


"""simple docstring"""






import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     A(     TensorFormatter[Mapping, np.ndarray, Mapping]		):

		"""simple docstring"""





		def __init__(   self				,							SCREAMING_SNAKE_CASE__=None				,							**SCREAMING_SNAKE_CASE__      )			->  Tuple:

					"""simple docstring"""



					super().__init__(features=SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  np_array_kwargs
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  int:

					"""simple docstring"""



					if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ):
								if column and all(
								    isinstance(SCREAMING_SNAKE_CASE__				,							np.ndarray      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
											return np.stack(SCREAMING_SNAKE_CASE__      )
								else:
											# don't use np.array(column, dtype=object)
											# since it fails in certain cases
											# see https://stackoverflow.com/q/51005699
											_UpperCamelCase				:Tuple				    =  np.empty(len(SCREAMING_SNAKE_CASE__      )				,							dtype=SCREAMING_SNAKE_CASE__      )
											_UpperCamelCase				:int				    =  column
											return out
					return column
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Tuple:

					"""simple docstring"""



					if isinstance(SCREAMING_SNAKE_CASE__				,							(str, bytes, type(SCREAMING_SNAKE_CASE__      ))      ):
								return value
					elif isinstance(SCREAMING_SNAKE_CASE__				,							(np.character, np.ndarray)      ) and np.issubdtype(value.dtype				,							np.character      ):
								return value
					elif isinstance(SCREAMING_SNAKE_CASE__				,							np.number      ):
								return value

					_UpperCamelCase				:Optional[int]				    =  {}

					if isinstance(SCREAMING_SNAKE_CASE__				,							np.ndarray      ) and np.issubdtype(value.dtype				,							np.integer      ):
								_UpperCamelCase				:List[Any]				    =  {'''dtype''': np.intaa}
					elif isinstance(SCREAMING_SNAKE_CASE__				,							np.ndarray      ) and np.issubdtype(value.dtype				,							np.floating      ):
								_UpperCamelCase				:Optional[Any]				    =  {'''dtype''': np.floataa}
					elif config.PIL_AVAILABLE and "PIL" in sys.modules:
								import PIL.Image

								if isinstance(SCREAMING_SNAKE_CASE__				,							PIL.Image.Image      ):
											return np.asarray(SCREAMING_SNAKE_CASE__				,							**self.np_array_kwargs      )

					return np.asarray(SCREAMING_SNAKE_CASE__				,							**{**default_dtype, **self.np_array_kwargs}      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

					"""simple docstring"""



					if config.TORCH_AVAILABLE and "torch" in sys.modules:
								import torch

								if isinstance(SCREAMING_SNAKE_CASE__				,							torch.Tensor      ):
											return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
					if hasattr(SCREAMING_SNAKE_CASE__				,							'''__array__'''      ) and not isinstance(SCREAMING_SNAKE_CASE__				,							(np.ndarray, np.character, np.number)      ):
								_UpperCamelCase				:Optional[int]				    =  data_struct.__array__()
					# support for nested types like struct of list of struct
					if isinstance(SCREAMING_SNAKE_CASE__				,							np.ndarray      ):
								if data_struct.dtype == object:
											return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__      ) for substruct in data_struct]      )
					if isinstance(SCREAMING_SNAKE_CASE__				,							(list, tuple)      ):
								return self._consolidate([self.recursive_tensorize(SCREAMING_SNAKE_CASE__      ) for substruct in data_struct]      )
					return self._tensorize(SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Union[str, Any]:

					"""simple docstring"""



					return map_nested(self._recursive_tensorize				,							SCREAMING_SNAKE_CASE__				,							map_list=SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Mapping:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.numpy_arrow_extractor().extract_row(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  self.python_features_decoder.decode_row(SCREAMING_SNAKE_CASE__      )
					return self.recursive_tensorize(SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  np.ndarray:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  self.numpy_arrow_extractor().extract_column(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Dict				    =  self.python_features_decoder.decode_column(SCREAMING_SNAKE_CASE__				,							pa_table.column_names[0]      )
					_UpperCamelCase				:Optional[Any]				    =  self.recursive_tensorize(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:List[str]				    =  self._consolidate(SCREAMING_SNAKE_CASE__      )
					return column





		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  Mapping:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.numpy_arrow_extractor().extract_batch(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  self.python_features_decoder.decode_batch(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Tuple				    =  self.recursive_tensorize(SCREAMING_SNAKE_CASE__      )
					for column_name in batch:
								_UpperCamelCase				:Union[str, Any]				    =  self._consolidate(batch[column_name]      )
					return batch





"""simple docstring"""






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

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


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

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

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

