





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 MobileNetVaImageProcessor





class        _lowerCamelCase      (       unittest.TestCase ):


						def __init__(self   ,							__a   ,							__a=7   ,							__a=3   ,							__a=18   ,							__a=30   ,							__a=4_00   ,							__a=True   ,							__a=None   ,							__a=True   ,							__a=None   ,							) ->   Tuple:
								UpperCamelCase   =	size if size is not None else {"shortest_edge": 20}
								UpperCamelCase   =	crop_size if crop_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_center_crop
								UpperCamelCase   =	crop_size




						def     snake_case_   (self		) ->   List[str]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}





@require_torch
@require_vision
class        _lowerCamelCase      (       __lowercase  ,			unittest.TestCase ):
						UpperCAmelCase_ =			MobileNetVaImageProcessor if is_vision_available() else None


						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	MobileNetVaImageProcessingTester(self		)


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


						def     snake_case_   (self		) ->   int:
								UpperCamelCase   =	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   ,							"crop_size"		)		)


						def     snake_case_   (self		) ->   str:
								UpperCamelCase   =	self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size   ,							{"shortest_edge": 20}		)
								self.assertEqual(image_processor.crop_size   ,							{"height": 18, "width": 18}		)

								UpperCamelCase   =	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     snake_case_   (self		) ->   Optional[int]:
								pass


						def     snake_case_   (self		) ->   int:
								# 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"		).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   =	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		) ->   Union[str, Any]:
								# 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.crop_size["height"],
								        self.image_processor_tester.crop_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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)




						def     snake_case_   (self		) ->   List[Any]:
								# 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.crop_size["height"],
								        self.image_processor_tester.crop_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.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )   ,							)













"""simple docstring"""





import argparse
from collections import defaultdict


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	F"{file}_{class_name}_{test_name}"
		done_test[_id] += 1

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	F"class {class_name}("
		UpperCamelCase   =	F"{4 * ' '}def {test_name}("
		UpperCamelCase   =	F"{8 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	F"{16 * ' '}{correct_line.split()[0]}"
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	False
		UpperCamelCase   =	0
		UpperCamelCase   =	0

		UpperCamelCase   =	[]
		for line in lines:
				if line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and line.startswith(_SCREAMING_SNAKE_CASE    ):
						UpperCamelCase   =	True
				elif in_class and in_func and (line.startswith(_SCREAMING_SNAKE_CASE    ) or line.startswith(_SCREAMING_SNAKE_CASE    )):
						UpperCamelCase   =	len(line.split(correct_line.split()[0]    )[0]    )
						count += 1

						if count == done_test[_id]:
								UpperCamelCase   =	True

				if in_class and in_func and in_line:
						if ")" not in line:
								continue
						else:
								UpperCamelCase   =	True

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F"{spaces * ' '}{correct_line}"    )
						UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	UpperCamelCase   =	False
				else:
						new_lines.append(_SCREAMING_SNAKE_CASE    )

		with open(_SCREAMING_SNAKE_CASE  ,	"w"    ) as f:
				for line in new_lines:
						f.write(_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None    ):




		"""simple docstring"""


		if fail is not None:
				with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
						UpperCamelCase   =	{l.strip() for l in f.readlines()}
		else:
				UpperCamelCase   =	None

		with open(_SCREAMING_SNAKE_CASE  ,	"r"    ) as f:
				UpperCamelCase   =	f.readlines()

		UpperCamelCase   =	defaultdict(_SCREAMING_SNAKE_CASE    )
		for line in correct_lines:
				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	line.split(";"    )
				if test_failures is None or "::".join([file, class_name, test_name]    ) in test_failures:
						overwrite_file(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						parser.add_argument('''--correct_filename''', help='''filename of tests with expected result''')
						parser.add_argument('''--fail_filename''', help='''filename of test failures''', type=str, default=None)
						lowerCAmelCase__     			=			parser.parse_args()

						main(args.correct_filename, args.fail_filename)






