


"""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 PoolFormerImageProcessor





class    __A   (unittest.TestCase):





							'''simple docstring'''

							def __init__( self							:					int						,				UpperCAmelCase_							:					Any						,				UpperCAmelCase_							:					Dict=7						,				UpperCAmelCase_							:					Tuple=3						,				UpperCAmelCase_							:					str=30						,				UpperCAmelCase_							:					Union[str, Any]=400						,				UpperCAmelCase_							:					Union[str, Any]=True						,				UpperCAmelCase_							:					List[str]=None						,				UpperCAmelCase_							:					Any=0.9						,				UpperCAmelCase_							:					int=None						,				UpperCAmelCase_							:					Dict=True						,				UpperCAmelCase_							:					str=[0.5, 0.5, 0.5]						,				UpperCAmelCase_							:					Optional[int]=[0.5, 0.5, 0.5]						,				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						size if size is not None else {"""shortest_edge""": 30}
														snake_case_							=						crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
														snake_case_							=						parent
														snake_case_							=						batch_size
														snake_case_							=						num_channels
														snake_case_							=						min_resolution
														snake_case_							=						max_resolution
														snake_case_							=						do_resize_and_center_crop
														snake_case_							=						size
														snake_case_							=						crop_pct
														snake_case_							=						crop_size
														snake_case_							=						do_normalize
														snake_case_							=						image_mean
														snake_case_							=						image_std



							def 						lowerCAmelCase				( self							:					int				)    ->Tuple:



														"""simple docstring"""



														return {
														    "size": self.size,
														    "do_resize_and_center_crop": self.do_resize_and_center_crop,
														    "crop_pct": self.crop_pct,
														    "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   (_lowerCamelCase  ,						unittest.TestCase):





							'''simple docstring'''

							__lowercase:						Union[str, Any]												=			PoolFormerImageProcessor if is_vision_available() else None



							def 						lowerCAmelCase				( self							:					Dict				)    ->str:



														"""simple docstring"""



														snake_case_							=						PoolFormerImageProcessingTester(self				)



							@property
							def 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->Tuple:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 						lowerCAmelCase				( self							:					Optional[int]				)    ->int:



														"""simple docstring"""



														snake_case_							=						self.image_processing_class(**self.image_processor_dict				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""do_resize_and_center_crop"""				)				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""size"""				)				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""crop_pct"""				)				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""do_normalize"""				)				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""image_mean"""				)				)
														self.assertTrue(hasattr(lowerCAmelCase_						,				"""image_std"""				)				)



							def 						lowerCAmelCase				( self							:					Optional[int]				)    ->List[str]:



														"""simple docstring"""



														snake_case_							=						self.image_processing_class.from_dict(self.image_processor_dict				)
														self.assertEqual(image_processor.size						,				{"""shortest_edge""": 30}				)
														self.assertEqual(image_processor.crop_size						,				{"""height""": 30, """width""": 30}				)

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



														"""simple docstring"""



														pass



							def 						lowerCAmelCase				( self							:					Optional[Any]				)    ->Union[str, Any]:



														"""simple docstring"""



														snake_case_							=						self.image_processing_class(**self.image_processor_dict				)
														# create random PIL images
														snake_case_							=						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCAmelCase_				)
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_						,				Image.Image				)

														# Test not batched input
														snake_case_							=						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
														snake_case_							=						image_processing(lowerCAmelCase_						,				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 						lowerCAmelCase				( self							:					Union[str, Any]				)    ->Any:



														"""simple docstring"""



														snake_case_							=						self.image_processing_class(**self.image_processor_dict				)
														# create random numpy tensors
														snake_case_							=						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCAmelCase_						,				numpify=lowerCAmelCase_				)
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_						,				np.ndarray				)

														# Test not batched input
														snake_case_							=						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
														snake_case_							=						image_processing(lowerCAmelCase_						,				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 						lowerCAmelCase				( self							:					Any				)    ->Dict:



														"""simple docstring"""



														snake_case_							=						self.image_processing_class(**self.image_processor_dict				)
														# create random PyTorch tensors
														snake_case_							=						prepare_image_inputs(self.image_processor_tester						,				equal_resolution=lowerCAmelCase_						,				torchify=lowerCAmelCase_				)
														for image in image_inputs:
																					self.assertIsInstance(lowerCAmelCase_						,				torch.Tensor				)

														# Test not batched input
														snake_case_							=						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
														snake_case_							=						image_processing(lowerCAmelCase_						,				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'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_000_000	)       ->	int:
	_snake_case   						=		limit + 1
	_snake_case   						=		[0] * limit
	for first_term in range(1			,				__A	):
		for n in range(__A			,				__A			,				__A	):
			_snake_case   						=		first_term + n / first_term
			if common_difference % 4:  # d must be divisble by 4
				continue
			else:
				common_difference /= 4
				if (
				    first_term > common_difference
				    and first_term < 4 * common_difference
				):  # since x,y,z are positive integers
					frequency[n] += 1  # so z>0 and a>d ,also 4d<a

	_snake_case   						=		sum(1 for x in frequency[1:limit] if x == 10	)

	return count


if __name__ == "__main__":
					print(F'''{solution() = }''')

