
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					(DPMSolverMultistepScheduler,)
	_lowerCAmelCase											=					(("num_inference_steps", 25),)





	def 	__snake_case					(      self					,							**UpperCamelCase__		):
					A__	:						int								=				{
					    '''num_train_timesteps''': 1000,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''solver_order''': 2,
					    '''prediction_type''': '''epsilon''',
					    '''thresholding''': False,
					    '''sample_max_value''': 1.0,
					    '''algorithm_type''': '''dpmsolver++''',
					    '''solver_type''': '''midpoint''',
					    '''lower_order_final''': False,
					    '''lambda_min_clipped''': -float('''inf'''		),
					    '''variance_type''': None,
					}

					config.update(**UpperCamelCase__		)
					return config





	def 	__snake_case					(      self					,							UpperCamelCase__=0					,							**UpperCamelCase__		):
					A__	:						Tuple								=				dict(self.forward_default_kwargs		)
					A__	:						Any								=				kwargs.pop('''num_inference_steps'''					,							UpperCamelCase__		)
					A__	:						Dict								=				self.dummy_sample
					A__	:						int								=				0.1 * sample
					A__	:						Optional[int]								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

									A__							,				A__	:						str								=				sample, sample
									for t in range(UpperCamelCase__					,							time_step + scheduler.config.solver_order + 1		):
													A__	:						Dict								=				scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							**UpperCamelCase__		).prev_sample
													A__	:						int								=				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		):
					pass





	def 	__snake_case					(      self					,							UpperCamelCase__=0					,							**UpperCamelCase__		):
					A__	:						Dict								=				dict(self.forward_default_kwargs		)
					A__	:						Optional[Any]								=				kwargs.pop('''num_inference_steps'''					,							UpperCamelCase__		)
					A__	:						Dict								=				self.dummy_sample
					A__	:						Optional[int]								=				0.1 * sample
					A__	:						int								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									A__	:						Union[str, Any]								=				self.get_scheduler_config()
									A__	:						List[Any]								=				scheduler_class(**UpperCamelCase__		)
									scheduler.set_timesteps(UpperCamelCase__		)

									# copy over dummy past residuals (must be after setting timesteps)
									A__	:						str								=				dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(UpperCamelCase__		)
													A__	:						Optional[Any]								=				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)
													A__	:						List[Any]								=				dummy_past_residuals[: new_scheduler.config.solver_order]

									A__	:						List[str]								=				scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							**UpperCamelCase__		).prev_sample
									A__	:						Dict								=				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					,							UpperCamelCase__=None					,							**UpperCamelCase__		):
					if scheduler is None:
									A__	:						Union[str, Any]								=				self.scheduler_classes[0]
									A__	:						Dict								=				self.get_scheduler_config(**UpperCamelCase__		)
									A__	:						Dict								=				scheduler_class(**UpperCamelCase__		)

					A__	:						List[Any]								=				10
					A__	:						Tuple								=				self.dummy_model()
					A__	:						Tuple								=				self.dummy_sample_deter
					scheduler.set_timesteps(UpperCamelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
									A__	:						Union[str, Any]								=				model(UpperCamelCase__					,							UpperCamelCase__		)
									A__	:						Tuple								=				scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		).prev_sample

					return sample





	def 	__snake_case					(      self		):
					A__	:						str								=				dict(self.forward_default_kwargs		)

					A__	:						Any								=				kwargs.pop('''num_inference_steps'''					,							UpperCamelCase__		)

					for scheduler_class in self.scheduler_classes:
									A__	:						Union[str, Any]								=				self.get_scheduler_config()
									A__	:						Optional[int]								=				scheduler_class(**UpperCamelCase__		)

									A__	:						int								=				self.dummy_sample
									A__	:						str								=				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'''		):
													A__	:						Optional[int]								=				num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									A__	:						Any								=				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									A__	:						int								=				dummy_past_residuals[: scheduler.config.solver_order]

									A__	:						int								=				scheduler.timesteps[5]
									A__	:						List[str]								=				scheduler.timesteps[6]

									A__	:						List[Any]								=				scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__					,							**UpperCamelCase__		).prev_sample
									A__	:						Dict								=				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		):
					for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					self.check_over_configs(thresholding=UpperCamelCase__		)
					for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
													for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=UpperCamelCase__					,							prediction_type=UpperCamelCase__					,							sample_max_value=UpperCamelCase__					,							algorithm_type='''dpmsolver++'''					,							solver_order=UpperCamelCase__					,							solver_type=UpperCamelCase__					,							)





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





	def 	__snake_case					(      self		):
					for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
													for order in [1, 2, 3]:
																	for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																									if order == 3:
																													continue
																					else:
																									self.check_over_configs(
																									    solver_order=UpperCamelCase__					,							solver_type=UpperCamelCase__					,							prediction_type=UpperCamelCase__					,							algorithm_type=UpperCamelCase__					,							)
																					A__	:						Union[str, Any]								=				self.full_loop(
																					    solver_order=UpperCamelCase__					,							solver_type=UpperCamelCase__					,							prediction_type=UpperCamelCase__					,							algorithm_type=UpperCamelCase__					,							)
																					assert not torch.isnan(UpperCamelCase__		).any(), "Samples have nan numbers"





	def 	__snake_case					(      self		):
					self.check_over_configs(lower_order_final=UpperCamelCase__		)
					self.check_over_configs(lower_order_final=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					self.check_over_configs(lambda_min_clipped=-float('''inf'''		)		)
					self.check_over_configs(lambda_min_clipped=-5.1		)





	def 	__snake_case					(      self		):
					self.check_over_configs(variance_type=UpperCamelCase__		)
					self.check_over_configs(variance_type='''learned_range'''		)





	def 	__snake_case					(      self		):
					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=UpperCamelCase__					,							time_step=0		)





	def 	__snake_case					(      self		):
					A__	:						Optional[Any]								=				self.full_loop()
					A__	:						List[str]								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.3_3_0_1		) < 1e-3





	def 	__snake_case					(      self		):
					A__	:						str								=				self.full_loop(thresholding=UpperCamelCase__					,							dynamic_thresholding_ratio=0.8_7					,							sample_max_value=0.5		)
					A__	:						Union[str, Any]								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 1.1_3_6_4		) < 1e-3





	def 	__snake_case					(      self		):
					A__	:						List[Any]								=				self.full_loop(prediction_type='''v_prediction'''		)
					A__	:						List[str]								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.2_2_5_1		) < 1e-3





	def 	__snake_case					(      self		):
					A__	:						int								=				self.full_loop(prediction_type='''v_prediction'''					,							use_karras_sigmas=UpperCamelCase__		)
					A__	:						str								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.2_0_9_6		) < 1e-3





	def 	__snake_case					(      self		):
					# make sure that iterating over schedulers with same config names gives same results
					# for defaults
					A__	:						int								=				DPMSolverMultistepScheduler(**self.get_scheduler_config()		)
					A__	:						Union[str, Any]								=				self.full_loop(scheduler=UpperCamelCase__		)
					A__	:						Union[str, Any]								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.3_3_0_1		) < 1e-3

					A__	:						Tuple								=				DPMSolverSinglestepScheduler.from_config(scheduler.config		)
					A__	:						Optional[int]								=				UniPCMultistepScheduler.from_config(scheduler.config		)
					A__	:						List[Any]								=				DEISMultistepScheduler.from_config(scheduler.config		)
					A__	:						Union[str, Any]								=				DPMSolverMultistepScheduler.from_config(scheduler.config		)

					A__	:						Any								=				self.full_loop(scheduler=UpperCamelCase__		)
					A__	:						Dict								=				torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.3_3_0_1		) < 1e-3





	def 	__snake_case					(      self		):
					A__	:						str								=				self.scheduler_classes[0]
					A__	:						Optional[int]								=				self.get_scheduler_config(thresholding=UpperCamelCase__					,							dynamic_thresholding_ratio=0		)
					A__	:						int								=				scheduler_class(**UpperCamelCase__		)

					A__	:						Any								=				10
					A__	:						Dict								=				self.dummy_model()
					A__	:						int								=				self.dummy_sample_deter.half()
					scheduler.set_timesteps(UpperCamelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
									A__	:						Any								=				model(UpperCamelCase__					,							UpperCamelCase__		)
									A__	:						Union[str, Any]								=				scheduler.step(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		).prev_sample

					assert sample.dtype == torch.floataa





	def 	__snake_case					(      self					,							**UpperCamelCase__		):
					for scheduler_class in self.scheduler_classes:
									A__	:						int								=				self.get_scheduler_config(**UpperCamelCase__		)
									A__	:						Tuple								=				scheduler_class(**UpperCamelCase__		)

									scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
									assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps

import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any=2				,    __UpperCamelCase			:    str=True				,    __UpperCamelCase			:    Optional[Any]=False							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						List[str]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						str								=				torch.nn.Linear(2				,    4							)
				A__	:						Any								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						Tuple								=				torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase				,    max_lr=0.0_1				,    steps_per_epoch=2				,    epochs=1							)
				A__							,				A__							,				A__	:						str								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				scheduler.step()
				if step_scheduler_with_optimizer or (num_processes == 1):
								assert (
								    scheduler.scheduler.last_epoch == num_processes
								), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
				else:
								assert (
								    scheduler.scheduler.last_epoch != num_processes
								), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Optional[Any]=2				,    __UpperCamelCase			:    Tuple=True				,    __UpperCamelCase			:    List[Any]=False							)			->							str:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				Accelerator(step_scheduler_with_optimizer=__UpperCamelCase				,    split_batches=__UpperCamelCase							)
				A__	:						List[str]								=				torch.nn.Linear(2				,    4							)
				A__	:						int								=				torch.optim.AdamW(model.parameters()				,    lr=1.0							)
				A__	:						List[Any]								=				torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase				,    lr_lambda=lambda __UpperCamelCase							: 1 - n / 10							)
				A__							,				A__							,				A__	:						int								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

				# Optimizer has stepped
				A__	:						List[Any]								=				False
				scheduler.step()
				A__	:						str								=				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

				# Optimizer has not stepped
				A__	:						str								=				True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								A__	:						Dict								=				1 - 2 / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int = 2							)			->							List[Any]:

				"""simple docstring"""







				from transformers import get_linear_schedule_with_warmup

				A__	:						str								=				[1, 2, 4]
				for num_steps in steps:
								A__	:						Any								=				GradientAccumulationPlugin(num_steps=__UpperCamelCase				,    adjust_scheduler=num_steps > 1							)
								A__	:						Tuple								=				Accelerator(gradient_accumulation_plugin=__UpperCamelCase							)
								A__	:						Optional[int]								=				torch.nn.Linear(2				,    4							)
								A__	:						Optional[int]								=				torch.optim.AdamW(model.parameters()				,    lr=1_0.0							)
								A__	:						Optional[int]								=				get_linear_schedule_with_warmup(optimizer=__UpperCamelCase				,    num_warmup_steps=0				,    num_training_steps=20							)

								A__							,				A__							,				A__	:						List[Any]								=				accelerator.prepare(__UpperCamelCase				,    __UpperCamelCase				,    __UpperCamelCase							)

								for i in range(10 * num_steps							):
												with accelerator.accumulate(__UpperCamelCase							):
																optimizer.step()
																scheduler.step()

												if i == (10 * num_steps - 2):
																assert (
																    scheduler.get_last_lr()[0] != 0
																), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
								assert (
								    scheduler.get_last_lr()[0] == 0
								), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
								GradientState._reset_state()







@require_cpu
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)					,							num_processes=1		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							step_scheduler_with_optimizer=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1					,							split_batches=UpperCamelCase__		)					,							num_processes=1		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							step_scheduler_with_optimizer=UpperCamelCase__		)		)





	@require_huggingface_suite
	def 	__snake_case					(      self		):
					AcceleratorState._reset_state(UpperCamelCase__		)
					debug_launcher(partial(UpperCamelCase__					,							num_processes=1		)		)
					debug_launcher(UpperCamelCase__		)