
"""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


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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]=None						):






							"""simple docstring"""



							if subparsers is not None:
														_lowerCAmelCase  	=			subparsers.add_parser('tpu-config'     ,  description=_description						)
							else:
														_lowerCAmelCase  	=			argparse.ArgumentParser('Accelerate tpu-config command'     ,  description=_description						)
							# Core arguments
							_lowerCAmelCase  	=			parser.add_argument_group(
							    'Config Arguments'     ,  'Arguments that can be configured through `accelerate config`.'						)
							config_args.add_argument(
							    '--config_file'     ,  type=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='Path to the config file to use for accelerate.'     ,  )
							config_args.add_argument(
							    '--tpu_name'     ,  default=SCREAMING_SNAKE_CASE     ,  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=SCREAMING_SNAKE_CASE     ,  help='The zone of the TPU to use. If not specified, will use the zone specified in the config file.'     ,  )
							_lowerCAmelCase  	=			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=SCREAMING_SNAKE_CASE     ,  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=SCREAMING_SNAKE_CASE						)
							return parser






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			None

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

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

							# Then send it to gcloud
							# Eventually try to use google-api-core to do this instead of subprocess
							_lowerCAmelCase  	=			['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(SCREAMING_SNAKE_CASE						)}"""						)
														return
							subprocess.run(SCREAMING_SNAKE_CASE						)
							print('Successfully setup pod.'						)






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			tpu_command_parser()
							_lowerCAmelCase  	=			parser.parse_args()

							tpu_command_launcher(SCREAMING_SNAKE_CASE						)








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]						):
														for y in range(image.shape[0]						):
																					_lowerCAmelCase  	=			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_lowerCAmelCase  	=			int(summation > 0						)
							return output


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






