


"""simple docstring"""







from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros


def 	lowercase     (							A_    ,  A_			)-> Optional[int]:
  '''simple docstring'''







  a		:						List[str] 				=     image.shape
  a		:						Dict 				=     cols - block_size[1] + 1
  a		:						List[Any] 				=     rows - block_size[0] + 1
  a		:						List[Any] 				=     zeros((dst_height * dst_width, block_size[1] * block_size[0])			)
  a		:						Any 				=     0
  for i in range(0    ,  __UpperCamelCase			):
    for j in range(0    ,  __UpperCamelCase			):
      a		:						Union[str, Any] 				=     ravel(image[i : i + block_size[0], j : j + block_size[1]]			)
      a		:						Optional[Any] 				=     window
      row += 1

  return image_array


def 	lowercase     (							A_    ,  A_			)-> Tuple:
  '''simple docstring'''







  a		:						Tuple 				=     image.shape[0], image.shape[1]
  a		:						List[Any] 				=     filter_kernel.shape[0]
  a		:						Optional[int] 				=     k_size // 2
  # Pads image with the edge values of array.
  a		:						Any 				=     pad(__UpperCamelCase    ,  __UpperCamelCase    ,  mode="edge"			)

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  a		:						str 				=     imacol(__UpperCamelCase    ,  (k_size, k_size)			)

  #  turn the kernel into shape(k*k, 1)
  a		:						int 				=     ravel(__UpperCamelCase			)
  # reshape and get the dst image
  a		:						Any 				=     dot(__UpperCamelCase    ,  __UpperCamelCase			).reshape(__UpperCamelCase    ,  __UpperCamelCase			)
  return dst


if __name__ == "__main__":
 # read original image
 __lowercase          = imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 __lowercase          = cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 __lowercase          = array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 __lowercase          = img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)








import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					import torch

					from transformers import GPTaLMHeadModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase__		(     unittest.TestCase ):







	'''simple docstring'''





	@slow
	@unittest.skip('''Model is not available.'''		)
	def 	__snake_case					(      self		):
					A__	:						int								=				'''nvidia/megatron-gpt2-345m/'''
					if "MYDIR" in os.environ:
									A__	:						Union[str, Any]								=				os.path.join(os.environ['''MYDIR''']					,							UpperCamelCase__		)
					A__	:						Union[str, Any]								=				GPTaLMHeadModel.from_pretrained(UpperCamelCase__		)
					model.to(UpperCamelCase__		)
					model.half()

					A__	:						Dict								=				torch.tensor(
					    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]					,							device=UpperCamelCase__					,							dtype=torch.long					,							)

					with torch.no_grad():
									A__	:						str								=				model(UpperCamelCase__		).logits

					A__	:						int								=				torch.Size((1, 9, 5_0257)		)
					self.assertEqual(output.shape					,							UpperCamelCase__		)

					A__	:						Dict								=				torch.tensor(
					    [
					        4.9_4_1_4,
					        -0.2_9_2_0,
					        -1.2_1_4_8,
					        -4.0_2_7_3,
					        -0.5_1_6_1,
					        -5.2_1_0_9,
					        -1.2_4_1_2,
					        -1.8_3_0_1,
					        -1.7_7_3_4,
					        -4.7_1_4_8,
					        -0.2_3_1_7,
					        -1.0_8_1_1,
					        -2.1_7_7_7,
					        0.4_1_4_1,
					        -3.7_9_6_9,
					        -4.0_5_8_6,
					        -2.5_3_3_2,
					        -3.3_8_0_9,
					        4.3_8_6_7,
					    ]					,							device=UpperCamelCase__					,							dtype=torch.half					,							)

					for i in range(19		):
									A__							,				A__	:						int								=				8 * i // 17, 2792 * i  # along the diagonal
									A__							,				A__	:						Optional[Any]								=				output[0, r, c], expected_diag[i]
									A__	:						Dict								=				F"row={r} col={c} computed={computed} expected={expected}"
									self.assertAlmostEqual(UpperCamelCase__					,							UpperCamelCase__					,							delta=1e-4					,							msg=UpperCamelCase__		)