



'''simple docstring'''

from collections import defaultdict
from math import ceil, sqrt






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						int = 1_000_000							,		_SCREAMING_SNAKE_CASE					:						int = 10							)      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     defaultdict(_SCREAMING_SNAKE_CASE							)

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




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)