


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    int=3     ):






				'''simple docstring'''






				_lowerCAmelCase  						=int(mask / 2     )
				# copy image size
				_lowerCAmelCase  						=zeros_like(_lowercase     )
				for i in range(_lowercase       ,     gray_img.shape[0] - bd     ):
								for j in range(_lowercase       ,     gray_img.shape[1] - bd     ):
												# get mask according with mask
												_lowerCAmelCase  						=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]     )
												# calculate mask median
												_lowerCAmelCase  						=sort(_lowercase     )[inta(divide((multiply(_lowercase       ,     _lowercase     ))       ,     2     ) + 1     )]
												_lowerCAmelCase  						=median
				return median_img


if __name__ == "__main__":
		# read original image
		__SCREAMING_SNAKE_CASE	:							List[Any]           =  imread('''../image_data/lena.jpg''')
		# turn image in gray scale value
		__SCREAMING_SNAKE_CASE	:							Tuple           =  cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		__SCREAMING_SNAKE_CASE	:							Optional[int]           =  median_filter(gray, 3)
		__SCREAMING_SNAKE_CASE	:							List[Any]           =  median_filter(gray, 5)

		# show result images
		imshow('''median filter with 3x3 mask''', medianaxa)
		imshow('''median filter with 5x5 mask''', medianaxa)
		waitKey(0)


"""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        __SCREAMING_SNAKE_CASE     (   unittest.TestCase							):







				@slow
				@unittest.skip('''Model is not available.'''  )
				def   lowercase_		(    self    :Union[str, Any]  )      ->					Optional[int]:




									"""simple docstring"""



									lowerCamelCase__		:   Any  				=					'''nvidia/megatron-gpt2-345m/'''
									if "MYDIR" in os.environ:
														lowerCamelCase__		:   str  				=					os.path.join(os.environ['''MYDIR''']      ,__UpperCAmelCase  )
									lowerCamelCase__		:   Dict  				=					GPTaLMHeadModel.from_pretrained(__UpperCAmelCase  )
									model.to(__UpperCAmelCase  )
									model.half()

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

									with torch.no_grad():
														lowerCamelCase__		:   Tuple  				=					model(__UpperCAmelCase  ).logits

									lowerCamelCase__		:   List[str]  				=					torch.Size((1, 9, 5_02_57)  )
									self.assertEqual(output.shape      ,__UpperCAmelCase  )

									lowerCamelCase__		:   Tuple  				=					torch.tensor(
									    [
									        4.9_414,
									        -0.2_920,
									        -1.2_148,
									        -4.0_273,
									        -0.5_161,
									        -5.2_109,
									        -1.2_412,
									        -1.8_301,
									        -1.7_734,
									        -4.7_148,
									        -0.2_317,
									        -1.0_811,
									        -2.1_777,
									        0.4_141,
									        -3.7_969,
									        -4.0_586,
									        -2.5_332,
									        -3.3_809,
									        4.3_867,
									    ]      ,device=__UpperCAmelCase      ,dtype=torch.half      ,)

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






