


"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       (DDIMScheduler,)
		A         =       (("eta", 0.0), ("num_inference_steps", 50))
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  {
					    '''num_train_timesteps''': 10_00,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''clip_sample''': True,
					}

					config.update(**SCREAMING_SNAKE_CASE__      )
					return config
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  self.scheduler_classes[0]
					_UpperCamelCase				:Tuple				    =  self.get_scheduler_config(**SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Any				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase    ,			_UpperCamelCase				:Dict				    =  10, 0.0

					_UpperCamelCase				:List[Any]				    =  self.dummy_model()
					_UpperCamelCase				:Optional[Any]				    =  self.dummy_sample_deter

					scheduler.set_timesteps(SCREAMING_SNAKE_CASE__      )

					for t in scheduler.timesteps:
								_UpperCamelCase				:Union[str, Any]				    =  model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
								_UpperCamelCase				:Tuple				    =  scheduler.step(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ).prev_sample

					return sample
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					for timesteps in [1_00, 5_00, 10_00]:
								self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



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

					_UpperCamelCase				:Tuple				    =  self.scheduler_classes[0]
					_UpperCamelCase				:List[Any]				    =  self.get_scheduler_config(steps_offset=1      )
					_UpperCamelCase				:Optional[int]				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )
					scheduler.set_timesteps(5      )
					assert torch.equal(scheduler.timesteps				,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]      )      )
		def      _UpperCamelCase(   self      )			->  str:

					"""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=SCREAMING_SNAKE_CASE__				,							beta_end=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					for timestep_spacing in ["trailing", "leading"]:
								self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					for rescale_betas_zero_snr in [True, False]:
								self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__      )
					for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(
											    thresholding=SCREAMING_SNAKE_CASE__				,							prediction_type=SCREAMING_SNAKE_CASE__				,							sample_max_value=SCREAMING_SNAKE_CASE__				,							)
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					for t in [1, 10, 49]:
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					for t, num_inference_steps in zip([1, 10, 50]				,							[10, 50, 5_00]      ):
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__				,							num_inference_steps=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					for t, eta in zip([1, 10, 49]				,							[0.0, 0.5, 1.0]      ):
								self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__				,							eta=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.scheduler_classes[0]
					_UpperCamelCase				:Tuple				    =  self.get_scheduler_config()
					_UpperCamelCase				:Union[str, Any]				    =  scheduler_class(**SCREAMING_SNAKE_CASE__      )

					assert torch.sum(torch.abs(scheduler._get_variance(0				,							0      ) - 0.0      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_20				,							4_00      ) - 0.1_4_7_7_1      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_80				,							9_60      ) - 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(4_87				,							4_86      ) - 0.0_0_9_7_9      )      ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99				,							9_98      ) - 0.0_2      )      ) < 1E-5
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.full_loop()

					_UpperCamelCase				:Optional[int]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:Dict				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					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      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  self.full_loop(prediction_type='''v_prediction'''      )

					_UpperCamelCase				:Optional[int]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:int				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					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      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							beta_start=0.0_1      )
					_UpperCamelCase				:Optional[Any]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:int				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					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      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:Any				    =  self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							beta_start=0.0_1      )
					_UpperCamelCase				:List[str]				    =  torch.sum(torch.abs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:Any				    =  torch.mean(torch.abs(SCREAMING_SNAKE_CASE__      )      )

					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





"""simple docstring"""






def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  word.upper()
			_UpperCamelCase				:Dict				    =  0
			_UpperCamelCase				:Union[str, Any]				    =  {}
			_UpperCamelCase				:Optional[int]				    =  []

			for letter in word:
						if letter not in letter_nums:
									_UpperCamelCase				:Dict				    =  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
					import pprint
					import time

					UpperCamelCase__							:Dict       =  time.time()
					with open("""dictionary.txt""") as in_file:
										UpperCamelCase__							:str       =  in_file.read().splitlines()

					UpperCamelCase__							:dict       =  {}
					for word in word_list:
										UpperCamelCase__							:List[Any]       =  get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															UpperCamelCase__							:Tuple       =  [word]

					with open("""word_patterns.txt""", """w""") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					UpperCamelCase__							:Dict       =  round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.

