






'''simple docstring'''




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	(DDIMParallelScheduler,)
							a__   		=	(('''eta''', 0.0), ('''num_inference_steps''', 50))

							def 							_A	(		self			:Optional[Any]       ,							**lowerCAmelCase__			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "clip_sample": True,
										}

										config.update(**lowerCAmelCase__				)
										return config

							def 							_A	(		self			:Any       ,							**lowerCAmelCase__			:Optional[int]				)				->  Optional[int]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.scheduler_classes[0]
										snake_case_				:					Tuple   =       self.get_scheduler_config(**lowerCAmelCase__				)
										snake_case_				:					Optional[Any]   =       scheduler_class(**lowerCAmelCase__				)

										snake_case_,	snake_case_				:					Union[str, Any]   =       10, 0.0

										snake_case_				:					Any   =       self.dummy_model()
										snake_case_				:					Optional[Any]   =       self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase__				)

										for t in scheduler.timesteps:
													snake_case_				:					Any   =       model(lowerCAmelCase__       ,							lowerCAmelCase__				)
													snake_case_				:					Tuple   =       scheduler.step(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				).prev_sample

										return sample

							def 							_A	(		self			:Dict				)				->  Optional[int]:


										'''simple docstring'''




										for timesteps in [100, 500, 1_000]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__				)

							def 							_A	(		self			:List[str]				)				->  Optional[int]:


										'''simple docstring'''




										for steps_offset in [0, 1]:
													self.check_over_configs(steps_offset=lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       self.scheduler_classes[0]
										snake_case_				:					str   =       self.get_scheduler_config(steps_offset=1				)
										snake_case_				:					Tuple   =       scheduler_class(**lowerCAmelCase__				)
										scheduler.set_timesteps(5				)
										assert torch.equal(scheduler.timesteps       ,							torch.LongTensor([801, 601, 401, 201, 1]				)				)

							def 							_A	(		self			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,							[0.0_0_2, 0.0_2, 0.2, 2]				):
													self.check_over_configs(beta_start=lowerCAmelCase__       ,							beta_end=lowerCAmelCase__				)

							def 							_A	(		self			:Any				)				->  Tuple:


										'''simple docstring'''




										for schedule in ["linear", "squaredcos_cap_v2"]:
													self.check_over_configs(beta_schedule=lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




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

							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Tuple:


										'''simple docstring'''




										for timestep_spacing in ["trailing", "leading"]:
													self.check_over_configs(timestep_spacing=lowerCAmelCase__				)

							def 							_A	(		self			:Optional[int]				)				->  int:


										'''simple docstring'''




										for rescale_betas_zero_snr in [True, False]:
													self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__				)

							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										self.check_over_configs(thresholding=lowerCAmelCase__				)
										for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(
																    thresholding=lowerCAmelCase__       ,							prediction_type=lowerCAmelCase__       ,							sample_max_value=lowerCAmelCase__       ,							)

							def 							_A	(		self			:List[str]				)				->  Dict:


										'''simple docstring'''




										for t in [1, 10, 49]:
													self.check_over_forward(time_step=lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  Optional[Any]:


										'''simple docstring'''




										for t, num_inference_steps in zip([1, 10, 50]       ,							[10, 50, 500]				):
													self.check_over_forward(time_step=lowerCAmelCase__       ,							num_inference_steps=lowerCAmelCase__				)

							def 							_A	(		self			:int				)				->  Union[str, Any]:


										'''simple docstring'''




										for t, eta in zip([1, 10, 49]       ,							[0.0, 0.5, 1.0]				):
													self.check_over_forward(time_step=lowerCAmelCase__       ,							eta=lowerCAmelCase__				)

							def 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       self.scheduler_classes[0]
										snake_case_				:					Any   =       self.get_scheduler_config()
										snake_case_				:					str   =       scheduler_class(**lowerCAmelCase__				)

										assert torch.sum(torch.abs(scheduler._get_variance(0       ,							0				) - 0.0				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420       ,							400				) - 0.1_4_7_7_1				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980       ,							960				) - 0.3_2_4_6_0				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(0       ,							0				) - 0.0				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ,							486				) - 0.0_0_9_7_9				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ,							998				) - 0.0_2				)				) < 1E-5

							def 							_A	(		self			:List[str]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					str   =       self.scheduler_classes[0]
										snake_case_				:					Optional[Any]   =       self.get_scheduler_config()
										snake_case_				:					Tuple   =       scheduler_class(**lowerCAmelCase__				)

										snake_case_,	snake_case_				:					Any   =       10, 0.0
										scheduler.set_timesteps(lowerCAmelCase__				)

										snake_case_				:					Optional[int]   =       self.dummy_model()
										snake_case_				:					Tuple   =       self.dummy_sample_deter
										snake_case_				:					str   =       self.dummy_sample_deter + 0.1
										snake_case_				:					Optional[Any]   =       self.dummy_sample_deter - 0.1

										snake_case_				:					int   =       samplea.shape[0]
										snake_case_				:					List[str]   =       torch.stack([samplea, samplea, samplea]       ,							dim=0				)
										snake_case_				:					int   =       torch.arange(lowerCAmelCase__				)[0:3, None].repeat(1       ,							lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       model(samples.flatten(0       ,							1				)       ,							timesteps.flatten(0       ,							1				)				)
										snake_case_				:					List[Any]   =       scheduler.batch_step_no_noise(lowerCAmelCase__       ,							timesteps.flatten(0       ,							1				)       ,							samples.flatten(0       ,							1				)       ,							lowerCAmelCase__				)

										snake_case_				:					Any   =       torch.sum(torch.abs(lowerCAmelCase__				)				)
										snake_case_				:					List[Any]   =       torch.mean(torch.abs(lowerCAmelCase__				)				)

										assert abs(result_sum.item() - 1_1_4_7.7_9_0_4				) < 1E-2
										assert abs(result_mean.item() - 0.4_9_8_2				) < 1E-3

							def 							_A	(		self			:Tuple				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.full_loop()

										snake_case_				:					Optional[Any]   =       torch.sum(torch.abs(lowerCAmelCase__				)				)
										snake_case_				:					List[str]   =       torch.mean(torch.abs(lowerCAmelCase__				)				)

										assert abs(result_sum.item() - 1_7_2.0_0_6_7				) < 1E-2
										assert abs(result_mean.item() - 0.2_2_3_9_6_7				) < 1E-3

							def 							_A	(		self			:int				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self.full_loop(prediction_type="v_prediction"				)

										snake_case_				:					str   =       torch.sum(torch.abs(lowerCAmelCase__				)				)
										snake_case_				:					List[str]   =       torch.mean(torch.abs(lowerCAmelCase__				)				)

										assert abs(result_sum.item() - 5_2.5_3_0_2				) < 1E-2
										assert abs(result_mean.item() - 0.0_6_8_4				) < 1E-3

							def 							_A	(		self			:Optional[int]				)				->  str:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.full_loop(set_alpha_to_one=lowerCAmelCase__       ,							beta_start=0.0_1				)
										snake_case_				:					Union[str, Any]   =       torch.sum(torch.abs(lowerCAmelCase__				)				)
										snake_case_				:					Any   =       torch.mean(torch.abs(lowerCAmelCase__				)				)

										assert abs(result_sum.item() - 1_4_9.8_2_9_5				) < 1E-2
										assert abs(result_mean.item() - 0.1_9_5_1				) < 1E-3






							def 							_A	(		self			:Dict				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.full_loop(set_alpha_to_one=lowerCAmelCase__       ,							beta_start=0.0_1				)
										snake_case_				:					Tuple   =       torch.sum(torch.abs(lowerCAmelCase__				)				)
										snake_case_				:					Tuple   =       torch.mean(torch.abs(lowerCAmelCase__				)				)

										assert abs(result_sum.item() - 1_4_9.0_7_8_4				) < 1E-2
										assert abs(result_mean.item() - 0.1_9_4_1				) < 1E-3












'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	__UpperCAmelCase       ( __magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if subparsers is not None:
						snake_case_				:					List[str]   =       subparsers.add_parser("test"						)
			else:
						snake_case_				:					List[Any]   =       argparse.ArgumentParser("Accelerate test command"						)

			parser.add_argument(
			    "--config_file"     ,default=__magic_name__     ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
			        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with 'huggingface'."
			    )     ,)

			if subparsers is not None:
						parser.set_defaults(func=__magic_name__						)
			return parser

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ["test_utils", "scripts", "test_script.py"]						)

			if args.config_file is None:
						snake_case_				:					str   =       script_name
			else:
						snake_case_				:					Any   =       F'''--config_file={args.config_file} {script_name}'''

			snake_case_				:					Union[str, Any]   =       ["accelerate-launch"] + test_args.split()
			snake_case_				:					Optional[int]   =       execute_subprocess_async(__magic_name__     ,env=os.environ.copy()						)
			if result.returncode == 0:
						print("Test is a success! You are ready for your distributed training!"						)

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       test_command_parser()
			snake_case_				:					Dict   =       parser.parse_args()
			test_command(__magic_name__						)


if __name__ == "__main__":
		main()




