
"""simple docstring"""




# 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 subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__							=				'Run commands across TPU VMs for initial setup before running `accelerate launch`.'



def        _lowerCamelCase ( __a=None    ):
		if subparsers is not None:
				SCREAMING_SNAKE_CASE_						 = subparsers.add_parser('''tpu-config''',							description=_description    )
		else:
				SCREAMING_SNAKE_CASE_						 = argparse.ArgumentParser('''Accelerate tpu-config command''',							description=_description    )
		# Core arguments
		SCREAMING_SNAKE_CASE_						 = parser.add_argument_group(
		    '''Config Arguments''',							'''Arguments that can be configured through `accelerate config`.'''    )
		config_args.add_argument(
		    '''--config_file''',							type=__a,							default=__a,							help='''Path to the config file to use for accelerate.''',							)
		config_args.add_argument(
		    '''--tpu_name''',							default=__a,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.''',							)
		config_args.add_argument(
		    '''--tpu_zone''',							default=__a,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.''',							)
		SCREAMING_SNAKE_CASE_						 = parser.add_argument_group('''TPU Arguments''',							'''Arguments for options ran inside the TPU.'''    )
		pod_args.add_argument(
		    '''--use_alpha''',							action='''store_true''',							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.''',							)
		pod_args.add_argument(
		    '''--command_file''',							default=__a,							help='''The path to the file containing the commands to run on the pod on startup.''',							)
		pod_args.add_argument(
		    '''--command''',							action='''append''',							nargs='''+''',							help='''A command to run on the pod. Can be passed multiple times.''',							)
		pod_args.add_argument(
		    '''--install_accelerate''',							action='''store_true''',							help='''Whether to install accelerate on the pod. Defaults to False.''',							)
		pod_args.add_argument(
		    '''--accelerate_version''',							default='''latest''',							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.''',							)
		pod_args.add_argument(
		    '''--debug''',							action='''store_true''',							help='''If set, will print the command that would be run instead of running it.'''    )

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



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(__a    ):
				SCREAMING_SNAKE_CASE_						 = load_config_from_file(args.config_file    )
				if not args.command_file and defaults.command_file is not None and not args.command:
						SCREAMING_SNAKE_CASE_						 = defaults.command_file
				if not args.command and defaults.commands is not None:
						SCREAMING_SNAKE_CASE_						 = defaults.commands
				if not args.tpu_name:
						SCREAMING_SNAKE_CASE_						 = defaults.tpu_name
				if not args.tpu_zone:
						SCREAMING_SNAKE_CASE_						 = defaults.tpu_zone
		if args.accelerate_version == "dev":
				SCREAMING_SNAKE_CASE_						 = '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				SCREAMING_SNAKE_CASE_						 = '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version    ),							__a    ):
				SCREAMING_SNAKE_CASE_						 = F'accelerate=={args.accelerate_version}'

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''    )

		if args.command_file:
				with open(args.command_file,							'''r'''    ) as f:
						SCREAMING_SNAKE_CASE_						 = [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0],							__a    ):
				SCREAMING_SNAKE_CASE_						 = [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		SCREAMING_SNAKE_CASE_						 = ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [F'pip install {args.accelerate_version}']
		new_cmd += args.command
		SCREAMING_SNAKE_CASE_						 = '''; '''.join(__a    )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		SCREAMING_SNAKE_CASE_						 = ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F'Running {" ".join(__a    )}'    )
				return
		subprocess.run(__a    )
		print('''Successfully setup pod.'''    )



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = tpu_command_parser()
		SCREAMING_SNAKE_CASE_						 = parser.parse_args()

		tpu_command_launcher(__a    )

"""simple docstring"""




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


lowerCAmelCase__							=				False





class  snake_case     (							unittest.TestCase   ):
							pass







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

							def _lowercase					(self    ):

									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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_    )

									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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_    )
											SCREAMING_SNAKE_CASE_						 = VersatileDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_       ,       torch_dtype=torch.floataa    )
									pipe.to(SCREAMING_SNAKE_CASE_    )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = generator.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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_    )

									SCREAMING_SNAKE_CASE_						 = '''cyberpunk 2077'''
									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''    )
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = 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

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = 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

									SCREAMING_SNAKE_CASE_						 = '''A painting of a squirrel eating a burger '''
									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipe.text_to_image(
									    prompt=SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       guidance_scale=7.5       ,       num_inference_steps=50       ,       output_type='''numpy'''    ).images

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = 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

									SCREAMING_SNAKE_CASE_						 = pipe.image_variation(SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       output_type='''numpy'''    ).images

									SCREAMING_SNAKE_CASE_						 = image[0, 2_53:2_56, 2_53:2_56, -1]

									assert image.shape == (1, 5_12, 5_12, 3)
									SCREAMING_SNAKE_CASE_						 = 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