code: 
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  class 					UpperCamelCase	(     A_      ):
  					__UpperCamelCase						=   (DDIMScheduler,)
  					__UpperCamelCase						=   (("""eta""", 0.0), ("""num_inference_steps""", 50))
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,**_lowerCAmelCase :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     {
  							    '''num_train_timesteps''': 1_000,
  							    '''beta_start''': 0.0_0_0_1,
  							    '''beta_end''': 0.0_2,
  							    '''beta_schedule''': '''linear''',
  							    '''clip_sample''': True,
  							}
  
  							config.update(**_lowerCAmelCase						)
  							return config
  
  
  
  
  					def 							UpperCamelCase_						( self :					str      ,**_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.scheduler_classes[0]
  							__snake_case		       =     self.get_scheduler_config(**_lowerCAmelCase						)
  							__snake_case		       =     scheduler_class(**_lowerCAmelCase						)
  
  							__snake_case		       =     10, 0.0
  
  							__snake_case		       =     self.dummy_model()
  							__snake_case		       =     self.dummy_sample_deter
  
  							scheduler.set_timesteps(_lowerCAmelCase						)
  
  							for t in scheduler.timesteps:
  									__snake_case		       =     model(_lowerCAmelCase      ,_lowerCAmelCase						)
  									__snake_case		       =     scheduler.step(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						).prev_sample
  
  							return sample
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for timesteps in [100, 500, 1_000]:
  									self.check_over_configs(num_train_timesteps=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for steps_offset in [0, 1]:
  									self.check_over_configs(steps_offset=_lowerCAmelCase						)
  
  							__snake_case		       =     self.scheduler_classes[0]
  							__snake_case		       =     self.get_scheduler_config(steps_offset=1						)
  							__snake_case		       =     scheduler_class(**_lowerCAmelCase						)
  							scheduler.set_timesteps(5						)
  							assert torch.equal(scheduler.timesteps      ,torch.LongTensor([801, 601, 401, 201, 1]						)						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,[0.0_0_2, 0.0_2, 0.2, 2]						):
  									self.check_over_configs(beta_start=_lowerCAmelCase      ,beta_end=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for schedule in ["linear", "squaredcos_cap_v2"]:
  									self.check_over_configs(beta_schedule=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for prediction_type in ["epsilon", "v_prediction"]:
  									self.check_over_configs(prediction_type=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for clip_sample in [True, False]:
  									self.check_over_configs(clip_sample=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for timestep_spacing in ["trailing", "leading"]:
  									self.check_over_configs(timestep_spacing=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for rescale_betas_zero_snr in [True, False]:
  									self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							self.check_over_configs(thresholding=_lowerCAmelCase						)
  							for threshold in [0.5, 1.0, 2.0]:
  									for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(
  											    thresholding=_lowerCAmelCase      ,prediction_type=_lowerCAmelCase      ,sample_max_value=_lowerCAmelCase      ,)
  
  
  
  
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for t in [1, 10, 49]:
  									self.check_over_forward(time_step=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for t, num_inference_steps in zip([1, 10, 50]      ,[10, 50, 500]						):
  									self.check_over_forward(time_step=_lowerCAmelCase      ,num_inference_steps=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							for t, eta in zip([1, 10, 49]      ,[0.0, 0.5, 1.0]						):
  									self.check_over_forward(time_step=_lowerCAmelCase      ,eta=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.scheduler_classes[0]
  							__snake_case		       =     self.get_scheduler_config()
  							__snake_case		       =     scheduler_class(**_lowerCAmelCase						)
  
  							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_4_7_7_1						)						) < 1E-5
  							assert torch.sum(torch.abs(scheduler._get_variance(980      ,960						) - 0.3_2_4_6_0						)						) < 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_0_9_7_9						)						) < 1E-5
  							assert torch.sum(torch.abs(scheduler._get_variance(999      ,998						) - 0.0_2						)						) < 1E-5
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.full_loop()
  
  							__snake_case		       =     torch.sum(torch.abs(_lowerCAmelCase						)						)
  							__snake_case		       =     torch.mean(torch.abs(_lowerCAmelCase						)						)
  
  							assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1E-2
  							assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1E-3
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.full_loop(prediction_type="v_prediction"						)
  
  							__snake_case		       =     torch.sum(torch.abs(_lowerCAmelCase						)						)
  							__snake_case		       =     torch.mean(torch.abs(_lowerCAmelCase						)						)
  
  							assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1E-2
  							assert abs(result_mean.item() - 0.0_6_8_4						) < 1E-3
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.full_loop(set_alpha_to_one=_lowerCAmelCase      ,beta_start=0.0_1						)
  							__snake_case		       =     torch.sum(torch.abs(_lowerCAmelCase						)						)
  							__snake_case		       =     torch.mean(torch.abs(_lowerCAmelCase						)						)
  
  							assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1E-2
  							assert abs(result_mean.item() - 0.1_9_5_1						) < 1E-3
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.full_loop(set_alpha_to_one=_lowerCAmelCase      ,beta_start=0.0_1						)
  							__snake_case		       =     torch.sum(torch.abs(_lowerCAmelCase						)						)
  							__snake_case		       =     torch.mean(torch.abs(_lowerCAmelCase						)						)
  
  							assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1E-2
  							assert abs(result_mean.item() - 0.1_9_4_1						) < 1E-3
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  