



'''simple docstring'''




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		List[str]		      = (IPNDMScheduler,)
	__SCREAMING_SNAKE_CASE       :		Tuple		      = (('num_inference_steps', 50),)


	def    _a     (self	,				**lowercase							):
				A_     :						List[Any]     =					{"""num_train_timesteps""": 1000}
				config.update(**lowercase							)
				return config


	def    _a     (self	,				lowercase=0	,				**lowercase							):
				A_     :						int     =					dict(self.forward_default_kwargs							)
				A_     :						Tuple     =					kwargs.pop("""num_inference_steps"""	,				lowercase							)
				A_     :						Optional[int]     =					self.dummy_sample
				A_     :						Tuple     =					0.1 * sample
				A_     :						Optional[Any]     =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowercase							)
										A_     :						List[str]     =					scheduler_class.from_pretrained(lowercase							)
										new_scheduler.set_timesteps(lowercase							)
										# copy over dummy past residuals
										A_     :						Union[str, Any]     =					dummy_past_residuals[:]

							A_     :						int     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						Any     =					new_scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample

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

							A_     :						str     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						Union[str, Any]     =					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    _a     (self							):
				pass


	def    _a     (self	,				lowercase=0	,				**lowercase							):
				A_     :						Tuple     =					dict(self.forward_default_kwargs							)
				A_     :						Union[str, Any]     =					kwargs.pop("""num_inference_steps"""	,				lowercase							)
				A_     :						List[Any]     =					self.dummy_sample
				A_     :						Tuple     =					0.1 * sample
				A_     :						int     =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

				for scheduler_class in self.scheduler_classes:
							A_     :						Tuple     =					self.get_scheduler_config()
							A_     :						Optional[int]     =					scheduler_class(**lowercase							)
							scheduler.set_timesteps(lowercase							)

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

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

							with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(lowercase							)
										A_     :						Union[str, Any]     =					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)
										A_     :						List[Any]     =					dummy_past_residuals[:]

							A_     :						str     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						List[Any]     =					new_scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample

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

							A_     :						List[Any]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						Dict     =					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    _a     (self	,				**lowercase							):
				A_     :						List[Any]     =					self.scheduler_classes[0]
				A_     :						List[str]     =					self.get_scheduler_config(**lowercase							)
				A_     :						List[str]     =					scheduler_class(**lowercase							)

				A_     :						List[str]     =					10
				A_     :						List[str]     =					self.dummy_model()
				A_     :						Tuple     =					self.dummy_sample_deter
				scheduler.set_timesteps(lowercase							)

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

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

				return sample


	def    _a     (self							):
				A_     :						Any     =					dict(self.forward_default_kwargs							)

				A_     :						Union[str, Any]     =					kwargs.pop("""num_inference_steps"""	,				lowercase							)

				for scheduler_class in self.scheduler_classes:
							A_     :						Optional[Any]     =					self.get_scheduler_config()
							A_     :						List[Any]     =					scheduler_class(**lowercase							)

							A_     :						Union[str, Any]     =					self.dummy_sample
							A_     :						Optional[Any]     =					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"""							):
										A_     :						Union[str, Any]     =					num_inference_steps

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

							A_     :						Any     =					scheduler.timesteps[5]
							A_     :						Any     =					scheduler.timesteps[6]

							A_     :						int     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						Optional[Any]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample

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

							A_     :						str     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample
							A_     :						List[str]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				**lowercase							).prev_sample

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


	def    _a     (self							):
				for timesteps in [100, 1000]:
							self.check_over_configs(num_train_timesteps=lowercase	,				time_step=lowercase							)


	def    _a     (self							):
				for t, num_inference_steps in zip([1, 5, 10]	,				[10, 50, 100]							):
							self.check_over_forward(num_inference_steps=lowercase	,				time_step=lowercase							)


	def    _a     (self							):
				A_     :						List[Any]     =					self.full_loop()
				A_     :						Optional[int]     =					torch.mean(torch.abs(lowercase							)							)

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




'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch







@require_torch
class      _lowerCAmelCase							:


	@property
	def    _a     (self							):
				return self.get_dummy_input()


	@property
	def    _a     (self							):
				if self.block_type == "down":
							return (4, 32, 16, 16)
				elif self.block_type == "mid":
							return (4, 32, 32, 32)
				elif self.block_type == "up":
							return (4, 32, 64, 64)

				raise ValueError(F'\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'							)


	def    _a     (self	,				lowercase=True	,				lowercase=False	,				lowercase=False	,				lowercase=False	,				):
				A_     :						List[str]     =					4
				A_     :						int     =					32
				A_     :						Optional[int]     =					(32, 32)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)
				A_     :						int     =					torch.device(lowercase							)
				A_     :						int     =					(batch_size, num_channels) + sizes
				A_     :						Optional[int]     =					randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							)
				A_     :						Union[str, Any]     =					{"""hidden_states""": hidden_states}

				if include_temb:
							A_     :						str     =					128
							A_     :						List[Any]     =					randn_tensor((batch_size, temb_channels)	,				generator=lowercase	,				device=lowercase							)

				if include_res_hidden_states_tuple:
							A_     :						List[str]     =					torch.manual_seed(1							)
							A_     :						int     =					(randn_tensor(lowercase	,				generator=lowercase	,				device=lowercase							),)

				if include_encoder_hidden_states:
							A_     :						List[str]     =					floats_tensor((batch_size, 32, 32)							).to(lowercase							)

				if include_skip_sample:
							A_     :						Dict     =					randn_tensor(((batch_size, 3) + sizes)	,				generator=lowercase	,				device=lowercase							)

				return dummy_input


	def    _a     (self							):
				A_     :						Tuple     =					{
				    """in_channels""": 32,
				    """out_channels""": 32,
				    """temb_channels""": 128,
				}
				if self.block_type == "up":
							A_     :						Any     =					32

				if self.block_type == "mid":
							init_dict.pop("""out_channels"""							)

				A_     :						Optional[int]     =					self.dummy_input
				return init_dict, inputs_dict


	def    _a     (self	,				lowercase							):
				A_,   A_     :						Optional[Any]     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						int     =					self.block_class(**lowercase							)
				unet_block.to(lowercase							)
				unet_block.eval()

				with torch.no_grad():
							A_     :						List[str]     =					unet_block(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						Union[str, Any]     =					output[0]

				self.assertEqual(output.shape	,				self.output_shape							)

				A_     :						int     =					output[0, -1, -3:, -3:]
				A_     :						List[Any]     =					torch.tensor(lowercase							).to(lowercase							)
				assert torch_all_close(output_slice.flatten()	,				lowercase	,				atol=5E-3							)


	@unittest.skipIf(torch_device == """mps"""	,				"""Training is not supported in mps"""							)
	def    _a     (self							):
				A_,   A_     :						Tuple     =					self.prepare_init_args_and_inputs_for_common()
				A_     :						List[str]     =					self.block_class(**lowercase							)
				model.to(lowercase							)
				model.train()
				A_     :						Any     =					model(**lowercase							)

				if isinstance(lowercase	,				lowercase							):
							A_     :						str     =					output[0]

				A_     :						Union[str, Any]     =					torch.device(lowercase							)
				A_     :						Tuple     =					randn_tensor(output.shape	,				device=lowercase							)
				A_     :						List[str]     =					torch.nn.functional.mse_loss(lowercase	,				lowercase							)
				loss.backward()