





import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class 				a			(      unittest.TestCase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   UNetaDModel(
										    block_out_channels=(32, 64)			, layers_per_block=2			, sample_size=32			, in_channels=3			, out_channels=3			, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')			, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')			, )
										return model






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.dummy_uncond_unet
										__SCREAMING_SNAKE_CASE:				List[Any]           =   KarrasVeScheduler()

										__SCREAMING_SNAKE_CASE:				List[Any]           =   KarrasVePipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   pipe(num_inference_steps=2			, generator=_lowerCAmelCase			, output_type='''numpy'''					).images

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				str           =   pipe(num_inference_steps=2			, generator=_lowerCAmelCase			, output_type='''numpy'''			, return_dict=_lowerCAmelCase					)[0]

										__SCREAMING_SNAKE_CASE:				Tuple           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				int           =   image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										__SCREAMING_SNAKE_CASE:				Dict           =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2





@slow
@require_torch
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''google/ncsnpp-celebahq-256'''
										__SCREAMING_SNAKE_CASE:				Dict           =   UNetaDModel.from_pretrained(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   KarrasVeScheduler()

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   KarrasVePipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Any           =   torch.manual_seed(0					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe(num_inference_steps=20			, generator=_lowerCAmelCase			, output_type='''numpy'''					).images

										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2










import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict





@dataclass
class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ComputeEnvironment.AMAZON_SAGEMAKER
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     True
				SCREAMING_SNAKE_CASE__     :   int		      =     '''ml.p3.2xlarge'''
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''accelerate_sagemaker_execution_role'''
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     '''hf-sm'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''us-east-1'''
				SCREAMING_SNAKE_CASE__     :   Any		      =     1
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''accelerate-sagemaker-1'''
				SCREAMING_SNAKE_CASE__     :   Dict		      =     '''1.6'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''4.4'''
				SCREAMING_SNAKE_CASE__     :   int		      =     '''train.py'''
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''False''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     [
				    '''--model_name_or_path''',
				    '''bert''',
				    '''--do_train''',
				    '''--do_test''',
				    '''False''',
				    '''--do_predict''',
				    '''--epochs''',
				    '''3''',
				    '''--learning_rate''',
				    '''5e-5''',
				    '''--max_steps''',
				    '''50.5''',
				]





class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args					)
										assert isinstance(converted_args['''model_name_or_path''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''do_train''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''epochs''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''learning_rate''']			, _lowerCAmelCase					)
										assert isinstance(converted_args['''max_steps''']			, _lowerCAmelCase					)

										with pytest.raises(_lowerCAmelCase					):
																_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args					)



