




"""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 GLPNImageProcessor




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Any=7    ,   lowercase__					:	str=3    ,   lowercase__					:	Dict=1_8    ,   lowercase__					:	Optional[int]=3_0    ,   lowercase__					:	Any=4_0_0    ,   lowercase__					:	int=True    ,   lowercase__					:	Any=3_2    ,   lowercase__					:	Optional[Any]=True    ,   ):
						__lowercase				:		Optional[int]												=				parent
						__lowercase				:		int												=				batch_size
						__lowercase				:		Any												=				num_channels
						__lowercase				:		str												=				image_size
						__lowercase				:		str												=				min_resolution
						__lowercase				:		Optional[int]												=				max_resolution
						__lowercase				:		Any												=				do_resize
						__lowercase				:		Any												=				size_divisor
						__lowercase				:		List[Any]												=				do_rescale



				def       snake_case    (     self					:	str						):
						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}





@require_torch
@require_vision
class       lowerCAmelCase__       (			__lowerCamelCase  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     List[str]       =      GLPNImageProcessor if is_vision_available() else None



				def       snake_case    (     self					:	Any						):
						__lowercase				:		str												=				GLPNImageProcessingTester(self						)



				@property
				def       snake_case    (     self					:	List[Any]						):
						return self.image_processor_tester.prepare_image_processor_dict()



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		Optional[Any]												=				self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,   "do_resize"						)						)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,   "size_divisor"						)						)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,   "resample"						)						)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_    ,   "do_rescale"						)						)



				def       snake_case    (     self					:	Tuple						):
						pass



				def       snake_case    (     self					:	List[str]						):
						# Initialize image_processing
						__lowercase				:		Optional[Any]												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__lowercase				:		Dict												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=SCREAMING_SNAKE_CASE_						)
						for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,   Image.Image						)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__lowercase				:		Tuple												=				image_processing(image_inputs[0]    ,   return_tensors="pt"						).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)



				def       snake_case    (     self					:	Dict						):
						# Initialize image_processing
						__lowercase				:		Any												=				self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__lowercase				:		Union[str, Any]												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=SCREAMING_SNAKE_CASE_    ,   numpify=SCREAMING_SNAKE_CASE_						)
						for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,   np.ndarray						)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__lowercase				:		List[Any]												=				image_processing(image_inputs[0]    ,   return_tensors="pt"						).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)



				def       snake_case    (     self					:	Dict						):
						# Initialize image_processing
						__lowercase				:		Tuple												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__lowercase				:		str												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=SCREAMING_SNAKE_CASE_    ,   torchify=SCREAMING_SNAKE_CASE_						)
						for image in image_inputs:
								self.assertIsInstance(SCREAMING_SNAKE_CASE_    ,   torch.Tensor						)

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						__lowercase				:		Optional[Any]												=				image_processing(image_inputs[0]    ,   return_tensors="pt"						).pixel_values
						self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
						self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)






"""simple docstring"""







import argparse
from collections import defaultdict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[str]:






		"""simple docstring"""


		__lowercase				:		Tuple												=				F'{file}_{class_name}_{test_name}'
		done_test[_id] += 1

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		Union[str, Any]												=				f.readlines()

		__lowercase				:		str												=				F'class {class_name}('
		__lowercase				:		Union[str, Any]												=				F'{4 * " "}def {test_name}('
		__lowercase				:		List[Any]												=				F'{8 * " "}{correct_line.split()[0]}'
		__lowercase				:		Optional[int]												=				F'{16 * " "}{correct_line.split()[0]}'
		__lowercase				:		List[Any]												=				False
		__lowercase				:		int												=				False
		__lowercase				:		int												=				False
		__lowercase				:		List[Any]												=				False
		__lowercase				:		str												=				0
		__lowercase				:		Dict												=				0

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

						if count == done_test[_id]:
								__lowercase				:		Any												=				True

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

				if in_class and in_func and in_line and insert_line:
						new_lines.append(F'{spaces * " "}{correct_line}'				)
						__lowercase				:		List[Any]												=				False
				else:
						new_lines.append(_lowerCamelCase				)

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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=None				)			->List[Any]:






		"""simple docstring"""


		if fail is not None:
				with open(_lowerCamelCase,  "r"				) as f:
						__lowercase				:		str												=				{l.strip() for l in f.readlines()}
		else:
				__lowercase				:		List[Any]												=				None

		with open(_lowerCamelCase,  "r"				) as f:
				__lowercase				:		str												=				f.readlines()

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


if __name__ == "__main__":
						__A   :  str					=      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)
						__A   :  Union[str, Any]					=      parser.parse_args()

						main(args.correct_filename, args.fail_filename)
