



"""simple docstring"""

from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: List[str]					):
							__a		:		Dict									=       k_size // 2
							__a							,     __a		:		str									=       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							__a		:		Optional[Any]									=       1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase					) + square(_lowerCamelCase					)) / (2 * square(_lowerCamelCase					))					)
							return g





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: Union[str, Any]					):
							__a							,     __a		:		Optional[Any]									=       image.shape[0], image.shape[1]
							# dst image height and width
							__a		:		int									=       height - k_size + 1
							__a		:		Dict									=       width - k_size + 1

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							__a		:		Any									=       zeros((dst_height * dst_width, k_size * k_size)					)
							__a		:		List[str]									=       0
							for i, j in product(range(_lowerCamelCase					)  ,       range(_lowerCamelCase					)					):
														__a		:		Optional[int]									=       ravel(image[i : i + k_size, j : j + k_size]					)
														__a		:		Union[str, Any]									=       window
														row += 1

							#  turn the kernel into shape(k*k, 1)
							__a		:		List[Any]									=       gen_gaussian_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		List[str]									=       ravel(_lowerCamelCase					)

							# reshape and get the dst image
							__a		:		Optional[Any]									=       dot(_lowerCamelCase  ,       _lowerCamelCase					).reshape(_lowerCamelCase  ,       _lowerCamelCase					).astype(_lowerCamelCase					)

							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)

	# get values with two different mask size
	lowercase__			   =  gaussian_filter(gray, 3, sigma=1)
	lowercase__			   =  gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("gaussian filter with 3x3 mask", gaussianaxa)
	imshow("gaussian filter with 5x5 mask", gaussianaxa)
	waitKey()






"""simple docstring"""

from typing import TYPE_CHECKING

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


lowercase__			   =  {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "LlamaForCausalLM",
	    "LlamaModel",
	    "LlamaPreTrainedModel",
	    "LlamaForSequenceClassification",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

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

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

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	lowercase__			   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

