



"""simple docstring"""







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Optional[int]							  =     (IPNDMScheduler,)
			A__	:					str							  =     (("num_inference_steps", 5_0),)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				A__											=					{"num_train_timesteps": 1000}
				config.update(**SCREAMING_SNAKE_CASE__		)
				return config


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__=0						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					dict(self.forward_default_kwargs		)
				A__											=					kwargs.pop("num_inference_steps"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					self.dummy_sample
				A__											=					0.1 * sample
				A__											=					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

				for scheduler_class in self.scheduler_classes:
					A__											=					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__		)
					A__											=					scheduler_class(**SCREAMING_SNAKE_CASE__		)
					scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)
					# copy over dummy past residuals
					A__											=					dummy_past_residuals[:]

					if time_step is None:
						A__											=					scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					new_scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					new_scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				pass


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__=0						,	**SCREAMING_SNAKE_CASE__		)      ->	Union[str, Any]:
				A__											=					dict(self.forward_default_kwargs		)
				A__											=					kwargs.pop("num_inference_steps"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					self.dummy_sample
				A__											=					0.1 * sample
				A__											=					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

				for scheduler_class in self.scheduler_classes:
					A__											=					self.get_scheduler_config()
					A__											=					scheduler_class(**SCREAMING_SNAKE_CASE__		)
					scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

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

					if time_step is None:
						A__											=					scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

						# copy over dummy past residual (must be after setting timesteps)
						A__											=					dummy_past_residuals[:]

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					new_scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					new_scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				A__											=					self.scheduler_classes[0]
				A__											=					self.get_scheduler_config(**SCREAMING_SNAKE_CASE__		)
				A__											=					scheduler_class(**SCREAMING_SNAKE_CASE__		)

				A__											=					10
				A__											=					self.dummy_model()
				A__											=					self.dummy_sample_deter
				scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)

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

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

				return sample


			def   snake_case__	(      self		)      ->	str:
				A__											=					dict(self.forward_default_kwargs		)

				A__											=					kwargs.pop("num_inference_steps"						,	SCREAMING_SNAKE_CASE__		)

				for scheduler_class in self.scheduler_classes:
					A__											=					self.get_scheduler_config()
					A__											=					scheduler_class(**SCREAMING_SNAKE_CASE__		)

					A__											=					self.dummy_sample
					A__											=					0.1 * sample

					if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__						,	"set_timesteps"		):
						scheduler.set_timesteps(SCREAMING_SNAKE_CASE__		)
					elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__						,	"set_timesteps"		):
						A__											=					num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					A__											=					[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
					A__											=					dummy_past_residuals[:]

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

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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

					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample
					A__											=					scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		).prev_sample

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


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				for timesteps in [100, 1000]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__						,	time_step=SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Dict:
				for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]		):
					self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__						,	time_step=SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					self.full_loop()
				A__											=					torch.mean(torch.abs(SCREAMING_SNAKE_CASE__		)		)

				assert abs(result_mean.item() - 2540529		) < 10









"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	None:
				warnings.warn(
				    "The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DeiTImageProcessor instead."						,	SCREAMING_SNAKE_CASE__						,	)
				super().__init__(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)




