






'''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


UpperCamelCase    :      int  			=		'Run commands across TPU VMs for initial setup before running `accelerate launch`.'
def 						A__			(				__lowerCAmelCase		:			List[str]=None						):
						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=__lowerCAmelCase   ,    default=__lowerCAmelCase   ,    help="""Path to the config file to use for accelerate."""   ,    )
						config_args.add_argument(
						    """--tpu_name"""   ,    default=__lowerCAmelCase   ,    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=__lowerCAmelCase   ,    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=__lowerCAmelCase   ,    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=__lowerCAmelCase						)
						return parser
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						lowerCamelCase__         =  None

						# Get the default from the config file if it exists.
						if args.config_file is not None or os.path.isfile(__lowerCAmelCase						):
												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						)   ,    __lowerCAmelCase						):
												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]   ,    __lowerCAmelCase						):
												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(__lowerCAmelCase						)

						# 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(__lowerCAmelCase						)}'''						)
												return
						subprocess.run(__lowerCAmelCase						)
						print("""Successfully setup pod."""						)





def 						A__			(				):
						lowerCamelCase__         =  tpu_command_parser()
						lowerCamelCase__         =  parser.parse_args()

						tpu_command_launcher(__lowerCAmelCase						)









'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,):
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  min_resolution
								lowerCamelCase__         =  max_resolution
								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean
								lowerCamelCase__         =  image_std






		def 			UpperCamelCase_					(      self       ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					LevitImageProcessor if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  LevitImageProcessingTester(self       )






		@property
		def 			UpperCamelCase_					(      self       ):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_mean"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_std"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_normalize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_resize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_center_crop"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""size"""       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 18}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 18, """width""": 18}       )

								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict   ,size=42   ,crop_size=84       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 42}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 84, """width""": 84}       )






		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,Image.Image       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,numpify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,np.ndarray       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,torchify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,torch.Tensor       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

