


"""simple docstring"""






import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available



def 						a_		(      lowerCamelCase=None ):
				if subparsers is not None:
								UpperCAmelCase__    =							subparsers.add_parser('env' )
				else:
								UpperCAmelCase__    =							argparse.ArgumentParser('Accelerate env command' )

				parser.add_argument(
				    '--config_file'      ,    default=lowerCamelCase      ,    help='The config file to use for the default values in the launching script.' )

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



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							torch.__version__
				UpperCAmelCase__    =							torch.cuda.is_available()
				UpperCAmelCase__    =							is_xpu_available()
				UpperCAmelCase__    =							is_npu_available()

				UpperCAmelCase__    =							'Not found'
				# Get the default from the config file.
				if args.config_file is not None or os.path.isfile(lowerCamelCase ):
								UpperCAmelCase__    =							load_config_from_file(args.config_file ).to_dict()

				UpperCAmelCase__    =							{
				    '`Accelerate` version': version,
				    'Platform': platform.platform(),
				    'Python version': platform.python_version(),
				    'Numpy version': np.__version__,
				    'PyTorch version (GPU?)': f'''{pt_version} ({pt_cuda_available})''',
				    'PyTorch XPU available': str(lowerCamelCase ),
				    'PyTorch NPU available': str(lowerCamelCase ),
				    'System RAM': f'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
				}
				if pt_cuda_available:
								UpperCAmelCase__    =							torch.cuda.get_device_name()

				print('\nCopy-and-paste the text below in your GitHub issue\n' )
				print('\n'.join([f'''- {prop}: {val}''' for prop, val in info.items()] ) )

				print('- `Accelerate` default config:' if args.config_file is None else '- `Accelerate` config passed:' )
				UpperCAmelCase__    =							(
				    '\n'.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
				    if isinstance(lowerCamelCase      ,    lowerCamelCase )
				    else f'''\t{accelerate_config}'''
				)
				print(lowerCamelCase )

				UpperCAmelCase__    =							accelerate_config

				return info



def 						a_		(      ):
				UpperCAmelCase__    =							env_command_parser()
				UpperCAmelCase__    =							parser.parse_args()
				env_command(lowerCamelCase )
				return 0


if __name__ == "__main__":
						raise SystemExit(main())




"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCAmelCase__						:						Optional[int]		  =			False
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					pass






@slow
@require_torch_gpu
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__    =							VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'    )
									UpperCAmelCase__    =							torch.manual_seed(0    )
									UpperCAmelCase__    =							pipe(
									    image=lowerCamelCase__					,generator=lowerCamelCase__					,guidance_scale=7.5					,num_inference_steps=50					,output_type='numpy'					,).images

									UpperCAmelCase__    =							image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									UpperCAmelCase__    =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]    )

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



