






"""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    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=18 ,				snake_case_=30 ,				snake_case_=400 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=[0.5, 0.5, 0.5] ,				snake_case_=[0.5, 0.5, 0.5] ,				)		->       Optional[int]:
        UpperCamelCase__         =    size if 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_normalize
        UpperCamelCase__         =    image_mean
        UpperCamelCase__         =    image_std







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
        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    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=DPTImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
        UpperCamelCase__         =    DPTImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
        return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
        UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(snake_case_ ,				'image_mean'      )      )
        self.assertTrue(hasattr(snake_case_ ,				'image_std'      )      )
        self.assertTrue(hasattr(snake_case_ ,				'do_normalize'      )      )
        self.assertTrue(hasattr(snake_case_ ,				'do_resize'      )      )
        self.assertTrue(hasattr(snake_case_ ,				'size'      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
        UpperCamelCase__         =    self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size ,				{'height': 18, 'width': 18}      )

        UpperCamelCase__         =    self.image_processing_class.from_dict(self.image_processor_dict ,				size=42      )
        self.assertEqual(image_processor.size ,				{'height': 42, 'width': 42}      )

   def 						SCREAMING_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=snake_case_      )
        for image in image_inputs:
             self.assertIsInstance(snake_case_ ,				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.size['height'],
                self.image_processor_tester.size['width'],
            ) ,				)

        # Test batched
        UpperCamelCase__         =    image_processing(snake_case_ ,				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 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
        # 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=snake_case_ ,				numpify=snake_case_      )
        for image in image_inputs:
             self.assertIsInstance(snake_case_ ,				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(snake_case_ ,				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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[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=snake_case_ ,				torchify=snake_case_      )
        for image in image_inputs:
             self.assertIsInstance(snake_case_ ,				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(snake_case_ ,				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"""





from collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


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




