

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__							(	SCREAMING_SNAKE_CASE_:							Any=None      )			->      Dict:




				'''simple docstring'''





				if subparsers is not None:
								A__        =			subparsers.add_parser("tpu-config"	,       description=_description      )
				else:
								A__        =			argparse.ArgumentParser("Accelerate tpu-config command"	,       description=_description      )
				# Core arguments
				A__        =			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."	,       )
				A__        =			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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      Dict:




				'''simple docstring'''





				A__        =			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_      ):
								A__        =			load_config_from_file(args.config_file      )
								if not args.command_file and defaults.command_file is not None and not args.command:
												A__        =			defaults.command_file
								if not args.command and defaults.commands is not None:
												A__        =			defaults.commands
								if not args.tpu_name:
												A__        =			defaults.tpu_name
								if not args.tpu_zone:
												A__        =			defaults.tpu_zone
				if args.accelerate_version == "dev":
								A__        =			"git+https://github.com/huggingface/accelerate.git"
				elif args.accelerate_version == "latest":
								A__        =			"accelerate -U"
				elif isinstance(parse(args.accelerate_version      )	,       SCREAMING_SNAKE_CASE_      ):
								A__        =			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:
												A__        =			[f.read().splitlines()]

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

				# Then send it to gcloud
				# Eventually try to use google-api-core to do this instead of subprocess
				A__        =			["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 					lowerCAmelCase__							(	)			->      int:




				'''simple docstring'''





				A__        =			tpu_command_parser()
				A__        =			parser.parse_args()

				tpu_command_launcher(SCREAMING_SNAKE_CASE_      )






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 PoolFormerImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=0.9      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      )  ->						str:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 30}
							A__        =			crop_size if crop_size is not None else {"height": 30, "width": 30}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize_and_center_crop
							A__        =			size
							A__        =			crop_pct
							A__        =			crop_size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       PoolFormerImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							A__        =			PoolFormerImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize_and_center_crop"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )
							self.assertTrue(hasattr(lowercase      ,      "crop_pct"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}       )
							self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}       )

							A__        =			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       )  ->						Optional[int]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

							# Test not batched input
							A__        =			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
							A__        =			image_processing(lowercase      ,      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"],
							    )      ,      )

