'''simple docstring'''


import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_pytesseract_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor






class 						A   (   unittest.TestCase   ):





							def __init__(      self				:							Optional[int]     ,		__a				:							Tuple     ,		__a				:							Optional[Any]=7     ,		__a				:							Tuple=3     ,		__a				:							Optional[Any]=1_8     ,		__a				:							int=3_0     ,		__a				:							List[Any]=4_0_0     ,		__a				:							Optional[int]=True     ,		__a				:							List[str]=None     ,		__a				:							Tuple=True     ,		)							->	Dict:
								__UpperCAmelCase							=			size if size is not None else {'''height''': 1_8, '''width''': 1_8}
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			batch_size
								__UpperCAmelCase							=			num_channels
								__UpperCAmelCase							=			image_size
								__UpperCAmelCase							=			min_resolution
								__UpperCAmelCase							=			max_resolution
								__UpperCAmelCase							=			do_resize
								__UpperCAmelCase							=			size
								__UpperCAmelCase							=			apply_ocr






							def       snake_case__						(      self				:							Any	)							->	List[str]:
								return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      LayoutLMvaImageProcessor if is_pytesseract_available() else None





							def       snake_case__						(      self				:							Union[str, Any]	)							->	int:
								__UpperCAmelCase							=			LayoutLMvaImageProcessingTester(self	)





							@property
							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[int]:
								return self.image_processor_tester.prepare_image_processor_dict()





							def       snake_case__						(      self				:							List[str]	)							->	Union[str, Any]:
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(__a     ,		'''do_resize'''	)	)
								self.assertTrue(hasattr(__a     ,		'''size'''	)	)
								self.assertTrue(hasattr(__a     ,		'''apply_ocr'''	)	)





							def       snake_case__						(      self				:							Dict	)							->	Optional[int]:
								__UpperCAmelCase							=			self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size     ,		{'''height''': 1_8, '''width''': 1_8}	)

								__UpperCAmelCase							=			self.image_processing_class.from_dict(self.image_processor_dict     ,		size=4_2	)
								self.assertEqual(image_processor.size     ,		{'''height''': 4_2, '''width''': 4_2}	)





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





							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								__UpperCAmelCase							=			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							=			image_processing(image_inputs[0]     ,		return_tensors='''pt'''	)
								self.assertEqual(
								    encoding.pixel_values.shape     ,		(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)

								self.assertIsInstance(encoding.words     ,		__a	)
								self.assertIsInstance(encoding.boxes     ,		__a	)

								# Test batched
								__UpperCAmelCase							=			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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)





							def       snake_case__						(      self				:							List[Any]	)							->	int:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								__UpperCAmelCase							=			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							=			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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)

								# Test batched
								__UpperCAmelCase							=			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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)





							def       snake_case__						(      self				:							List[str]	)							->	str:
								# Initialize image_processing
								__UpperCAmelCase							=			self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								__UpperCAmelCase							=			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							=			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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)

								# Test batched
								__UpperCAmelCase							=			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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    )     ,		)






							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								# with apply_OCR = True
								__UpperCAmelCase							=			LayoutLMvaImageProcessor()

								from datasets import load_dataset

								__UpperCAmelCase							=			load_dataset('''hf-internal-testing/fixtures_docvqa'''     ,		split='''test'''	)

								__UpperCAmelCase							=			Image.open(ds[0]['''file''']	).convert('''RGB'''	)

								__UpperCAmelCase							=			image_processing(__a     ,		return_tensors='''pt'''	)

								self.assertEqual(encoding.pixel_values.shape     ,		(1, 3, 2_2_4, 2_2_4)	)
								self.assertEqual(len(encoding.words	)     ,		len(encoding.boxes	)	)

								# fmt: off
								# the words and boxes were obtained with Tesseract 4.1.1
								__UpperCAmelCase							=			[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
								__UpperCAmelCase							=			[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
								# fmt: on

								self.assertListEqual(encoding.words     ,		__a	)
								self.assertListEqual(encoding.boxes     ,		__a	)

								# with apply_OCR = False
								__UpperCAmelCase							=			LayoutLMvaImageProcessor(apply_ocr=__a	)

								__UpperCAmelCase							=			image_processing(__a     ,		return_tensors='''pt'''	)

								self.assertEqual(encoding.pixel_values.shape     ,		(1, 3, 2_2_4, 2_2_4)	)

'''simple docstring'''


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__lowerCAmelCase	:       Optional[int]						       =	[
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Any     ,		__a				:							str     ,		__a				:							bool     ,		__a				:							str = None     ,		__a				:							list = None	)							->	Tuple:
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			os.path.abspath(os.path.join('''examples'''     ,		'''by_feature'''	)	)
								__UpperCAmelCase							=			os.path.abspath('''examples'''	)
								for item in os.listdir(__a	):
									if item not in EXCLUDE_EXAMPLES:
										__UpperCAmelCase							=			os.path.join(__a     ,		__a	)
										if os.path.isfile(__a	) and ".py" in item_path:
											with self.subTest(
											    tested_script=__a     ,		feature_script=__a     ,		tested_section='''main()''' if parser_only else '''training_function()'''     ,		):
												__UpperCAmelCase							=			compare_against_test(
												    os.path.join(__a     ,		__a	)     ,		__a     ,		__a     ,		__a	)
												__UpperCAmelCase							=			'''\n'''.join(__a	)
												if special_strings is not None:
													for string in special_strings:
														__UpperCAmelCase							=			diff.replace(__a     ,		''''''	)
												self.assertEqual(__a     ,		''''''	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	str:
								self.one_complete_example('''complete_nlp_example.py'''     ,		__a	)
								self.one_complete_example('''complete_nlp_example.py'''     ,		__a	)






							def       snake_case__						(      self				:							List[str]	)							->	Tuple:
								__UpperCAmelCase							=			os.path.abspath(os.path.join('''examples'''     ,		'''cv_example.py'''	)	)
								__UpperCAmelCase							=			[
								    ''' ''' * 1_6 + '''{\n\n''',
								    ''' ''' * 2_0 + '''"accuracy": eval_metric["accuracy"],\n\n''',
								    ''' ''' * 2_0 + '''"f1": eval_metric["f1"],\n\n''',
								    ''' ''' * 2_0 + '''"train_loss": total_loss.item() / len(train_dataloader),\n\n''',
								    ''' ''' * 2_0 + '''"epoch": epoch,\n\n''',
								    ''' ''' * 1_6 + '''},\n\n''',
								    ''' ''' * 1_6 + '''step=epoch,\n''',
								    ''' ''' * 1_2,
								    ''' ''' * 8 + '''for step, batch in enumerate(active_dataloader):\n''',
								]
								self.one_complete_example('''complete_cv_example.py'''     ,		__a     ,		__a     ,		__a	)
								self.one_complete_example('''complete_cv_example.py'''     ,		__a     ,		__a     ,		__a	)






@mock.patch.dict(os.environ       ,	{'''TESTING_MOCKED_DATALOADERS''': '''1'''}   )
class 						A   (   UpperCAmelCase   ):
							a_        =      False





							@classmethod
							def       snake_case__						(      cls				:							Tuple	)							->	str:
								super().setUpClass()
								__UpperCAmelCase							=			tempfile.mkdtemp()
								__UpperCAmelCase							=			os.path.join(cls._tmpdir     ,		'''default_config.yml'''	)

								write_basic_config(save_location=cls.configPath	)
								__UpperCAmelCase							=			['''accelerate''', '''launch''', '''--config_file''', cls.configPath]





							@classmethod
							def       snake_case__						(      cls				:							Dict	)							->	int:
								super().tearDownClass()
								shutil.rmtree(cls._tmpdir	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
								run_command(self._launch_args + testargs	)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir     ,		'''epoch_0'''	)	)	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs	)
								self.assertTrue(os.path.exists(os.path.join(self.tmpdir     ,		'''step_2'''	)	)	)





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir     ,		'epoch_0'	)}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
								self.assertNotIn('''epoch 0:'''     ,		__a	)
								self.assertIn('''epoch 1:'''     ,		__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
								__UpperCAmelCase							=			f"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir     ,		'step_2'	)}
        """.split()
								__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
								if torch.cuda.is_available():
									__UpperCAmelCase							=			torch.cuda.device_count()
								else:
									__UpperCAmelCase							=			1
								if num_processes > 1:
									self.assertNotIn('''epoch 0:'''     ,		__a	)
									self.assertIn('''epoch 1:'''     ,		__a	)
								else:
									self.assertIn('''epoch 0:'''     ,		__a	)
									self.assertIn('''epoch 1:'''     ,		__a	)





							@slow
							def       snake_case__						(      self				:							Any	)							->	Optional[Any]:
								__UpperCAmelCase							=			'''
        examples/by_feature/cross_validation.py
        --num_folds 2
        '''.split()
								with mock.patch.dict(os.environ     ,		{'''TESTING_MOCKED_DATALOADERS''': '''0'''}	):
									__UpperCAmelCase							=			run_command(self._launch_args + testargs     ,		return_stdout=__a	)
									__UpperCAmelCase							=			re.findall('''({.+})'''     ,		__a	)
									__UpperCAmelCase							=			[r for r in results if '''accuracy''' in r][-1]
									__UpperCAmelCase							=			ast.literal_eval(__a	)
									self.assertGreaterEqual(results['''accuracy''']     ,		0.7_5	)





							def       snake_case__						(      self				:							Dict	)							->	int:
								__UpperCAmelCase							=			['''examples/by_feature/multi_process_metrics.py''']
								run_command(self._launch_args + testargs	)





							@require_trackers
							@mock.patch.dict(os.environ     ,		{'''WANDB_MODE''': '''offline'''}	)
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[int]:
								with tempfile.TemporaryDirectory() as tmpdir:
									__UpperCAmelCase							=			f"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
									run_command(self._launch_args + testargs	)
									self.assertTrue(os.path.exists(os.path.join(__a     ,		'''tracking'''	)	)	)





							def       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
								__UpperCAmelCase							=			['''examples/by_feature/gradient_accumulation.py''']
								run_command(self._launch_args + testargs	)






							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								__UpperCAmelCase							=			['''examples/by_feature/local_sgd.py''']
								run_command(self._launch_args + testargs	)
