

'''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   UpperCamelCase__    (   _UpperCamelCase   ):


		"""simple docstring"""

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


							'''simple docstring'''

							super().__init__(features=_UpperCAmelCase    ,			cache_dir=_UpperCAmelCase    ,			keep_in_memory=_UpperCAmelCase    ,			**_UpperCAmelCase			)
							UpperCAmelCase		:							Tuple   					=   Sql(
							    cache_dir=_UpperCAmelCase    ,			features=_UpperCAmelCase    ,			sql=_UpperCAmelCase    ,			con=_UpperCAmelCase    ,			**_UpperCAmelCase    ,			)







		def       A_       ( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]   					=   None
							UpperCAmelCase		:							Dict   					=   None
							UpperCAmelCase		:							Dict   					=   None
							UpperCAmelCase		:							Optional[int]   					=   None

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

							# Build dataset for splits
							UpperCAmelCase		:							List[str]   					=   self.builder.as_dataset(
							    split="train"    ,			verification_mode=_UpperCAmelCase    ,			in_memory=self.keep_in_memory			)
							return dataset







class   UpperCamelCase__    :


		"""simple docstring"""

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


							'''simple docstring'''

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

							UpperCAmelCase		:							Dict   					=   dataset
							UpperCAmelCase		:							List[Any]   					=   name
							UpperCAmelCase		:							Tuple   					=   con
							UpperCAmelCase		:							Union[str, Any]   					=   batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
							UpperCAmelCase		:							List[Any]   					=   num_proc
							UpperCAmelCase		:							Tuple   					=   to_sql_kwargs







		def       A_       ( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Tuple   					=   self.to_sql_kwargs.pop("sql"    ,			_UpperCAmelCase			)
							UpperCAmelCase		:							str   					=   self.to_sql_kwargs.pop("con"    ,			_UpperCAmelCase			)
							UpperCAmelCase		:							Optional[Any]   					=   self.to_sql_kwargs.pop("index"    ,			_UpperCAmelCase			)

							UpperCAmelCase		:							Any   					=   self._write(index=_UpperCAmelCase    ,			**self.to_sql_kwargs			)
							return written







		def       A_       ( self    ,			snake_case			):


							'''simple docstring'''

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







		def       A_       ( self    ,			snake_case    ,			**snake_case			):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]   					=   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:
												UpperCAmelCase		:							List[Any]   					=   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    ,			_UpperCAmelCase    ,			_UpperCAmelCase			)]    ,			)    ,			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





'''simple docstring'''

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.ndarray:
		_a     :      Union[str, Any]        =							cva.getAffineTransform(lowerCAmelCase_						,			lowerCAmelCase_  )
		return cva.warpAffine(lowerCAmelCase_						,			lowerCAmelCase_						,			(rows, cols)  )


if __name__ == "__main__":
							# read original image
							__lowerCAmelCase									=   cva.imread(
							    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
							)
							# turn image in gray scale value
							__lowerCAmelCase									=   cva.cvtColor(image, cva.COLOR_BGR2GRAY)
							# get image shape
							__lowerCAmelCase			,					__lowerCAmelCase									=   gray_img.shape

							# set different points to rotate image
							__lowerCAmelCase									=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
							__lowerCAmelCase									=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
							__lowerCAmelCase									=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

							# add all rotated images in a list
							__lowerCAmelCase									=   [
							    gray_img,
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
							]

							# plot different image rotations
							__lowerCAmelCase									=   plt.figure(1)
							__lowerCAmelCase									=   ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
							for i, image in enumerate(images):
														plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
														plt.title(titles[i])
														plt.axis('''off''')
														plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
							plt.show()




