


"""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 ViTImageProcessor




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				Union[str, Any]=1_3			,			UpperCAmelCase__			:				Any=3			,			UpperCAmelCase__			:				Tuple=2_2_4			,			UpperCAmelCase__			:				List[Any]=3_0			,			UpperCAmelCase__			:				Tuple=4_0_0			,			UpperCAmelCase__			:				Optional[int]=True			,			UpperCAmelCase__			:				Optional[Any]=None			,			UpperCAmelCase__			:				str=True			,			UpperCAmelCase__			:				Any=[0.5, 0.5, 0.5]			,			UpperCAmelCase__			:				Optional[int]=[0.5, 0.5, 0.5]			,			)       ->	str:
						__SCREAMING_SNAKE_CASE						   =				size if size is not None else {"height": 1_8, "width": 1_8}
						__SCREAMING_SNAKE_CASE						   =				parent
						__SCREAMING_SNAKE_CASE						   =				batch_size
						__SCREAMING_SNAKE_CASE						   =				num_channels
						__SCREAMING_SNAKE_CASE						   =				image_size
						__SCREAMING_SNAKE_CASE						   =				min_resolution
						__SCREAMING_SNAKE_CASE						   =				max_resolution
						__SCREAMING_SNAKE_CASE						   =				do_resize
						__SCREAMING_SNAKE_CASE						   =				size
						__SCREAMING_SNAKE_CASE						   =				do_normalize
						__SCREAMING_SNAKE_CASE						   =				image_mean
						__SCREAMING_SNAKE_CASE						   =				image_std




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Any:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}




@require_torch
@require_vision
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  List[str]    					=			ViTImageProcessor if is_vision_available() else None




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				EfficientFormerImageProcessorTester(self   )




	@property
	def   UpperCAmelCase_      ( self			:				Any   )       ->	Tuple:
						return self.image_proc_tester.prepare_image_processor_dict()




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				self.image_processing_class(**self.image_processor_dict   )
						self.assertTrue(hasattr(UpperCAmelCase__			,			"image_mean"   )   )
						self.assertTrue(hasattr(UpperCAmelCase__			,			"image_std"   )   )
						self.assertTrue(hasattr(UpperCAmelCase__			,			"do_normalize"   )   )
						self.assertTrue(hasattr(UpperCAmelCase__			,			"do_resize"   )   )
						self.assertTrue(hasattr(UpperCAmelCase__			,			"size"   )   )




	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[Any]:
						pass




	def   UpperCAmelCase_      ( self			:				int   )       ->	Optional[int]:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE						   =				self.image_processing_class(**self.image_processor_dict   )
						# create random PIL images
						__SCREAMING_SNAKE_CASE						   =				prepare_image_inputs(self.image_proc_tester			,			equal_resolution=UpperCAmelCase__   )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__			,			Image.Image   )

						# Test not batched input
						__SCREAMING_SNAKE_CASE						   =				image_processor(image_inputs[0]			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)

						# Test batched
						__SCREAMING_SNAKE_CASE						   =				image_processor(UpperCAmelCase__			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Any:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE						   =				self.image_processing_class(**self.image_processor_dict   )
						# create random numpy tensors
						__SCREAMING_SNAKE_CASE						   =				prepare_image_inputs(self.image_proc_tester			,			equal_resolution=UpperCAmelCase__			,			numpify=UpperCAmelCase__   )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__			,			np.ndarray   )

						# Test not batched input
						__SCREAMING_SNAKE_CASE						   =				image_processor(image_inputs[0]			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)

						# Test batched
						__SCREAMING_SNAKE_CASE						   =				image_processor(UpperCAmelCase__			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)




	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Union[str, Any]:
						# Initialize image_processor
						__SCREAMING_SNAKE_CASE						   =				self.image_processing_class(**self.image_processor_dict   )
						# create random PyTorch tensors
						__SCREAMING_SNAKE_CASE						   =				prepare_image_inputs(self.image_proc_tester			,			equal_resolution=UpperCAmelCase__			,			torchify=UpperCAmelCase__   )
						for image in image_inputs:
											self.assertIsInstance(UpperCAmelCase__			,			torch.Tensor   )

						# Test not batched input
						__SCREAMING_SNAKE_CASE						   =				image_processor(image_inputs[0]			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        1,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)

						# Test batched
						__SCREAMING_SNAKE_CASE						   =				image_processor(UpperCAmelCase__			,			return_tensors="pt"   ).pixel_values
						self.assertEqual(
						    encoded_images.shape			,			(
						        self.image_proc_tester.batch_size,
						        self.image_proc_tester.num_channels,
						        self.image_proc_tester.size["height"],
						        self.image_proc_tester.size["width"],
						    )			,			)




"""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


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




def    UpperCAmelCase__       (lowerCAmelCase_=None   ):




					'''simple docstring'''


					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=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."  ,						)
					__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=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    UpperCAmelCase__       (lowerCAmelCase_   ):




					'''simple docstring'''


					__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(lowerCAmelCase_   ):
										__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   )  ,						lowerCAmelCase_   ):
										__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]  ,						lowerCAmelCase_   ):
										__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(lowerCAmelCase_   )

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




def    UpperCAmelCase__       ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tpu_command_parser()
					__SCREAMING_SNAKE_CASE						   =				parser.parse_args()

					tpu_command_launcher(lowerCAmelCase_   )
