



'''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

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser



def    __lowerCamelCase						(      _lowercase=None	)   ->    Optional[int]:
							UpperCAmelCase  :	Union[str, Any]							  =	argparse.ArgumentParser(add_help=_lowercase			,       allow_abbrev=_lowercase	)
							# The main config parser
							UpperCAmelCase  :	Union[str, Any]							  =	config_command_parser(_lowercase	)
							# The subparser to add commands to
							UpperCAmelCase  :	Union[str, Any]							  =	config_parser.add_subparsers(title="""subcommands"""			,       dest="""subcommand"""	)

							# Then add other parsers with the parent parser
							default_command_parser(_lowercase			,       parents=[parent_parser]	)
							update_command_parser(_lowercase			,       parents=[parent_parser]	)

							return config_parser



def    __lowerCamelCase						(      )   ->    Any:
							UpperCAmelCase  :	Dict							  =	get_config_parser()
							UpperCAmelCase  :	Any							  =	config_parser.parse_args()

							if not hasattr(_lowercase			,       """func"""	):
														config_parser.print_help()
														exit(1	)

							# Run
							args.func(_lowercase	)


if __name__ == "__main__":
							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


a     :    Tuple      						=					False

class     UpperCamelCase_				(							unittest.TestCase    ):
	pass




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





	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Any							  =	VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion"""					)
								pipe.to(A					)
								pipe.set_progress_bar_config(disable=A					)

								UpperCAmelCase  :	List[str]							  =	load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""					)
								UpperCAmelCase  :	List[Any]							  =	torch.manual_seed(0					)
								UpperCAmelCase  :	List[str]							  =	pipe(
								    image=A	,	generator=A	,	guidance_scale=7.5	,	num_inference_steps=50	,	output_type="""numpy"""	,	).images

								UpperCAmelCase  :	Tuple							  =	image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase  :	List[str]							  =	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

