
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case					:						Dict				 =    False





class  _snake_case   (	unittest.TestCase							):
			pass







@nightly
@require_torch_gpu
class  _snake_case   (	unittest.TestCase							):







			def      SCREAMING_SNAKE_CASE       (							self						):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[str]        =	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,  torch_dtype=torch.floataa						)
				pipe.to(__SCREAMING_SNAKE_CASE						)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

				__magic_name__						:						List[Any]        =	load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)

				__magic_name__						:						Any        =	torch.manual_seed(0						)
				__magic_name__						:						str        =	pipe.dual_guided(
				    prompt="first prompt"		,  image=__SCREAMING_SNAKE_CASE		,  text_to_image_strength=0.75		,  generator=__SCREAMING_SNAKE_CASE		,  guidance_scale=7.5		,  num_inference_steps=2		,  output_type="numpy"		,  ).images

				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(__SCREAMING_SNAKE_CASE						)
					__magic_name__						:						int        =	VersatileDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE		,  torch_dtype=torch.floataa						)
				pipe.to(__SCREAMING_SNAKE_CASE						)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

				__magic_name__						:						List[Any]        =	generator.manual_seed(0						)
				__magic_name__						:						Tuple        =	pipe.dual_guided(
				    prompt="first prompt"		,  image=__SCREAMING_SNAKE_CASE		,  text_to_image_strength=0.75		,  generator=__SCREAMING_SNAKE_CASE		,  guidance_scale=7.5		,  num_inference_steps=2		,  output_type="numpy"		,  ).images

				assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"







			def      SCREAMING_SNAKE_CASE       (							self						):
				__magic_name__						:						List[str]        =	VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,  torch_dtype=torch.floataa						)
				pipe.to(__SCREAMING_SNAKE_CASE						)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

				__magic_name__						:						List[str]        =	"cyberpunk 2077"
				__magic_name__						:						List[Any]        =	load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
				__magic_name__						:						List[str]        =	torch.manual_seed(0						)
				__magic_name__						:						Tuple        =	pipe.dual_guided(
				    prompt=__SCREAMING_SNAKE_CASE		,  image=__SCREAMING_SNAKE_CASE		,  text_to_image_strength=0.75		,  generator=__SCREAMING_SNAKE_CASE		,  guidance_scale=7.5		,  num_inference_steps=50		,  output_type="numpy"		,  ).images

				__magic_name__						:						str        =	image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				__magic_name__						:						List[Any]        =	np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]						)

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

				__magic_name__						:						Union[str, Any]        =	"A painting of a squirrel eating a burger "
				__magic_name__						:						str        =	torch.manual_seed(0						)
				__magic_name__						:						Tuple        =	pipe.text_to_image(
				    prompt=__SCREAMING_SNAKE_CASE		,  generator=__SCREAMING_SNAKE_CASE		,  guidance_scale=7.5		,  num_inference_steps=50		,  output_type="numpy"						).images

				__magic_name__						:						List[Any]        =	image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				__magic_name__						:						Optional[int]        =	np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]						)

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

				__magic_name__						:						Any        =	pipe.image_variation(__SCREAMING_SNAKE_CASE		,  generator=__SCREAMING_SNAKE_CASE		,  output_type="numpy"						).images

				__magic_name__						:						str        =	image[0, 253:256, 253:256, -1]

				assert image.shape == (1, 512, 512, 3)
				__magic_name__						:						List[str]        =	np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]						)

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

# 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.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser



def 	__A		(      ):



 """simple docstring"""


 __a							=	ArgumentParser("Accelerate CLI tool"				,					usage="accelerate <command> [<args>]"				,					allow_abbrev=_A       )
 __a							=	parser.add_subparsers(help="accelerate command helpers"       )

 # Register commands
 get_config_parser(subparsers=_A       )
 env_command_parser(subparsers=_A       )
 launch_command_parser(subparsers=_A       )
 tpu_command_parser(subparsers=_A       )
 test_command_parser(subparsers=_A       )

 # Let's go
 __a							=	parser.parse_args()

 if not hasattr(_A				,					"func"       ):
  parser.print_help()
  exit(1       )

 # Run
 args.func(_A       )


if __name__ == "__main__":
  main()


