


"""simple docstring"""







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 		_A    (    SCREAMING_SNAKE_CASE_    ):







    """simple docstring"""




    UpperCAmelCase			:				Optional[Any]      =			(DPMSolverMultistepScheduler,)
    UpperCAmelCase			:				List[Any]      =			(("""num_inference_steps""", 2_5),)






    def       __snake_case				(   self							:       Optional[int]							,					**__UpperCAmelCase							:       Any):
      a		:						int 				=     {
          '''num_train_timesteps''': 1000,
          '''beta_start''': 0.0_001,
          '''beta_end''': 0.02,
          '''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							:       List[Any]							,					__UpperCAmelCase							:       Union[str, Any]=0							,					**__UpperCAmelCase							:       Tuple):
      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 + 0.15, residual + 0.10]

      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		:						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							:       int):
      pass






    def       __snake_case				(   self							:       int							,					__UpperCAmelCase							:       Union[str, Any]=0							,					**__UpperCAmelCase							:       int):
      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 + 0.15, residual + 0.10]

      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							:       List[Any]							,					__UpperCAmelCase							:       str=None							,					**__UpperCAmelCase							:       str):
      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							:       Any):
      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 + 0.15, residual + 0.10]
        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							:       Tuple):
      for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=UpperCamelCase__)






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






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






    def       __snake_case				(   self							:       Union[str, Any]):
      self.check_over_configs(lambda_min_clipped=-float("inf"))
      self.check_over_configs(lambda_min_clipped=-5.1)






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






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

      assert abs(result_mean.item() - 0.3_301) < 1e-3






    def       __snake_case				(   self							:       List[str]):
      a		:						str 				=     self.full_loop(thresholding=UpperCamelCase__							,					dynamic_thresholding_ratio=0.87							,					sample_max_value=0.5)
      a		:						Union[str, Any] 				=     torch.mean(torch.abs(UpperCamelCase__))

      assert abs(result_mean.item() - 1.1_364) < 1e-3






    def       __snake_case				(   self							:       Tuple):
      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_251) < 1e-3






    def       __snake_case				(   self							:       List[Any]):
      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_096) < 1e-3






    def       __snake_case				(   self							:       str):
      # 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_301) < 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_301) < 1e-3






    def       __snake_case				(   self							:       Tuple):
      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							:       int							,					**__UpperCAmelCase							:       int):
      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__		)