
'''simple docstring'''





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    List[str]				=							(KDPMaDiscreteScheduler,)
 lowercase_					:    Dict				=							10







 def 							UpperCamelCase  (					self		:  List[Any]			,				**snake_case__		:  Dict ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							{
       "num_train_timesteps": 11_00,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
   }

   config.update(**snake_case__ )
   return config







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   for timesteps in [10, 50, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=snake_case__ )







 def 							UpperCamelCase  (					self		:  str ):



   '''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=snake_case__			,				beta_end=snake_case__ )







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							self.scheduler_classes[0]
   UpperCAmelCase__      :					Tuple				       =							self.get_scheduler_config(prediction_type="v_prediction" )
   UpperCAmelCase__      :					int				       =							scheduler_class(**snake_case__ )

   scheduler.set_timesteps(self.num_inference_steps )

   UpperCAmelCase__      :					str				       =							self.dummy_model()
   UpperCAmelCase__      :					Tuple				       =							self.dummy_sample_deter * scheduler.init_noise_sigma
   UpperCAmelCase__      :					Dict				       =							sample.to(snake_case__ )

   for i, t in enumerate(scheduler.timesteps ):
     UpperCAmelCase__      :					Optional[Any]				       =							scheduler.scale_model_input(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					Tuple				       =							model(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					Dict				       =							scheduler.step(snake_case__			,				snake_case__			,				snake_case__ )
     UpperCAmelCase__      :					Optional[Any]				       =							output.prev_sample

   UpperCAmelCase__      :					List[str]				       =							torch.sum(torch.abs(snake_case__ ) )
   UpperCAmelCase__      :					List[str]				       =							torch.mean(torch.abs(snake_case__ ) )

   if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 4.6_934e-07 ) < 1e-2
     assert abs(result_mean.item() - 6.1_112e-10 ) < 1e-3
   else:
     # CUDA
     assert abs(result_sum.item() - 4.693_428_650_170_972e-07 ) < 1e-2
     assert abs(result_mean.item() - 0.0002 ) < 1e-3







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   if torch_device == "mps":
     return
   UpperCAmelCase__      :					List[str]				       =							self.scheduler_classes[0]
   UpperCAmelCase__      :					Dict				       =							self.get_scheduler_config()
   UpperCAmelCase__      :					List[Any]				       =							scheduler_class(**snake_case__ )

   scheduler.set_timesteps(self.num_inference_steps )

   UpperCAmelCase__      :					Optional[int]				       =							self.dummy_model()
   UpperCAmelCase__      :					List[Any]				       =							self.dummy_sample_deter * scheduler.init_noise_sigma
   UpperCAmelCase__      :					Dict				       =							sample.to(snake_case__ )

   for i, t in enumerate(scheduler.timesteps ):
     UpperCAmelCase__      :					List[str]				       =							scheduler.scale_model_input(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					List[str]				       =							model(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					List[Any]				       =							scheduler.step(snake_case__			,				snake_case__			,				snake_case__ )
     UpperCAmelCase__      :					Union[str, Any]				       =							output.prev_sample

   UpperCAmelCase__      :					Any				       =							torch.sum(torch.abs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							torch.mean(torch.abs(snake_case__ ) )

   if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 20.4125 ) < 1e-2
     assert abs(result_mean.item() - 0.0266 ) < 1e-3
   else:
     # CUDA
     assert abs(result_sum.item() - 20.4125 ) < 1e-2
     assert abs(result_mean.item() - 0.0266 ) < 1e-3







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   if torch_device == "mps":
     return
   UpperCAmelCase__      :					Union[str, Any]				       =							self.scheduler_classes[0]
   UpperCAmelCase__      :					int				       =							self.get_scheduler_config()
   UpperCAmelCase__      :					Union[str, Any]				       =							scheduler_class(**snake_case__ )

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

   UpperCAmelCase__      :					Union[str, Any]				       =							self.dummy_model()
   UpperCAmelCase__      :					Dict				       =							self.dummy_sample_deter.to(snake_case__ ) * scheduler.init_noise_sigma

   for t in scheduler.timesteps:
     UpperCAmelCase__      :					List[str]				       =							scheduler.scale_model_input(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					List[Any]				       =							model(snake_case__			,				snake_case__ )

     UpperCAmelCase__      :					int				       =							scheduler.step(snake_case__			,				snake_case__			,				snake_case__ )
     UpperCAmelCase__      :					List[Any]				       =							output.prev_sample

   UpperCAmelCase__      :					Any				       =							torch.sum(torch.abs(snake_case__ ) )
   UpperCAmelCase__      :					Dict				       =							torch.mean(torch.abs(snake_case__ ) )

   if str(snake_case__ ).startswith("cpu" ):
     # The following sum varies between 148 and 156 on mps. Why?
     assert abs(result_sum.item() - 20.4125 ) < 1e-2
     assert abs(result_mean.item() - 0.0266 ) < 1e-3
   else:
     # CUDA
     assert abs(result_sum.item() - 20.4125 ) < 1e-2
     assert abs(result_mean.item() - 0.0266 ) < 1e-3





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



