"""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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

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

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

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


if __name__ == "__main__":
     # read original image
     _a      :		Optional[Any]           =   imread(r'../image_data/lena.jpg')
     # turn image in gray scale value
     _a      :		Dict           =   cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     _a      :		Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
     _a      :		List[Any]           =   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 itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_a      :		Dict           =   datasets.utils.logging.get_logger(__name__)
@dataclass
class 						__A							( datasets.BuilderConfig		):
     _UpperCamelCase							:			int							=		10_000
     _UpperCamelCase							:			Optional[List[str]]							=		None
     _UpperCamelCase							:			Optional[datasets.Features]							=		None







class 						__A							( datasets.ArrowBasedBuilder		):
     _UpperCamelCase							:			List[str]							=		ParquetConfig






     def     __A  (		self  ):
       return datasets.DatasetInfo(features=self.config.features  )






     def     __A  (		self				,     a__  ):
       if not self.config.data_files:
         raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"  )
       _lowerCAmelCase						:       Optional[Any]  			=     dl_manager.download_and_extract(self.config.data_files  )
       if isinstance(a__				,     (str, list, tuple)  ):
         _lowerCAmelCase						:       Any  			=     data_files
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Tuple  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Any  			=     [dl_manager.iter_files(a__  ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,     gen_kwargs={"""files""": files}  )]
       _lowerCAmelCase						:       Optional[Any]  			=     []
       for split_name, files in data_files.items():
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Dict  			=     [files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase						:       Tuple  			=     [dl_manager.iter_files(a__  ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
           for file in itertools.chain.from_iterable(a__  ):
             with open(a__				,     """rb"""  ) as f:
               _lowerCAmelCase						:       Optional[Any]  			=     datasets.Features.from_arrow_schema(pq.read_schema(a__  )  )
             break
         splits.append(datasets.SplitGenerator(name=a__				,     gen_kwargs={"""files""": files}  )  )
       return splits






     def     __A  (		self				,     a__  ):
       if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _lowerCAmelCase						:       Optional[int]  			=     table_cast(a__				,     self.info.features.arrow_schema  )
       return pa_table






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.info.features.arrow_schema if self.info.features is not None else None
       if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema  ) != sorted(self.config.columns  ):
           raise ValueError(
               F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"  )
       for file_idx, file in enumerate(itertools.chain.from_iterable(a__  )  ):
         with open(a__				,     """rb"""  ) as f:
           _lowerCAmelCase						:       Tuple  			=     pq.ParquetFile(a__  )
           try:
             for batch_idx, record_batch in enumerate(
                 parquet_file.iter_batches(batch_size=self.config.batch_size				,     columns=self.config.columns  )  ):
               _lowerCAmelCase						:       Any  			=     pa.Table.from_batches([record_batch]  )
               # Uncomment for debugging (will print the Arrow table size and elements)
               # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
               # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
               yield F"{file_idx}_{batch_idx}", self._cast_table(a__  )
           except ValueError as e:
             logger.error(F"Failed to read file '{file}' with error {type(a__  )}: {e}"  )
             raise




