



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class 	a__       (       _snake_case       ):


					"""simple docstring"""



					A__  :				Union[str, Any]					 =				(DPMSolverMultistepScheduler,)
					A__  :				List[str]					 =				(('''num_inference_steps''', 25),)

					def 				__UpperCAmelCase		(		self     :List[str]      ,  **lowercase__     :Any    ):
										lowercase						   =       {
										    'num_train_timesteps': 1000,
										    'beta_start': 0.0001,
										    'beta_end': 0.02,
										    'beta_schedule': 'linear',
										    'solver_order': 2,
										    'prediction_type': 'epsilon',
										    'thresholding': False,
										    'sample_max_value': 1.0,
										    'algorithm_type': 'dpmsolver++',
										    'solver_type': 'midpoint',
										    'lower_order_final': False,
										    'lambda_min_clipped': -float('inf'    ),
										    'variance_type': None,
										}

										config.update(**lowercase__    )
										return config

					def 				__UpperCAmelCase		(		self     :str      ,  lowercase__     :Union[str, Any]=0      ,  **lowercase__     :Any    ):
										lowercase						   =       dict(self.forward_default_kwargs    )
										lowercase						   =       kwargs.pop('num_inference_steps'      ,  lowercase__    )
										lowercase						   =       self.dummy_sample
										lowercase						   =       0.1 * sample
										lowercase						   =       [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
															lowercase						   =       self.get_scheduler_config(**lowercase__    )
															lowercase						   =       scheduler_class(**lowercase__    )
															scheduler.set_timesteps(lowercase__    )
															# copy over dummy past residuals
															lowercase						   =       dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(lowercase__    )
																				lowercase						   =       scheduler_class.from_pretrained(lowercase__    )
																				new_scheduler.set_timesteps(lowercase__    )
																				# copy over dummy past residuals
																				lowercase						   =       dummy_past_residuals[: new_scheduler.config.solver_order]

															lowercase   ,      lowercase						   =       sample, sample
															for t in range(lowercase__      ,  time_step + scheduler.config.solver_order + 1    ):
																				lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample
																				lowercase						   =       new_scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample

																				assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

					def 				__UpperCAmelCase		(		self     :Dict    ):
										pass

					def 				__UpperCAmelCase		(		self     :List[str]      ,  lowercase__     :Dict=0      ,  **lowercase__     :int    ):
										lowercase						   =       dict(self.forward_default_kwargs    )
										lowercase						   =       kwargs.pop('num_inference_steps'      ,  lowercase__    )
										lowercase						   =       self.dummy_sample
										lowercase						   =       0.1 * sample
										lowercase						   =       [residual + 0.2, residual + 0.15, residual + 0.10]

										for scheduler_class in self.scheduler_classes:
															lowercase						   =       self.get_scheduler_config()
															lowercase						   =       scheduler_class(**lowercase__    )
															scheduler.set_timesteps(lowercase__    )

															# copy over dummy past residuals (must be after setting timesteps)
															lowercase						   =       dummy_past_residuals[: scheduler.config.solver_order]

															with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(lowercase__    )
																				lowercase						   =       scheduler_class.from_pretrained(lowercase__    )
																				# copy over dummy past residuals
																				new_scheduler.set_timesteps(lowercase__    )

																				# copy over dummy past residual (must be after setting timesteps)
																				lowercase						   =       dummy_past_residuals[: new_scheduler.config.solver_order]

															lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample
															lowercase						   =       new_scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample

															assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

					def 				__UpperCAmelCase		(		self     :Optional[Any]      ,  lowercase__     :Any=None      ,  **lowercase__     :str    ):
										if scheduler is None:
															lowercase						   =       self.scheduler_classes[0]
															lowercase						   =       self.get_scheduler_config(**lowercase__    )
															lowercase						   =       scheduler_class(**lowercase__    )

										lowercase						   =       10
										lowercase						   =       self.dummy_model()
										lowercase						   =       self.dummy_sample_deter
										scheduler.set_timesteps(lowercase__    )

										for i, t in enumerate(scheduler.timesteps    ):
															lowercase						   =       model(lowercase__      ,  lowercase__    )
															lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__    ).prev_sample

										return sample

					def 				__UpperCAmelCase		(		self     :int    ):
										lowercase						   =       dict(self.forward_default_kwargs    )

										lowercase						   =       kwargs.pop('num_inference_steps'      ,  lowercase__    )

										for scheduler_class in self.scheduler_classes:
															lowercase						   =       self.get_scheduler_config()
															lowercase						   =       scheduler_class(**lowercase__    )

															lowercase						   =       self.dummy_sample
															lowercase						   =       0.1 * sample

															if num_inference_steps is not None and hasattr(lowercase__      ,  'set_timesteps'    ):
																				scheduler.set_timesteps(lowercase__    )
															elif num_inference_steps is not None and not hasattr(lowercase__      ,  'set_timesteps'    ):
																				lowercase						   =       num_inference_steps

															# copy over dummy past residuals (must be done after set_timesteps)
															lowercase						   =       [residual + 0.2, residual + 0.15, residual + 0.10]
															lowercase						   =       dummy_past_residuals[: scheduler.config.solver_order]

															lowercase						   =       scheduler.timesteps[5]
															lowercase						   =       scheduler.timesteps[6]

															lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample
															lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__      ,  **lowercase__    ).prev_sample

															self.assertEqual(output_a.shape      ,  sample.shape    )
															self.assertEqual(output_a.shape      ,  output_a.shape    )

					def 				__UpperCAmelCase		(		self     :str    ):
										for timesteps in [25, 50, 100, 999, 1000]:
															self.check_over_configs(num_train_timesteps=lowercase__    )

					def 				__UpperCAmelCase		(		self     :List[Any]    ):
										self.check_over_configs(thresholding=lowercase__    )
										for order in [1, 2, 3]:
															for solver_type in ["midpoint", "heun"]:
																				for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																														self.check_over_configs(
																														    thresholding=lowercase__      ,  prediction_type=lowercase__      ,  sample_max_value=lowercase__      ,  algorithm_type='dpmsolver++'      ,  solver_order=lowercase__      ,  solver_type=lowercase__      ,  )

					def 				__UpperCAmelCase		(		self     :Dict    ):
										for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=lowercase__    )

					def 				__UpperCAmelCase		(		self     :Tuple    ):
										for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
															for solver_type in ["midpoint", "heun"]:
																				for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																														if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																																			if order == 3:
																																								continue
																														else:
																																			self.check_over_configs(
																																			    solver_order=lowercase__      ,  solver_type=lowercase__      ,  prediction_type=lowercase__      ,  algorithm_type=lowercase__      ,  )
																														lowercase						   =       self.full_loop(
																														    solver_order=lowercase__      ,  solver_type=lowercase__      ,  prediction_type=lowercase__      ,  algorithm_type=lowercase__      ,  )
																														assert not torch.isnan(lowercase__    ).any(), "Samples have nan numbers"

					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										self.check_over_configs(lower_order_final=lowercase__    )
										self.check_over_configs(lower_order_final=lowercase__    )

					def 				__UpperCAmelCase		(		self     :Dict    ):
										self.check_over_configs(lambda_min_clipped=-float('inf'    )    )
										self.check_over_configs(lambda_min_clipped=-5.1    )

					def 				__UpperCAmelCase		(		self     :int    ):
										self.check_over_configs(variance_type=lowercase__    )
										self.check_over_configs(variance_type='learned_range'    )

					def 				__UpperCAmelCase		(		self     :str    ):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
															self.check_over_forward(num_inference_steps=lowercase__      ,  time_step=0    )

					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       self.full_loop()
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 0.3301    ) < 1E-3

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										lowercase						   =       self.full_loop(thresholding=lowercase__      ,  dynamic_thresholding_ratio=0.87      ,  sample_max_value=0.5    )
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 1.1364    ) < 1E-3

					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.full_loop(prediction_type='v_prediction'    )
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 0.2251    ) < 1E-3

					def 				__UpperCAmelCase		(		self     :Optional[Any]    ):
										lowercase						   =       self.full_loop(prediction_type='v_prediction'      ,  use_karras_sigmas=lowercase__    )
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 0.2096    ) < 1E-3

					def 				__UpperCAmelCase		(		self     :Dict    ):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										lowercase						   =       DPMSolverMultistepScheduler(**self.get_scheduler_config()    )
										lowercase						   =       self.full_loop(scheduler=lowercase__    )
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 0.3301    ) < 1E-3

										lowercase						   =       DPMSolverSinglestepScheduler.from_config(scheduler.config    )
										lowercase						   =       UniPCMultistepScheduler.from_config(scheduler.config    )
										lowercase						   =       DEISMultistepScheduler.from_config(scheduler.config    )
										lowercase						   =       DPMSolverMultistepScheduler.from_config(scheduler.config    )

										lowercase						   =       self.full_loop(scheduler=lowercase__    )
										lowercase						   =       torch.mean(torch.abs(lowercase__    )    )

										assert abs(result_mean.item() - 0.3301    ) < 1E-3

					def 				__UpperCAmelCase		(		self     :Union[str, Any]    ):
										lowercase						   =       self.scheduler_classes[0]
										lowercase						   =       self.get_scheduler_config(thresholding=lowercase__      ,  dynamic_thresholding_ratio=0    )
										lowercase						   =       scheduler_class(**lowercase__    )

										lowercase						   =       10
										lowercase						   =       self.dummy_model()
										lowercase						   =       self.dummy_sample_deter.half()
										scheduler.set_timesteps(lowercase__    )

										for i, t in enumerate(scheduler.timesteps    ):
															lowercase						   =       model(lowercase__      ,  lowercase__    )
															lowercase						   =       scheduler.step(lowercase__      ,  lowercase__      ,  lowercase__    ).prev_sample

										assert sample.dtype == torch.floataa



					def 				__UpperCAmelCase		(		self     :List[Any]      ,  **lowercase__     :List[Any]    ):
										for scheduler_class in self.scheduler_classes:
															lowercase						   =       self.get_scheduler_config(**lowercase__    )
															lowercase						   =       scheduler_class(**lowercase__    )

															scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
															assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps











def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					for index, item in enumerate(_UpperCAmelCase				):
										if item == target:
															return index
					return -1





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if not (0 <= high < len(_UpperCAmelCase				) and 0 <= low < len(_UpperCAmelCase				)):
										raise Exception('Invalid upper or lower bound!'				)
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(_UpperCAmelCase       ,			low + 1       ,			high - 1       ,			_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     input('''Enter numbers separated by comma:\n''').strip()
		__magic_name__       =     [int(item.strip()) for item in user_input.split(''',''')]

		__magic_name__       =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__magic_name__       =     linear_search(sequence, target)
		if result != -1:
				print(F"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(F"""{target} was not found in {sequence}""")






