

"""simple docstring"""


import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor





class    snake_case     (    unittest.TestCase  ):

				def __init__(     self							:    Any					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    List[str]=7					,  UpperCamelCase__							:    Union[str, Any]=3					,  UpperCamelCase__							:    Union[str, Any]=1_8					,  UpperCamelCase__							:    Optional[int]=3_0					,  UpperCamelCase__							:    int=4_0_0					,  UpperCamelCase__							:    List[Any]=True					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    Optional[int]=True					,  UpperCamelCase__							:    Union[str, Any]=[0.5, 0.5, 0.5]					,  UpperCamelCase__							:    List[Any]=[0.5, 0.5, 0.5]					,  )->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      size if size is not None else {"height": 1_8, "width": 1_8}
						__lowerCAmelCase:	List[Any]   				=      parent
						__lowerCAmelCase:	List[Any]   				=      batch_size
						__lowerCAmelCase:	Optional[int]   				=      num_channels
						__lowerCAmelCase:	Optional[int]   				=      image_size
						__lowerCAmelCase:	List[Any]   				=      min_resolution
						__lowerCAmelCase:	List[Any]   				=      max_resolution
						__lowerCAmelCase:	str   				=      do_resize
						__lowerCAmelCase:	Union[str, Any]   				=      size
						__lowerCAmelCase:	Tuple   				=      do_normalize
						__lowerCAmelCase:	int   				=      image_mean
						__lowerCAmelCase:	Optional[int]   				=      image_std




				def 							lowercase_					(     self							:    int)->       str:



						'''simple docstring'''


						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						}





@require_torch
@require_vision
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Tuple       				=  DPTImageProcessor if is_vision_available() else None

				def 							lowercase_					(     self							:    Any)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      DPTImageProcessingTester(self)

				@property
				def 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 							lowercase_					(     self							:    Tuple)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_mean"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "image_std"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "do_resize"))
						self.assertTrue(hasattr(UpperCamelCase__					,  "size"))

				def 							lowercase_					(     self							:    Any)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size					,  {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:	Tuple   				=      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 							lowercase_					(     self							:    str)->       List[Any]:



						'''simple docstring'''


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

						# Test not batched input
						__lowerCAmelCase:	Tuple   				=      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
						__lowerCAmelCase:	str   				=      image_processing(UpperCamelCase__					,  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 							lowercase_					(     self							:    Tuple)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__lowerCAmelCase:	List[Any]   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  numpify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  np.ndarray)

						# Test not batched input
						__lowerCAmelCase:	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.size["height"],
						        self.image_processor_tester.size["width"],
						    )					,  )

						# Test batched
						__lowerCAmelCase:	int   				=      image_processing(UpperCamelCase__					,  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 							lowercase_					(     self							:    Optional[int])->       Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__lowerCAmelCase:	Optional[Any]   				=      prepare_image_inputs(self.image_processor_tester					,  equal_resolution=UpperCamelCase__					,  torchify=UpperCamelCase__)
						for image in image_inputs:
								self.assertIsInstance(UpperCamelCase__					,  torch.Tensor)

						# Test not batched input
						__lowerCAmelCase:	int   				=      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
						__lowerCAmelCase:	Optional[Any]   				=      image_processing(UpperCamelCase__					,  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"],
						    )					,  )





"""simple docstring"""


import math
import sys






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if number != int(__SCREAMING_SNAKE_CASE			):
				raise ValueError("the value of input must be a natural number"			)
		if number < 0:
				raise ValueError("the value of input must not be a negative number"			)
		if number == 0:
				return 1
		__lowerCAmelCase:	int   				=      [-1] * (number + 1)
		__lowerCAmelCase:	Optional[Any]   				=      0
		for i in range(1 ,			number + 1			):
				__lowerCAmelCase:	List[str]   				=      sys.maxsize
				__lowerCAmelCase:	int   				=      int(math.sqrt(__SCREAMING_SNAKE_CASE			)			)
				for j in range(1 ,			root + 1			):
						__lowerCAmelCase:	Optional[Any]   				=      1 + answers[i - (j**2)]
						__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[Any]   				=      answer
		return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


