


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				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	int:




									"""simple docstring"""





									super().__init__(features=_SCREAMING_SNAKE_CASE      ,   cache_dir=_SCREAMING_SNAKE_CASE      ,   keep_in_memory=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      Sql(
									    cache_dir=_SCREAMING_SNAKE_CASE      ,   features=_SCREAMING_SNAKE_CASE      ,   sql=_SCREAMING_SNAKE_CASE      ,   con=_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE      ,   )






		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None
									_UpperCAmelCase					=      None

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

									# Build dataset for splits
									_UpperCAmelCase					=      self.builder.as_dataset(
									    split='train'      ,   verification_mode=_SCREAMING_SNAKE_CASE      ,   in_memory=self.keep_in_memory						)
									return dataset



class 		__a				:
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Any:




									"""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					=      dataset
									_UpperCAmelCase					=      name
									_UpperCAmelCase					=      con
									_UpperCAmelCase					=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_UpperCAmelCase					=      num_proc
									_UpperCAmelCase					=      to_sql_kwargs
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.to_sql_kwargs.pop('sql'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.to_sql_kwargs.pop('con'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.to_sql_kwargs.pop('index'      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self._write(index=_SCREAMING_SNAKE_CASE      ,   **self.to_sql_kwargs						)
									return written
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      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		,	_UpperCAmelCase					=      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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)]      ,   )      ,   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




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
