

import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
     import sqlitea

     import sqlalchemy


_lowerCAmelCase :	Union[str, Any]     				=    datasets.utils.logging.get_logger(__name__)


@dataclass
class     lowerCAmelCase				(    datasets.BuilderConfig				):


       '''simple docstring'''







       snake_case    							=	None
       snake_case    							=	None
       snake_case    							=	None
       snake_case    							=	True
       snake_case    							=	None
       snake_case    							=	None
       snake_case    							=	None
       snake_case    							=	10_000
       snake_case    							=	None

       def   lowerCamelCase__						(	self					:  Dict     )   -> Any:







        '''simple docstring'''

        if self.sql is None:
         raise ValueError('sql must be specified'     )
        if self.con is None:
         raise ValueError('con must be specified'     )

       def   lowerCamelCase__						(	self					:  List[Any]						,	__snake_case					:  dict						,	__snake_case					:  Optional[datasets.Features] = None						,	)   -> str:







        '''simple docstring'''

        lowerCamelCase      						=    config_kwargs.copy()
        # We need to stringify the Selectable object to make its hash deterministic

        # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
        lowerCamelCase      						=    config_kwargs['sql']
        if not isinstance(__snake_case						,	__snake_case     ):
         if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

          if isinstance(__snake_case						,	sqlalchemy.sql.Selectable     ):
           lowerCamelCase      						=    sqlalchemy.create_engine(config_kwargs['con'].split('://'     )[0] + '://'     )
           lowerCamelCase      						=    str(sql.compile(dialect=engine.dialect     )     )
           lowerCamelCase      						=    sql_str
          else:
           raise TypeError(
               F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(__snake_case     )}: {sql}'''     )
         else:
          raise TypeError(
              F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(__snake_case     )}: {sql}'''     )
        lowerCamelCase      						=    config_kwargs['con']
        if not isinstance(__snake_case						,	__snake_case     ):
         lowerCamelCase      						=    id(__snake_case     )
         logger.info(
             F'''SQL connection \'con\' of type {type(__snake_case     )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''     )

        return super().create_config_id(__snake_case						,	custom_features=__snake_case     )





       @property
       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    {
            'index_col': self.index_col,
            'columns': self.columns,
            'params': self.params,
            'coerce_float': self.coerce_float,
            'parse_dates': self.parse_dates,
        }
        return pd_read_sql_kwargs


class     lowerCAmelCase				(    datasets.ArrowBasedBuilder				):


       '''simple docstring'''







       snake_case    							=	SqlConfig

       def   lowerCamelCase__						(	self					:  Tuple     )   -> Any:







        '''simple docstring'''

        return datasets.DatasetInfo(features=self.config.features     )

       def   lowerCamelCase__						(	self					:  int						,	__snake_case					:  str     )   -> List[str]:







        '''simple docstring'''

        return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,	gen_kwargs={}     )]

       def   lowerCamelCase__						(	self					:  List[str]						,	__snake_case					:  pa.Table     )   -> pa.Table:







        '''simple docstring'''

        if self.config.features is not None:
         lowerCamelCase      						=    self.config.features.arrow_schema
         if all(not require_storage_cast(__snake_case     ) for feature in self.config.features.values()     ):
          # cheaper cast
          lowerCamelCase      						=    pa.Table.from_arrays([pa_table[field.name] for field in schema]						,	schema=__snake_case     )
         else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          lowerCamelCase      						=    table_cast(__snake_case						,	__snake_case     )
        return pa_table





       def   lowerCamelCase__						(	self					:  Any     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    self.config.chunksize
        lowerCamelCase      						=    pd.read_sql(
            self.config.sql						,	self.config.con						,	chunksize=__snake_case						,	**self.config.pd_read_sql_kwargs     )
        lowerCamelCase      						=    [sql_reader] if chunksize is None else sql_reader
        for chunk_idx, df in enumerate(__snake_case     ):
         lowerCamelCase      						=    pa.Table.from_pandas(__snake_case     )
         yield chunk_idx, self._cast_table(__snake_case     )







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

from digital_image_processing.filters.convolve import img_convolve







def a_		(       UpperCamelCase_   :							Dict  )							->   str:







 """simple docstring"""

 lowerCamelCase      						=    np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]  )
 lowerCamelCase      						=    np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]  )

 lowerCamelCase      						=    np.abs(img_convolve(UpperCamelCase_   ,					UpperCamelCase_  )  )
 lowerCamelCase      						=    np.abs(img_convolve(UpperCamelCase_   ,					UpperCamelCase_  )  )
 # modify the pix within [0, 255]
 lowerCamelCase      						=    dst_x * 2_5_5 / np.max(UpperCamelCase_  )
 lowerCamelCase      						=    dst_y * 2_5_5 / np.max(UpperCamelCase_  )

 lowerCamelCase      						=    np.sqrt((np.square(UpperCamelCase_  )) + (np.square(UpperCamelCase_  ))  )
 lowerCamelCase      						=    dst_xy * 2_5_5 / np.max(UpperCamelCase_  )
 lowerCamelCase      						=    dst_xy.astype(np.uinta  )

 lowerCamelCase      						=    np.arctana(UpperCamelCase_   ,					UpperCamelCase_  )
 return dst, theta


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

     _lowerCAmelCase				,   _lowerCAmelCase :	Union[str, Any]     				=    sobel_filter(gray)

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




