



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 		UpperCAmelCase__      (      unittest.TestCase       ):




			'''simple docstring'''






			def __init__(       self							:			Any	,	a_							:			Tuple	,	a_							:			List[Any]=7	,	a_							:			Union[str, Any]=3	,	a_							:			str=18	,	a_							:			Optional[int]=30	,	a_							:			Tuple=4_00	,	a_							:			Tuple=True	,	a_							:			Optional[Any]=None	,	a_							:			Tuple=True	,	a_							:			Dict=None	,	a_							:			Optional[int]=True	,	a_							:			Union[str, Any]=[0.5, 0.5, 0.5]	,	a_							:			int=[0.5, 0.5, 0.5]	,	):
					'''simple docstring'''





					__UpperCAmelCase   :					Dict													=     size if size is not None else {'''height''': 20, '''width''': 20}
					__UpperCAmelCase   :					Any													=     crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

					__UpperCAmelCase   :					Optional[int]													=     parent
					__UpperCAmelCase   :					str													=     batch_size
					__UpperCAmelCase   :					Any													=     num_channels
					__UpperCAmelCase   :					int													=     image_size
					__UpperCAmelCase   :					int													=     min_resolution
					__UpperCAmelCase   :					Optional[Any]													=     max_resolution
					__UpperCAmelCase   :					int													=     do_resize
					__UpperCAmelCase   :					Tuple													=     size
					__UpperCAmelCase   :					int													=     do_center_crop
					__UpperCAmelCase   :					Optional[int]													=     crop_size
					__UpperCAmelCase   :					List[str]													=     do_normalize
					__UpperCAmelCase   :					List[str]													=     image_mean
					__UpperCAmelCase   :					List[Any]													=     image_std




			def 			snake_case__					(       self							:			Optional[int]			):
					'''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 		UpperCAmelCase__      (      __UpperCamelCase						,unittest.TestCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	DeiTImageProcessor if is_vision_available() else None
			UpperCamelCase      						=	True




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     DeiTImageProcessingTester(self			)




			@property
			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					return self.image_processor_tester.prepare_image_processor_dict()




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     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 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class.from_dict(self.image_processor_dict			)
					self.assertEqual(image_processor.size	,	{'''height''': 20, '''width''': 20}			)
					self.assertEqual(image_processor.crop_size	,	{'''height''': 18, '''width''': 18}			)

					__UpperCAmelCase   :					Dict													=     self.image_processing_class.from_dict(self.image_processor_dict	,	size=42	,	crop_size=84			)
					self.assertEqual(image_processor.size	,	{'''height''': 42, '''width''': 42}			)
					self.assertEqual(image_processor.crop_size	,	{'''height''': 84, '''width''': 84}			)




			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					pass




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					__UpperCAmelCase   :					List[Any]													=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=a_			)
					for image in image_inputs:
							self.assertIsInstance(a_	,	Image.Image			)

					# Test not batched input
					__UpperCAmelCase   :					Any													=     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
					__UpperCAmelCase   :					Optional[Any]													=     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 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[str]													=     self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__UpperCAmelCase   :					Tuple													=     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
					__UpperCAmelCase   :					Dict													=     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
					__UpperCAmelCase   :					Dict													=     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 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__UpperCAmelCase   :					Optional[int]													=     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
					__UpperCAmelCase   :					List[Any]													=     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
					__UpperCAmelCase   :					str													=     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


__A             =logging.getLogger(__name__)

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     ArgumentParser()
		__UpperCAmelCase							, __UpperCAmelCase   :					Optional[int]													=     parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')						):
						parser.add_argument(arg.split('''='''						)[0]						)

		return parser.parse_args()

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					int													=     parse_args()
		__UpperCAmelCase   :					int													=     88_88
		__UpperCAmelCase   :					Union[str, Any]													=     int(os.environ['''SM_NUM_GPUS''']						)
		__UpperCAmelCase   :					Any													=     json.loads(os.environ['''SM_HOSTS''']						)
		__UpperCAmelCase   :					int													=     len(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase   :					Union[str, Any]													=     hosts.index(_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     '''INFO'''

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


if __name__ == "__main__":
				main()





