
'''simple docstring'''





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     a (					unittest.TestCase     ):




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

										snake_case							:  Dict			=     torch.tensor(
										    [[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]],     device=SCREAMING_SNAKE_CASE_,     dtype=torch.long,     )

										with torch.no_grad():
																snake_case							:  Union[str, Any]			=     model(SCREAMING_SNAKE_CASE_   ).logits

										snake_case							:  Tuple			=     torch.Size((1, 9, 5_02_57)   )
										self.assertEqual(output.shape,     SCREAMING_SNAKE_CASE_   )

										snake_case							:  int			=     torch.tensor(
										    [
										        4.9414,
										        -0.2920,
										        -1.2148,
										        -4.0273,
										        -0.5161,
										        -5.2109,
										        -1.2412,
										        -1.8301,
										        -1.7734,
										        -4.7148,
										        -0.2317,
										        -1.0811,
										        -2.1777,
										        0.4141,
										        -3.7969,
										        -4.0586,
										        -2.5332,
										        -3.3809,
										        4.3867,
										    ],     device=SCREAMING_SNAKE_CASE_,     dtype=torch.half,     )

										for i in range(19   ):
																snake_case,       snake_case							:  str			=     8 * i // 17, 27_92 * i  # along the diagonal
																snake_case,       snake_case							:  int			=     output[0, r, c], expected_diag[i]
																snake_case							:  Any			=     F"""row={r} col={c} computed={computed} expected={expected}"""
																self.assertAlmostEqual(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     delta=1e-4,     msg=SCREAMING_SNAKE_CASE_   )



"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   try:
      return int(image[x_coordinate][y_coordinate] >= center    )
   except (IndexError, TypeError):
      return 0







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [1, 2, 4, 8, 16, 32, 64, 128]

   # skip get_neighbors_pixel if center is null
   if center is None:
      return 0

   # Starting from the top right, assigning value to pixels clockwise
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

   # Converting the binary value to decimal.
   return sum(
       binary_value * power for binary_value, power in zip(__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    _a									=				np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
            _a									=				local_binary_value(image, i, j)

    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()


