





"""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 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Any:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				k_size // 2
  lowerCAmelCase__				,							lowerCAmelCase__     :Tuple											=				mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  lowerCAmelCase__     :List[Any]											=				1 / (2 * pi * sigma) * exp(-(square(_SCREAMING_SNAKE_CASE     ) + square(_SCREAMING_SNAKE_CASE     )) / (2 * square(_SCREAMING_SNAKE_CASE     ))     )
  return g
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__				,							lowerCAmelCase__     :Optional[Any]											=				image.shape[0], image.shape[1]
  # dst image height and width
  lowerCAmelCase__     :Union[str, Any]											=				height - k_size + 1
  lowerCAmelCase__     :Optional[Any]											=				width - k_size + 1

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

  #  turn the kernel into shape(k*k, 1)
  lowerCAmelCase__     :str											=				gen_gaussian_kernel(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :str											=				ravel(_SCREAMING_SNAKE_CASE     )

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

  return dst


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

  # get values with two different mask size
  __A											=						gaussian_filter(gray, 3, sigma=1)
  __A											=						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"""






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
       import sqlitea

       import sqlalchemy





class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            super().__init__(features=__lowerCAmelCase						,			cache_dir=__lowerCAmelCase						,			keep_in_memory=__lowerCAmelCase						,			**__lowerCAmelCase		)
            lowercase								=      Sql(
                cache_dir=__lowerCAmelCase						,			features=__lowerCAmelCase						,			sql=__lowerCAmelCase						,			con=__lowerCAmelCase						,			**__lowerCAmelCase						,			)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      None
            lowercase								=      None
            lowercase								=      None
            lowercase								=      None

            self.builder.download_and_prepare(
                download_config=__lowerCAmelCase						,			download_mode=__lowerCAmelCase						,			verification_mode=__lowerCAmelCase						,			base_path=__lowerCAmelCase						,			)

            # Build dataset for splits
            lowercase								=      self.builder.as_dataset(
                split="""train"""						,			verification_mode=__lowerCAmelCase						,			in_memory=self.keep_in_memory		)
            return dataset






class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if num_proc is not None and num_proc <= 0:
                  raise ValueError(f'num_proc {num_proc} must be an integer > 0.'		)

            lowercase								=      dataset
            lowercase								=      name
            lowercase								=      con
            lowercase								=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
            lowercase								=      num_proc
            lowercase								=      to_sql_kwargs

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.to_sql_kwargs.pop("""sql"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""con"""						,			__lowerCAmelCase		)
            lowercase								=      self.to_sql_kwargs.pop("""index"""						,			__lowerCAmelCase		)

            lowercase								=      self._write(index=__lowerCAmelCase						,			**self.to_sql_kwargs		)
            return written

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase				,				lowercase				,				lowercase								=      args
            lowercase								=      {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
            lowercase								=      query_table(
                table=self.dataset.data						,			key=slice(__lowerCAmelCase						,			offset + self.batch_size		)						,			indices=self.dataset._indices						,			)
            lowercase								=      batch.to_pandas()
            lowercase								=      df.to_sql(self.name						,			self.con						,			index=__lowerCAmelCase						,			**__lowerCAmelCase		)
            return num_rows or len(__lowerCAmelCase		)







      def 							A__			(		self						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      0

            if self.num_proc is None or self.num_proc == 1:
                  for offset in logging.tqdm(
                      range(0						,			len(self.dataset		)						,			self.batch_size		)						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                        written += self._batch_sql((offset, index, to_sql_kwargs)		)
            else:
                  lowercase				,				lowercase								=      len(self.dataset		), self.batch_size
                  with multiprocessing.Pool(self.num_proc		) as pool:
                        for num_rows in logging.tqdm(
                            pool.imap(
                                self._batch_sql						,			[(offset, index, to_sql_kwargs) for offset in range(0						,			__lowerCAmelCase						,			__lowerCAmelCase		)]						,			)						,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size						,			unit="""ba"""						,			disable=not logging.is_progress_bar_enabled()						,			desc="""Creating SQL from Arrow format"""						,			):
                              written += num_rows

            return written



