





'''simple docstring'''

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


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=3  ):
		__UpperCAmelCase	:							str     =     int(mask / 2  )
		# copy image size
		__UpperCAmelCase	:							Dict     =     zeros_like(_UpperCAmelCase  )
		for i in range(_UpperCAmelCase,       gray_img.shape[0] - bd  ):
				for j in range(_UpperCAmelCase,       gray_img.shape[1] - bd  ):
						# get mask according with mask
						__UpperCAmelCase	:							List[str]     =     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]  )
						# calculate mask median
						__UpperCAmelCase	:							int     =     sort(_UpperCAmelCase  )[inta(divide((multiply(_UpperCAmelCase,       _UpperCAmelCase  )),       2  ) + 1  )]
						__UpperCAmelCase	:							str     =     median
		return median_img


if __name__ == "__main__":
			# read original image
			lowerCAmelCase__      :   int      	=			imread("../image_data/lena.jpg")
			# turn image in gray scale value
			lowerCAmelCase__      :   Any      	=			cvtColor(img, COLOR_BGR2GRAY)

			# get values with two different mask size
			lowerCAmelCase__      :   Tuple      	=			median_filter(gray, 3)
			lowerCAmelCase__      :   Dict      	=			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'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




