



'''simple docstring'''




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort







class       _lowerCAmelCase				(     UpperCamelCase_ ,     unittest.TestCase					):
	# FIXME: add fast tests
	pass







@nightly
@require_onnxruntime
@require_torch_gpu
class       _lowerCAmelCase				(     unittest.TestCase					):


	@property
	def        _a      (self				):
				return (
				    "CUDAExecutionProvider",
				    {
				        "gpu_mem_limit": "15000000000",  # 15GB
				        "arena_extend_strategy": "kSameAsRequested",
				    },
				)


	@property
	def        _a      (self				):
				A_      :		List[str]      =	ort.SessionOptions()
				A_      :		Tuple      =	False
				return options


	def        _a      (self				):
				A_      :		int      =	load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/in_paint/overture-creations-5sI6fQgYIuo.png"""				)
				A_      :		List[str]      =	load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""				)
				A_      :		Dict      =	OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-inpainting"""			, revision="""onnx"""			, safety_checker=lowercase_			, feature_extractor=lowercase_			, provider=self.gpu_provider			, sess_options=self.gpu_options			, )
				pipe.set_progress_bar_config(disable=lowercase_				)

				A_      :		Optional[int]      =	'A red cat sitting on a park bench'

				A_      :		List[Any]      =	np.random.RandomState(0				)
				A_      :		List[Any]      =	pipe(
				    prompt=lowercase_			, image=lowercase_			, mask_image=lowercase_			, guidance_scale=7.5			, num_inference_steps=10			, generator=lowercase_			, output_type="""np"""			, )
				A_      :		int      =	output.images
				A_      :		Tuple      =	images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				A_      :		Tuple      =	np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]				)

				assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3


	def        _a      (self				):
				A_      :		Dict      =	load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/in_paint/overture-creations-5sI6fQgYIuo.png"""				)
				A_      :		Optional[Any]      =	load_image(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""				)
				A_      :		List[str]      =	LMSDiscreteScheduler.from_pretrained(
				    """runwayml/stable-diffusion-inpainting"""			, subfolder="""scheduler"""			, revision="""onnx"""				)
				A_      :		Tuple      =	OnnxStableDiffusionInpaintPipeline.from_pretrained(
				    """runwayml/stable-diffusion-inpainting"""			, revision="""onnx"""			, scheduler=lowercase_			, safety_checker=lowercase_			, feature_extractor=lowercase_			, provider=self.gpu_provider			, sess_options=self.gpu_options			, )
				pipe.set_progress_bar_config(disable=lowercase_				)

				A_      :		List[str]      =	'A red cat sitting on a park bench'

				A_      :		Optional[int]      =	np.random.RandomState(0				)
				A_      :		List[Any]      =	pipe(
				    prompt=lowercase_			, image=lowercase_			, mask_image=lowercase_			, guidance_scale=7.5			, num_inference_steps=20			, generator=lowercase_			, output_type="""np"""			, )
				A_      :		int      =	output.images
				A_      :		List[str]      =	images[0, 255:258, 255:258, -1]

				assert images.shape == (1, 512, 512, 3)
				A_      :		List[Any]      =	np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]				)

				assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3



# Copyright 2022 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
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_ (       SCREAMING_SNAKE_CASE__   : str=None	):
       '''simple docstring'''





       if subparsers is not None:
              _lowerCamelCase			:						str            =subparsers.add_parser('env'	)
       else:
              _lowerCamelCase			:						List[Any]            =argparse.ArgumentParser('Accelerate env command'	)

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

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




def       a_ (       SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =torch.__version__
       _lowerCamelCase			:						Optional[Any]            =torch.cuda.is_available()
       _lowerCamelCase			:						int            =is_xpu_available()
       _lowerCamelCase			:						Union[str, Any]            =is_npu_available()

       _lowerCamelCase			:						Optional[int]            ='Not found'
       # Get the default from the config file.
       if args.config_file is not None or os.path.isfile(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						Any            =load_config_from_file(args.config_file	).to_dict()

       _lowerCamelCase			:						List[str]            ={
           '`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(SCREAMING_SNAKE_CASE__	),
           'PyTorch NPU available': str(SCREAMING_SNAKE_CASE__	),
           'System RAM': F'''{psutil.virtual_memory().total / 1_024 ** 3:.2f} GB''',
       }
       if pt_cuda_available:
              _lowerCamelCase			:						List[Any]            =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:'	)
       _lowerCamelCase			:						Dict            =(
           '\n'.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
           if isinstance(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
           else F'''\t{accelerate_config}'''
       )
       print(SCREAMING_SNAKE_CASE__	)

       _lowerCamelCase			:						str            =accelerate_config

       return info




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =env_command_parser()
       _lowerCamelCase			:						Any            =parser.parse_args()
       env_command(SCREAMING_SNAKE_CASE__	)
       return 0


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


