

'''simple docstring'''
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 					_snake_case		(A__):
			__A  :	List[str]        =(PNDMScheduler,)
			__A  :	Dict        =(('num_inference_steps', 50),)






			def 		UpperCamelCase__							(  self			,**_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  {
				    "num_train_timesteps": 10_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**__UpperCAmelCase     )
				return config






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      str						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      List[str]						=  kwargs.pop("num_inference_steps"			,__UpperCAmelCase     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.dummy_sample
				UpperCAmelCase_     :      Any						=  0.1 * sample
				UpperCAmelCase_     :      Tuple						=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Any						=  self.get_scheduler_config(**__UpperCAmelCase     )
					UpperCAmelCase_     :      Union[str, Any]						=  scheduler_class(**__UpperCAmelCase     )
					scheduler.set_timesteps(__UpperCAmelCase     )
					# copy over dummy past residuals
					UpperCAmelCase_     :      Dict						=  dummy_past_residuals[:]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(__UpperCAmelCase     )
						UpperCAmelCase_     :      Dict						=  scheduler_class.from_pretrained(__UpperCAmelCase     )
						new_scheduler.set_timesteps(__UpperCAmelCase     )
						# copy over dummy past residuals
						UpperCAmelCase_     :      int						=  dummy_past_residuals[:]

					UpperCAmelCase_     :      List[Any]						=  scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      Dict						=  new_scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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

					UpperCAmelCase_     :      str						=  scheduler.step_plms(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      List[str]						=  new_scheduler.step_plms(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      int						=  kwargs.pop("num_inference_steps"			,__UpperCAmelCase     )
				UpperCAmelCase_     :      Dict						=  self.dummy_sample
				UpperCAmelCase_     :      Union[str, Any]						=  0.1 * sample
				UpperCAmelCase_     :      str						=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      int						=  self.get_scheduler_config()
					UpperCAmelCase_     :      int						=  scheduler_class(**__UpperCAmelCase     )
					scheduler.set_timesteps(__UpperCAmelCase     )

					# copy over dummy past residuals (must be after setting timesteps)
					UpperCAmelCase_     :      Optional[Any]						=  dummy_past_residuals[:]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(__UpperCAmelCase     )
						UpperCAmelCase_     :      List[str]						=  scheduler_class.from_pretrained(__UpperCAmelCase     )
						# copy over dummy past residuals
						new_scheduler.set_timesteps(__UpperCAmelCase     )

						# copy over dummy past residual (must be after setting timesteps)
						UpperCAmelCase_     :      Union[str, Any]						=  dummy_past_residuals[:]

					UpperCAmelCase_     :      List[str]						=  scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      Optional[Any]						=  new_scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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

					UpperCAmelCase_     :      Optional[Any]						=  scheduler.step_plms(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      Tuple						=  new_scheduler.step_plms(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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






			def 		UpperCamelCase__							(  self			,**_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      List[str]						=  self.get_scheduler_config(**__UpperCAmelCase     )
				UpperCAmelCase_     :      Any						=  scheduler_class(**__UpperCAmelCase     )

				UpperCAmelCase_     :      int						=  10
				UpperCAmelCase_     :      Union[str, Any]						=  self.dummy_model()
				UpperCAmelCase_     :      Optional[int]						=  self.dummy_sample_deter
				scheduler.set_timesteps(__UpperCAmelCase     )

				for i, t in enumerate(scheduler.prk_timesteps     ):
					UpperCAmelCase_     :      Optional[int]						=  model(__UpperCAmelCase			,__UpperCAmelCase     )
					UpperCAmelCase_     :      Optional[Any]						=  scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     ).prev_sample

				for i, t in enumerate(scheduler.plms_timesteps     ):
					UpperCAmelCase_     :      Any						=  model(__UpperCAmelCase			,__UpperCAmelCase     )
					UpperCAmelCase_     :      Any						=  scheduler.step_plms(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     ).prev_sample

				return sample






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  dict(self.forward_default_kwargs     )

				UpperCAmelCase_     :      Any						=  kwargs.pop("num_inference_steps"			,__UpperCAmelCase     )

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Union[str, Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Optional[Any]						=  scheduler_class(**__UpperCAmelCase     )

					UpperCAmelCase_     :      List[str]						=  self.dummy_sample
					UpperCAmelCase_     :      Dict						=  0.1 * sample

					if num_inference_steps is not None and hasattr(__UpperCAmelCase			,"set_timesteps"     ):
						scheduler.set_timesteps(__UpperCAmelCase     )
					elif num_inference_steps is not None and not hasattr(__UpperCAmelCase			,"set_timesteps"     ):
						UpperCAmelCase_     :      Optional[int]						=  num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					UpperCAmelCase_     :      Tuple						=  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
					UpperCAmelCase_     :      Optional[Any]						=  dummy_past_residuals[:]

					UpperCAmelCase_     :      str						=  scheduler.step_prk(__UpperCAmelCase			,0			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      List[Any]						=  scheduler.step_prk(__UpperCAmelCase			,1			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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

					UpperCAmelCase_     :      Union[str, Any]						=  scheduler.step_plms(__UpperCAmelCase			,0			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample
					UpperCAmelCase_     :      Any						=  scheduler.step_plms(__UpperCAmelCase			,1			,__UpperCAmelCase			,**__UpperCAmelCase     ).prev_sample

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






			def 		UpperCamelCase__							(  self     ):
				for timesteps in [1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				for steps_offset in [0, 1]:
					self.check_over_configs(steps_offset=__UpperCAmelCase     )

				UpperCAmelCase_     :      int						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      Any						=  self.get_scheduler_config(steps_offset=1     )
				UpperCAmelCase_     :      List[str]						=  scheduler_class(**__UpperCAmelCase     )
				scheduler.set_timesteps(10     )
				assert torch.equal(
				    scheduler.timesteps			,torch.LongTensor(
				        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]     )			,)






			def 		UpperCamelCase__							(  self     ):
				for beta_start, beta_end in zip([0.0001, 0.001]			,[0.002, 0.02]     ):
					self.check_over_configs(beta_start=__UpperCAmelCase			,beta_end=__UpperCAmelCase     )






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






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






			def 		UpperCamelCase__							(  self     ):
				for t in [1, 5, 10]:
					self.check_over_forward(time_step=__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				for t, num_inference_steps in zip([1, 5, 10]			,[10, 50, 1_00]     ):
					self.check_over_forward(num_inference_steps=__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
				UpperCAmelCase_     :      Optional[Any]						=  27

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Optional[Any]						=  self.dummy_sample
					UpperCAmelCase_     :      List[Any]						=  0.1 * sample

					UpperCAmelCase_     :      List[Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      str						=  scheduler_class(**__UpperCAmelCase     )

					scheduler.set_timesteps(__UpperCAmelCase     )

					# before power of 3 fix, would error on first step, so we only need to do two
					for i, t in enumerate(scheduler.prk_timesteps[:2]     ):
						UpperCAmelCase_     :      str						=  scheduler.step_prk(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     ).prev_sample






			def 		UpperCamelCase__							(  self     ):
				with self.assertRaises(__UpperCAmelCase     ):
					UpperCAmelCase_     :      Optional[Any]						=  self.scheduler_classes[0]
					UpperCAmelCase_     :      List[str]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Any						=  scheduler_class(**__UpperCAmelCase     )

					scheduler.step_plms(self.dummy_sample			,1			,self.dummy_sample     ).prev_sample






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.full_loop()
				UpperCAmelCase_     :      Optional[int]						=  torch.sum(torch.abs(__UpperCAmelCase     )     )
				UpperCAmelCase_     :      List[str]						=  torch.mean(torch.abs(__UpperCAmelCase     )     )

				assert abs(result_sum.item() - 198.1318     ) < 1E-2
				assert abs(result_mean.item() - 0.2580     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  self.full_loop(prediction_type="v_prediction"     )
				UpperCAmelCase_     :      List[Any]						=  torch.sum(torch.abs(__UpperCAmelCase     )     )
				UpperCAmelCase_     :      Optional[Any]						=  torch.mean(torch.abs(__UpperCAmelCase     )     )

				assert abs(result_sum.item() - 67.3986     ) < 1E-2
				assert abs(result_mean.item() - 0.0878     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				# We specify different beta, so that the first alpha is 0.99
				UpperCAmelCase_     :      Dict						=  self.full_loop(set_alpha_to_one=__UpperCAmelCase			,beta_start=0.01     )
				UpperCAmelCase_     :      Tuple						=  torch.sum(torch.abs(__UpperCAmelCase     )     )
				UpperCAmelCase_     :      Union[str, Any]						=  torch.mean(torch.abs(__UpperCAmelCase     )     )

				assert abs(result_sum.item() - 230.0399     ) < 1E-2
				assert abs(result_mean.item() - 0.2995     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				# We specify different beta, so that the first alpha is 0.99
				UpperCAmelCase_     :      Dict						=  self.full_loop(set_alpha_to_one=__UpperCAmelCase			,beta_start=0.01     )
				UpperCAmelCase_     :      Tuple						=  torch.sum(torch.abs(__UpperCAmelCase     )     )
				UpperCAmelCase_     :      Dict						=  torch.mean(torch.abs(__UpperCAmelCase     )     )

				assert abs(result_sum.item() - 186.9482     ) < 1E-2
				assert abs(result_mean.item() - 0.2434     ) < 1E-3







"""simple docstring"""




def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   len(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   (
			    first_str_length if first_str_length > second_str_length else second_str_length
			)
			SCREAMING_SNAKE_CASE__										=   []
			for char_count in range(snake_case__     ):
						if char_count < first_str_length:
									output_list.append(first_str[char_count]     )
						if char_count < second_str_length:
									output_list.append(second_str[char_count]     )
			return "".join(snake_case__     )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")


