 51, 0]

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






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[int]    =				self.scheduler_classes[0]
     lowerCamelCase :					int    =				self.get_scheduler_config()
     lowerCamelCase :					Optional[Any]    =				scheduler_class(**__A )

     lowerCamelCase :					Optional[Any]    =				[100, 87, 50, 1, 0]
     lowerCamelCase :					Union[str, Any]    =				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 _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				self.scheduler_classes[0]
     lowerCamelCase :					int    =				self.get_scheduler_config()
     lowerCamelCase :					Any    =				scheduler_class(**__A )

     lowerCamelCase :					int    =				[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


_snake_case             =     '''docs/source/_static/js/custom.js'''






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  with open(SCREAMING_SNAKE_CASE_      ,						encoding="utf-8"      ,						newline="\n"	) as f:
    lowerCamelCase :					List[str]    =				f.readlines()
  lowerCamelCase :					int    =				0

  # First let's put the right version
  while not lines[index].startswith("const stableVersion ="	):
    index += 1
  lowerCamelCase :					str    =				f"""const stableVersion = \"v{version}\"\n"""

  # Then update the dictionary
  while not lines[index].startswith("const versionMapping = {"	):
    index += 1

  # We go until the end
  while not lines[index].startswith("}"	):
    index += 1
  # We add the new version at the end
  lines[index - 1] += f"""    \"v{version}\": \"v{version}\",\n"""

  with open(SCREAMING_SNAKE_CASE_      ,						"w"      ,						encoding="utf-8"      ,						newline="\n"	) as f:
    f.writelines(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   _snake_case             =     argparse.ArgumentParser()
   parser.add_argument('''--version''', help='''Release version.''')
   _snake_case             =     parser.parse_args()
   update_custom_js(args.version)



