



"""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 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:List[str]       ,  lowercase_			:List[Any]=13       ,  lowercase_			:Dict=3       ,  lowercase_			:List[Any]=2_24       ,  lowercase_			:Any=30       ,  lowercase_			:Optional[Any]=4_00       ,  lowercase_			:Union[str, Any]=True       ,  lowercase_			:Tuple=None       ,  lowercase_			:Optional[Any]=True       ,  lowercase_			:Dict=[0.5, 0.5, 0.5]       ,  lowercase_			:str=[0.5, 0.5, 0.5]       ,  )    ->      Optional[Any]:
												UpperCAmelCase					=			size if size is not None else {'height': 18, 'width': 18}
												UpperCAmelCase					=			parent
												UpperCAmelCase					=			batch_size
												UpperCAmelCase					=			num_channels
												UpperCAmelCase					=			image_size
												UpperCAmelCase					=			min_resolution
												UpperCAmelCase					=			max_resolution
												UpperCAmelCase					=			do_resize
												UpperCAmelCase					=			size
												UpperCAmelCase					=			do_normalize
												UpperCAmelCase					=			image_mean
												UpperCAmelCase					=			image_std






						def      UpperCAmelCase__					( self			:List[str] )    ->      List[str]:
												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 				A_      (					SCREAMING_SNAKE_CASE_     ,       unittest.TestCase    ):



						"""simple docstring"""
						__UpperCamelCase		    =							ViTImageProcessor if is_vision_available() else None






						def      UpperCAmelCase__					( self			:str )    ->      Dict:
												UpperCAmelCase					=			EfficientFormerImageProcessorTester(self )






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      List[str]:
												return self.image_proc_tester.prepare_image_processor_dict()






						def      UpperCAmelCase__					( self			:str )    ->      Tuple:
												UpperCAmelCase					=			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       ,  'size' ) )






						def      UpperCAmelCase__					( self			:Dict )    ->      Dict:
												pass






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

												# Test not batched input
												UpperCAmelCase					=			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
												UpperCAmelCase					=			image_processor(_lowerCAmelCase       ,  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			:Dict )    ->      Tuple:
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												UpperCAmelCase					=			prepare_image_inputs(self.image_proc_tester       ,  equal_resolution=_lowerCAmelCase       ,  numpify=_lowerCAmelCase )
												for image in image_inputs:
																		self.assertIsInstance(_lowerCAmelCase       ,  np.ndarray )

												# Test not batched input
												UpperCAmelCase					=			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
												UpperCAmelCase					=			image_processor(_lowerCAmelCase       ,  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			:Union[str, Any] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												UpperCAmelCase					=			prepare_image_inputs(self.image_proc_tester       ,  equal_resolution=_lowerCAmelCase       ,  torchify=_lowerCAmelCase )
												for image in image_inputs:
																		self.assertIsInstance(_lowerCAmelCase       ,  torch.Tensor )

												# Test not batched input
												UpperCAmelCase					=			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
												UpperCAmelCase					=			image_processor(_lowerCAmelCase       ,  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"""


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			ArgumentParser(
						    description=(
						        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
						    )						)

						# Optional arguments for the launch helper
						parser.add_argument('--num_cores'		,     type=lowercase_		,     default=1		,     help='Number of TPU cores to use (1 or 8).'						)

						# positional
						parser.add_argument(
						    'training_script'		,     type=lowercase_		,     help=(
						        'The full path to the single TPU training '
						        'program/script to be launched in parallel, '
						        'followed by all the arguments for the '
						        'training script'
						    )		,     )

						# rest from the training program
						parser.add_argument('training_script_args'		,     nargs=lowercase_						)

						return parser.parse_args()

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			parse_args()

						# Import training_script as a module.
						UpperCAmelCase					=			Path(args.training_script						)
						sys.path.append(str(script_fpath.parent.resolve()						)						)
						UpperCAmelCase					=			script_fpath.stem
						UpperCAmelCase					=			importlib.import_module(lowercase_						)

						# Patch sys.argv
						UpperCAmelCase					=			[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores						)]

						xmp.spawn(mod._mp_fn		,     args=()		,     nprocs=args.num_cores						)


if __name__ == "__main__":
				main()





