




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 DeiTImageProcessor
class      a__       (   unittest.TestCase ):






						def __init__(  self						,     _A						,     _A=7						,     _A=3						,     _A=1_8						,     _A=3_0						,     _A=4_0_0						,     _A=True						,     _A=None						,     _A=True						,     _A=None						,     _A=True						,     _A=[0.5, 0.5, 0.5]						,     _A=[0.5, 0.5, 0.5]						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			size if size is not None else {"height": 2_0, "width": 2_0}
											__lowerCAmelCase							  =			crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

											__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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "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 ):
						_a				:       Optional[int]   =       DeiTImageProcessor if is_vision_available() else None
						_a				:       List[Any]   =       True






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			DeiTImageProcessingTester(self     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(_A						,     "do_resize"     )     )
											self.assertTrue(hasattr(_A						,     "size"     )     )
											self.assertTrue(hasattr(_A						,     "do_center_crop"     )     )
											self.assertTrue(hasattr(_A						,     "center_crop"     )     )
											self.assertTrue(hasattr(_A						,     "do_normalize"     )     )
											self.assertTrue(hasattr(_A						,     "image_mean"     )     )
											self.assertTrue(hasattr(_A						,     "image_std"     )     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,     {"height": 2_0, "width": 2_0}     )
											self.assertEqual(image_processor.crop_size						,     {"height": 1_8, "width": 1_8}     )

											__lowerCAmelCase							  =			self.image_processing_class.from_dict(self.image_processor_dict						,     size=4_2						,     crop_size=8_4     )
											self.assertEqual(image_processor.size						,     {"height": 4_2, "width": 4_2}     )
											self.assertEqual(image_processor.crop_size						,     {"height": 8_4, "width": 8_4}     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											pass






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											__lowerCAmelCase							  =			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_A     )
											for image in image_inputs:
																self.assertIsInstance(_A						,     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(_A						,     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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											__lowerCAmelCase							  =			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_A						,     numpify=_A     )
											for image in image_inputs:
																self.assertIsInstance(_A						,     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(_A						,     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 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											__lowerCAmelCase							  =			prepare_image_inputs(self.image_processor_tester						,     equal_resolution=_A						,     torchify=_A     )
											for image in image_inputs:
																self.assertIsInstance(_A						,     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(_A						,     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"],
											    )						,     )










import json
import logging
import os
import subprocess
from argparse import ArgumentParser


UpperCamelCase__            =     logging.getLogger(__name__)







def       _a (		):
					__lowerCAmelCase							  =			ArgumentParser()
					__lowerCAmelCase  ,				__lowerCAmelCase							  =			parser.parse_known_args()
					for arg in unknown:
										if arg.startswith(("-", "--")	):
															parser.add_argument(arg.split("="	)[0]	)

					return parser.parse_args()







def       _a (		):
					__lowerCAmelCase							  =			parse_args()
					__lowerCAmelCase							  =			88_88
					__lowerCAmelCase							  =			int(os.environ["SM_NUM_GPUS"]	)
					__lowerCAmelCase							  =			json.loads(os.environ["SM_HOSTS"]	)
					__lowerCAmelCase							  =			len(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			os.environ["SM_CURRENT_HOST"]
					__lowerCAmelCase							  =			hosts.index(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			"INFO"

					if num_nodes > 1:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					else:
										__lowerCAmelCase							  =			F"""python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]	)}"""
					try:
										subprocess.run(SCREAMING_SNAKE_CASE_ , shell=SCREAMING_SNAKE_CASE_	)
					except Exception as e:
										logger.info(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					main()




