
'''simple docstring'''







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  UpperCamelCase__					):
					lowerCAmelCase_					       =					(IPNDMScheduler,)
					lowerCAmelCase_					       =					(('''num_inference_steps''', 5_0),)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	{'''num_train_timesteps''': 10_00}
											config.update(**UpperCamelCase_		)
											return config






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=0    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	dict(self.forward_default_kwargs		)
											lowercase_     :					List[str]													=	kwargs.pop('''num_inference_steps'''    ,							UpperCamelCase_		)
											lowercase_     :					int													=	self.dummy_sample
											lowercase_     :					Union[str, Any]													=	0.1 * sample
											lowercase_     :					Union[str, Any]													=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					str													=	self.get_scheduler_config(**UpperCamelCase_		)
																	lowercase_     :					Dict													=	scheduler_class(**UpperCamelCase_		)
																	scheduler.set_timesteps(UpperCamelCase_		)
																	# copy over dummy past residuals
																	lowercase_     :					int													=	dummy_past_residuals[:]

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

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

																	lowercase_     :					List[Any]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					List[Any]													=	new_scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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

																	lowercase_     :					Dict													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					List[str]													=	new_scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE=0    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	dict(self.forward_default_kwargs		)
											lowercase_     :					int													=	kwargs.pop('''num_inference_steps'''    ,							UpperCamelCase_		)
											lowercase_     :					Dict													=	self.dummy_sample
											lowercase_     :					int													=	0.1 * sample
											lowercase_     :					str													=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					int													=	self.get_scheduler_config()
																	lowercase_     :					List[str]													=	scheduler_class(**UpperCamelCase_		)
																	scheduler.set_timesteps(UpperCamelCase_		)

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

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(UpperCamelCase_		)
																							lowercase_     :					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)
																							lowercase_     :					List[Any]													=	dummy_past_residuals[:]

																	lowercase_     :					Optional[int]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					Union[str, Any]													=	new_scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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

																	lowercase_     :					List[str]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					str													=	new_scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Any													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config(**UpperCamelCase_		)
											lowercase_     :					Tuple													=	scheduler_class(**UpperCamelCase_		)

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

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_     :					Any													=	model(UpperCamelCase_    ,							UpperCamelCase_		)
																	lowercase_     :					List[str]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_		).prev_sample

											for i, t in enumerate(scheduler.timesteps		):
																	lowercase_     :					Optional[int]													=	model(UpperCamelCase_    ,							UpperCamelCase_		)
																	lowercase_     :					Optional[int]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_		).prev_sample

											return sample






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					str													=	dict(self.forward_default_kwargs		)

											lowercase_     :					Union[str, Any]													=	kwargs.pop('''num_inference_steps'''    ,							UpperCamelCase_		)

											for scheduler_class in self.scheduler_classes:
																	lowercase_     :					Union[str, Any]													=	self.get_scheduler_config()
																	lowercase_     :					Union[str, Any]													=	scheduler_class(**UpperCamelCase_		)

																	lowercase_     :					Tuple													=	self.dummy_sample
																	lowercase_     :					Optional[int]													=	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'''		):
																							lowercase_     :					List[Any]													=	num_inference_steps

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

																	lowercase_     :					Any													=	scheduler.timesteps[5]
																	lowercase_     :					List[str]													=	scheduler.timesteps[6]

																	lowercase_     :					Optional[int]													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					Dict													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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

																	lowercase_     :					str													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample
																	lowercase_     :					Tuple													=	scheduler.step(UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							**UpperCamelCase_		).prev_sample

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=UpperCamelCase_    ,							time_step=UpperCamelCase_		)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.full_loop()
											lowercase_     :					Dict													=	torch.mean(torch.abs(UpperCamelCase_		)		)

											assert abs(result_mean.item() - 2_54_05_29		) < 10






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  Tuple   							=	{"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  str   							=	[
							    "VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTMSNModel",
							    "ViTMSNForImageClassification",
							    "ViTMSNPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_msn import (
														    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMSNForImageClassification,
														    ViTMSNModel,
														    ViTMSNPreTrainedModel,
														)

else:
							import sys

							_lowercase			:  int   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



