



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					List[Any]     		=(PNDMScheduler,)
				UpperCamelCase__				:					List[str]     		=(("""num_inference_steps""", 5_0),)




				def 							A__      (						self					:						Dict,					**__lowercase					:						Dict						):
							lowercase__					  =				{
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Optional[Any]=0,					**__lowercase					:						Optional[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 + 0.1, residual + 0.05]

							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[:]

										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[:]

										lowercase__					  =				scheduler.step_prk(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				new_scheduler.step_prk(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample

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

										lowercase__					  =				scheduler.step_plms(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				new_scheduler.step_plms(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample

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




				def 							A__      (						self					:						Optional[Any]						):
							pass




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						List[str]=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 + 0.1, residual + 0.05]

							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[:]

										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[:]

										lowercase__					  =				scheduler.step_prk(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				new_scheduler.step_prk(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample

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

										lowercase__					  =				scheduler.step_plms(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				new_scheduler.step_plms(__lowercase,					__lowercase,					__lowercase,					**__lowercase						).prev_sample

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




				def 							A__      (						self					:						Dict,					**__lowercase					:						Any						):
							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.prk_timesteps						):
										lowercase__					  =				model(__lowercase,					__lowercase						)
										lowercase__					  =				scheduler.step_prk(__lowercase,					__lowercase,					__lowercase						).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps						):
										lowercase__					  =				model(__lowercase,					__lowercase						)
										lowercase__					  =				scheduler.step_plms(__lowercase,					__lowercase,					__lowercase						).prev_sample

							return sample




				def 							A__      (						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 + 0.1, residual + 0.05]
										lowercase__					  =				dummy_past_residuals[:]

										lowercase__					  =				scheduler.step_prk(__lowercase,					0,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				scheduler.step_prk(__lowercase,					1,					__lowercase,					**__lowercase						).prev_sample

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

										lowercase__					  =				scheduler.step_plms(__lowercase,					0,					__lowercase,					**__lowercase						).prev_sample
										lowercase__					  =				scheduler.step_plms(__lowercase,					1,					__lowercase,					**__lowercase						).prev_sample

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




				def 							A__      (						self					:						str						):
							for timesteps in [100, 1000]:
										self.check_over_configs(num_train_timesteps=__lowercase						)




				def 							A__      (						self					:						List[str]						):
							for steps_offset in [0, 1]:
										self.check_over_configs(steps_offset=__lowercase						)

							lowercase__					  =				self.scheduler_classes[0]
							lowercase__					  =				self.get_scheduler_config(steps_offset=1						)
							lowercase__					  =				scheduler_class(**__lowercase						)
							scheduler.set_timesteps(10						)
							assert torch.equal(
							    scheduler.timesteps,					torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]						),					)




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




				def 							A__      (						self					:						Tuple						):
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=__lowercase						)




				def 							A__      (						self					:						Union[str, Any]						):
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=__lowercase						)




				def 							A__      (						self					:						List[Any]						):
							for t in [1, 5, 10]:
										self.check_over_forward(time_step=__lowercase						)




				def 							A__      (						self					:						int						):
							for t, num_inference_steps in zip([1, 5, 10],					[10, 50, 100]						):
										self.check_over_forward(num_inference_steps=__lowercase						)




				def 							A__      (						self					:						int						):
							# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
							lowercase__					  =				27

							for scheduler_class in self.scheduler_classes:
										lowercase__					  =				self.dummy_sample
										lowercase__					  =				0.1 * sample

										lowercase__					  =				self.get_scheduler_config()
										lowercase__					  =				scheduler_class(**__lowercase						)

										scheduler.set_timesteps(__lowercase						)

										# 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]						):
													lowercase__					  =				scheduler.step_prk(__lowercase,					__lowercase,					__lowercase						).prev_sample




				def 							A__      (						self					:						List[str]						):
							with self.assertRaises(__lowercase						):
										lowercase__					  =				self.scheduler_classes[0]
										lowercase__					  =				self.get_scheduler_config()
										lowercase__					  =				scheduler_class(**__lowercase						)

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




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				self.full_loop()
							lowercase__					  =				torch.sum(torch.abs(__lowercase						)						)
							lowercase__					  =				torch.mean(torch.abs(__lowercase						)						)

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




				def 							A__      (						self					:						Any						):
							lowercase__					  =				self.full_loop(prediction_type="v_prediction"						)
							lowercase__					  =				torch.sum(torch.abs(__lowercase						)						)
							lowercase__					  =				torch.mean(torch.abs(__lowercase						)						)

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




				def 							A__      (						self					:						Optional[int]						):
							# We specify different beta, so that the first alpha is 0.99
							lowercase__					  =				self.full_loop(set_alpha_to_one=__lowercase,					beta_start=0.01						)
							lowercase__					  =				torch.sum(torch.abs(__lowercase						)						)
							lowercase__					  =				torch.mean(torch.abs(__lowercase						)						)

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




				def 							A__      (						self					:						List[str]						):
							# We specify different beta, so that the first alpha is 0.99
							lowercase__					  =				self.full_loop(set_alpha_to_one=__lowercase,					beta_start=0.01						)
							lowercase__					  =				torch.sum(torch.abs(__lowercase						)						)
							lowercase__					  =				torch.mean(torch.abs(__lowercase						)						)

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




