



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def     __lowerCamelCase							(   __magic_name__						:	List[str]    ):
							a__:							List[Any]									=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]    )
							a__:							Any									=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]    )

							a__:							int									=np.abs(img_convolve(UpperCAmelCase_  ,			UpperCAmelCase_    )    )
							a__:							List[str]									=np.abs(img_convolve(UpperCAmelCase_  ,			UpperCAmelCase_    )    )
							# modify the pix within [0, 255]
							a__:							Optional[Any]									=dst_x * 255 / np.max(UpperCAmelCase_    )
							a__:							Any									=dst_y * 255 / np.max(UpperCAmelCase_    )

							a__:							Optional[Any]									=np.sqrt((np.square(UpperCAmelCase_    )) + (np.square(UpperCAmelCase_    ))    )
							a__:							Union[str, Any]									=dst_xy * 255 / np.max(UpperCAmelCase_    )
							a__:							List[str]									=dst_xy.astype(np.uinta    )

							a__:							List[str]									=np.arctana(UpperCAmelCase_  ,			UpperCAmelCase_    )
							return dst, theta


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

		__UpperCAmelCase    	=  sobel_filter(gray)

		# show result images
		imshow('''sobel filter''', sobel_grad)
		imshow('''sobel theta''', sobel_theta)
		waitKey(0)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





