



'''simple docstring'''
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 							lowercase__	(							lowercase   ):
	lowercase__  				=			(DEISMultistepScheduler,)
	lowercase__  				=			(("""num_inference_steps""", 25),)





	def 	UpperCamelCase_  ( self					:				Dict ,**lowerCamelCase__					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   {
							    'num_train_timesteps': 1000,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							}

							config.update(**lowerCamelCase__ )
							return config





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				Tuple=0 ,**lowerCamelCase__					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   dict(self.forward_default_kwargs )
							_UpperCamelCase					:		int 				=   kwargs.pop('num_inference_steps' ,lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   self.dummy_sample
							_UpperCamelCase					:		Union[str, Any] 				=   0.1 * sample
							_UpperCamelCase					:		str 				=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
													_UpperCamelCase					:		Optional[Any] 				=   self.get_scheduler_config(**lowerCamelCase__ )
													_UpperCamelCase					:		Optional[int] 				=   scheduler_class(**lowerCamelCase__ )
													scheduler.set_timesteps(lowerCamelCase__ )
													# copy over dummy past residuals
													_UpperCamelCase					:		Optional[Any] 				=   dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowerCamelCase__ )
																			_UpperCamelCase					:		Tuple 				=   scheduler_class.from_pretrained(lowerCamelCase__ )
																			new_scheduler.set_timesteps(lowerCamelCase__ )
																			# copy over dummy past residuals
																			_UpperCamelCase					:		str 				=   dummy_past_residuals[: new_scheduler.config.solver_order]

													_UpperCamelCase				,			_UpperCamelCase					:		Any 				=   sample, sample
													for t in range(lowerCamelCase__ ,time_step + scheduler.config.solver_order + 1 ):
																			_UpperCamelCase					:		int 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample
																			_UpperCamelCase					:		Optional[Any] 				=   new_scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample

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





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							pass





	def 	UpperCamelCase_  ( self					:				List[Any] ,lowerCamelCase__					:				List[str]=0 ,**lowerCamelCase__					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   dict(self.forward_default_kwargs )
							_UpperCamelCase					:		List[Any] 				=   kwargs.pop('num_inference_steps' ,lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   self.dummy_sample
							_UpperCamelCase					:		str 				=   0.1 * sample
							_UpperCamelCase					:		Tuple 				=   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

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

																			# copy over dummy past residual (must be after setting timesteps)
																			_UpperCamelCase					:		Optional[Any] 				=   dummy_past_residuals[: new_scheduler.config.solver_order]

													_UpperCamelCase					:		Optional[int] 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample
													_UpperCamelCase					:		List[Any] 				=   new_scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample

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





	def 	UpperCamelCase_  ( self					:				Any ,lowerCamelCase__					:				int=None ,**lowerCamelCase__					:				Any ):







							'''simple docstring'''
							if scheduler is None:
													_UpperCamelCase					:		Any 				=   self.scheduler_classes[0]
													_UpperCamelCase					:		Optional[Any] 				=   self.get_scheduler_config(**lowerCamelCase__ )
													_UpperCamelCase					:		str 				=   scheduler_class(**lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   self.scheduler_classes[0]
							_UpperCamelCase					:		str 				=   self.get_scheduler_config(**lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   scheduler_class(**lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   10
							_UpperCamelCase					:		Optional[int] 				=   self.dummy_model()
							_UpperCamelCase					:		Tuple 				=   self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase__ )

							for i, t in enumerate(scheduler.timesteps ):
													_UpperCamelCase					:		Dict 				=   model(lowerCamelCase__ ,lowerCamelCase__ )
													_UpperCamelCase					:		List[str] 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ).prev_sample

							return sample





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   dict(self.forward_default_kwargs )

							_UpperCamelCase					:		Dict 				=   kwargs.pop('num_inference_steps' ,lowerCamelCase__ )

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

													_UpperCamelCase					:		str 				=   self.dummy_sample
													_UpperCamelCase					:		Optional[Any] 				=   0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCamelCase__ ,'set_timesteps' ):
																			scheduler.set_timesteps(lowerCamelCase__ )
													elif num_inference_steps is not None and not hasattr(lowerCamelCase__ ,'set_timesteps' ):
																			_UpperCamelCase					:		List[Any] 				=   num_inference_steps

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

													_UpperCamelCase					:		Union[str, Any] 				=   scheduler.timesteps[5]
													_UpperCamelCase					:		int 				=   scheduler.timesteps[6]

													_UpperCamelCase					:		Tuple 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample
													_UpperCamelCase					:		str 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ ).prev_sample

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





	def 	UpperCamelCase_  ( self					:				Union[str, Any] ):







							'''simple docstring'''
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							_UpperCamelCase					:		List[str] 				=   DEISMultistepScheduler(**self.get_scheduler_config() )
							_UpperCamelCase					:		Any 				=   self.full_loop(scheduler=lowerCamelCase__ )
							_UpperCamelCase					:		List[Any] 				=   torch.mean(torch.abs(lowerCamelCase__ ) )

							assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3

							_UpperCamelCase					:		str 				=   DPMSolverSinglestepScheduler.from_config(scheduler.config )
							_UpperCamelCase					:		List[str] 				=   DPMSolverMultistepScheduler.from_config(scheduler.config )
							_UpperCamelCase					:		Union[str, Any] 				=   UniPCMultistepScheduler.from_config(scheduler.config )
							_UpperCamelCase					:		List[str] 				=   DEISMultistepScheduler.from_config(scheduler.config )

							_UpperCamelCase					:		Dict 				=   self.full_loop(scheduler=lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   torch.mean(torch.abs(lowerCamelCase__ ) )

							assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							for timesteps in [25, 50, 100, 999, 1000]:
													self.check_over_configs(num_train_timesteps=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							self.check_over_configs(thresholding=lowerCamelCase__ )
							for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=lowerCamelCase__ ,prediction_type=lowerCamelCase__ ,sample_max_value=lowerCamelCase__ ,algorithm_type='deis' ,solver_order=lowerCamelCase__ ,solver_type=lowerCamelCase__ ,)





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=lowerCamelCase__ ,solver_type=lowerCamelCase__ ,prediction_type=lowerCamelCase__ ,algorithm_type=lowerCamelCase__ ,)
																															_UpperCamelCase					:		int 				=   self.full_loop(
																															    solver_order=lowerCamelCase__ ,solver_type=lowerCamelCase__ ,prediction_type=lowerCamelCase__ ,algorithm_type=lowerCamelCase__ ,)
																															assert not torch.isnan(lowerCamelCase__ ).any(), "Samples have nan numbers"





	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							self.check_over_configs(lower_order_final=lowerCamelCase__ )
							self.check_over_configs(lower_order_final=lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
													self.check_over_forward(num_inference_steps=lowerCamelCase__ ,time_step=0 )





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   self.full_loop()
							_UpperCamelCase					:		Tuple 				=   torch.mean(torch.abs(lowerCamelCase__ ) )

							assert abs(result_mean.item() - 0.2_3_9_1_6 ) < 1E-3





	def 	UpperCamelCase_  ( self					:				int ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   self.full_loop(prediction_type='v_prediction' )
							_UpperCamelCase					:		Tuple 				=   torch.mean(torch.abs(lowerCamelCase__ ) )

							assert abs(result_mean.item() - 0.0_9_1 ) < 1E-3





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   self.scheduler_classes[0]
							_UpperCamelCase					:		List[Any] 				=   self.get_scheduler_config(thresholding=lowerCamelCase__ ,dynamic_thresholding_ratio=0 )
							_UpperCamelCase					:		Any 				=   scheduler_class(**lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   10
							_UpperCamelCase					:		List[Any] 				=   self.dummy_model()
							_UpperCamelCase					:		Any 				=   self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase__ )

							for i, t in enumerate(scheduler.timesteps ):
													_UpperCamelCase					:		Optional[Any] 				=   model(lowerCamelCase__ ,lowerCamelCase__ )
													_UpperCamelCase					:		List[Any] 				=   scheduler.step(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ).prev_sample

							assert sample.dtype == torch.floataa



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment





class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  inspect.getfile(accelerate.test_utils		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_script.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(
             mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_distributed_data_loop.py"]		)
         UpperCAmelCase__								=  os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["scripts", "test_ops.py"]		)


   @require_multi_gpu
   def 			lowercase_      (self     :						List[str]		)						->				Any:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						str		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices."""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
         print(f"""Command: {cmd}"""		)
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


   @require_multi_gpu
   def 			lowercase_      (self     :						Tuple		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__		)]
         with patch_environment(omp_num_threads=1		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)





   @require_multi_gpu
   def 			lowercase_      (self     :						Dict		)						->				str:
         """simple docstring"""



         print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""		)
         UpperCAmelCase__								=  ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
         with patch_environment(omp_num_threads=1       ,  cuda_visible_devices="0,1"		):
               execute_subprocess_async(__UpperCAmelCase       ,  env=os.environ.copy()		)


if __name__ == "__main__":
      UpperCamelCase__											=					Accelerator()
      UpperCamelCase__											=					(accelerator.state.process_index + 2, 1_0)
      UpperCamelCase__											=					torch.randint(0, 1_0, shape).to(accelerator.device)

      UpperCamelCase__											=					''

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
            error_msg += "Padding was not done with the right value (0)."

      UpperCamelCase__											=					accelerator.pad_across_processes(tensor, pad_first=True)
      if tensora.shape[0] != accelerator.state.num_processes + 1:
            error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
      UpperCamelCase__											=					accelerator.state.num_processes - accelerator.state.process_index - 1
      if not torch.equal(tensora[index:], tensor):
            error_msg += "Tensors have different values."
      if not torch.all(tensora[:index] == 0):
            error_msg += "Padding was not done with the right value (0)."

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)



