




"""simple docstring"""



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class       UpperCAmelCase_    (		A_				):
    lowercase__		   = (DDIMScheduler,)
    lowercase__		   = (('''eta''', 0.0), ('''num_inference_steps''', 50))




    def 				__magic_name__				(       self		:			int      ,					**snake_case_		:			str ) ->				Tuple:




     '''simple docstring'''

     A__			=							{
         "num_train_timesteps": 1_000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**snake_case_ )
     return config




    def 				__magic_name__				(       self		:			List[Any]      ,					**snake_case_		:			Tuple ) ->				Any:




     '''simple docstring'''

     A__			=							self.scheduler_classes[0]
     A__			=							self.get_scheduler_config(**snake_case_ )
     A__			=							scheduler_class(**snake_case_ )

     A__, A__			=							10, 0.0

     A__			=							self.dummy_model()
     A__			=							self.dummy_sample_deter

     scheduler.set_timesteps(snake_case_ )

     for t in scheduler.timesteps:
      A__			=							model(snake_case_      ,					snake_case_ )
      A__			=							scheduler.step(snake_case_      ,					snake_case_      ,					snake_case_      ,					snake_case_ ).prev_sample

     return sample




    def 				__magic_name__				(       self		:			int ) ->				Any:




     '''simple docstring'''

     for timesteps in [100, 500, 1_000]:
      self.check_over_configs(num_train_timesteps=snake_case_ )




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				Optional[Any]:




     '''simple docstring'''

     for steps_offset in [0, 1]:
      self.check_over_configs(steps_offset=snake_case_ )

     A__			=							self.scheduler_classes[0]
     A__			=							self.get_scheduler_config(steps_offset=1 )
     A__			=							scheduler_class(**snake_case_ )
     scheduler.set_timesteps(5 )
     assert torch.equal(scheduler.timesteps      ,					torch.LongTensor([801, 601, 401, 201, 1] ) )




    def 				__magic_name__				(       self		:			List[str] ) ->				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=snake_case_      ,					beta_end=snake_case_ )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Dict:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Any ) ->				Any:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Any ) ->				Dict:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			str ) ->				Dict:




     '''simple docstring'''

     for timestep_spacing in ["trailing", "leading"]:
      self.check_over_configs(timestep_spacing=snake_case_ )




    def 				__magic_name__				(       self		:			List[str] ) ->				Any:




     '''simple docstring'''

     for rescale_betas_zero_snr in [True, False]:
      self.check_over_configs(rescale_betas_zero_snr=snake_case_ )




    def 				__magic_name__				(       self		:			Tuple ) ->				int:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			int ) ->				Any:




     '''simple docstring'''

     for t in [1, 10, 49]:
      self.check_over_forward(time_step=snake_case_ )




    def 				__magic_name__				(       self		:			Tuple ) ->				Any:




     '''simple docstring'''

     for t, num_inference_steps in zip([1, 10, 50]      ,					[10, 50, 500] ):
      self.check_over_forward(time_step=snake_case_      ,					num_inference_steps=snake_case_ )




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




     '''simple docstring'''

     for t, eta in zip([1, 10, 49]      ,					[0.0, 0.5, 1.0] ):
      self.check_over_forward(time_step=snake_case_      ,					eta=snake_case_ )




    def 				__magic_name__				(       self		:			List[Any] ) ->				str:




     '''simple docstring'''

     A__			=							self.scheduler_classes[0]
     A__			=							self.get_scheduler_config()
     A__			=							scheduler_class(**snake_case_ )

     assert torch.sum(torch.abs(scheduler._get_variance(0      ,					0 ) - 0.0 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(420      ,					400 ) - 0.1_4771 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(980      ,					960 ) - 0.3_2460 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(0      ,					0 ) - 0.0 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487      ,					486 ) - 0.0_0979 ) ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999      ,					998 ) - 0.02 ) ) < 1e-5




    def 				__magic_name__				(       self		:			str ) ->				int:




     '''simple docstring'''

     A__			=							self.full_loop()

     A__			=							torch.sum(torch.abs(snake_case_ ) )
     A__			=							torch.mean(torch.abs(snake_case_ ) )

     assert abs(result_sum.item() - 172.0067 ) < 1e-2
     assert abs(result_mean.item() - 0.22_3967 ) < 1e-3




    def 				__magic_name__				(       self		:			Optional[Any] ) ->				int:




     '''simple docstring'''

     A__			=							self.full_loop(prediction_type="v_prediction" )

     A__			=							torch.sum(torch.abs(snake_case_ ) )
     A__			=							torch.mean(torch.abs(snake_case_ ) )

     assert abs(result_sum.item() - 52.5302 ) < 1e-2
     assert abs(result_mean.item() - 0.0684 ) < 1e-3




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.full_loop(set_alpha_to_one=snake_case_      ,					beta_start=0.01 )
     A__			=							torch.sum(torch.abs(snake_case_ ) )
     A__			=							torch.mean(torch.abs(snake_case_ ) )

     assert abs(result_sum.item() - 149.8295 ) < 1e-2
     assert abs(result_mean.item() - 0.1951 ) < 1e-3




    def 				__magic_name__				(       self		:			str ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.full_loop(set_alpha_to_one=snake_case_      ,					beta_start=0.01 )
     A__			=							torch.sum(torch.abs(snake_case_ ) )
     A__			=							torch.mean(torch.abs(snake_case_ ) )

     assert abs(result_sum.item() - 149.0784 ) < 1e-2
     assert abs(result_mean.item() - 0.1941 ) < 1e-3











"""simple docstring"""



import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


SCREAMING_SNAKE_CASE							=			2 << 20  # 2mb
SCREAMING_SNAKE_CASE							=			get_logger(__name__)




class       UpperCAmelCase_    (		A_				):




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self._options.get_all_options().get("runner" )
     return runner in [None, "DirectRunner", "PortableRunner"]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						Dict:
 A__			=							FileSystems
 if fs.exists(lowercase_		):
  if force_upload:
   logger.info(f"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.create(lowercase_		) as remote_file:
  with open(lowercase_     ,					"rb"		) as local_file:
   A__			=							local_file.read(lowercase_		)
   while chunk:
    remote_file.write(lowercase_		)
    A__			=							local_file.read(lowercase_		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_=False		)		->						int:
 A__			=							FileSystems
 if os.path.exists(lowercase_		):
  if force_download:
   logger.info(f"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""		)
  else:
   logger.info(f"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""		)
   return
 with fs.open(lowercase_		) as remote_file:
  with open(lowercase_     ,					"wb"		) as local_file:
   A__			=							remote_file.read(lowercase_		)
   while chunk:
    local_file.write(lowercase_		)
    A__			=							remote_file.read(lowercase_		)





