



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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.sql.Selectable"]			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,						__lowerCAmelCase					:					Optional[Features] = None			,						__lowerCAmelCase					:					str = None			,						__lowerCAmelCase					:					bool = False			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	List[str]:
    super().__init__(features=__lowerCAmelCase			,						cache_dir=__lowerCAmelCase			,						keep_in_memory=__lowerCAmelCase			,						**__lowerCAmelCase      )
    _A      =       Sql(
        cache_dir=__lowerCAmelCase			,						features=__lowerCAmelCase			,						sql=__lowerCAmelCase			,						con=__lowerCAmelCase			,						**__lowerCAmelCase			,						)

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       None
    _A      =       None
    _A      =       None
    _A      =       None

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

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


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					Dataset			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]			,						__lowerCAmelCase					:					Optional[int] = None			,						__lowerCAmelCase					:					Optional[int] = None			,						**__lowerCAmelCase					:					Optional[int]			,						)   ->	str:
    if num_proc is not None and num_proc <= 0:
       raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''      )

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

 def     snake_case_      (    self					:					str      )   ->	int:
    _A      =       self.to_sql_kwargs.pop('''sql'''			,						__lowerCAmelCase      )
    _A      =       self.to_sql_kwargs.pop('''con'''			,						__lowerCAmelCase      )
    _A      =       self.to_sql_kwargs.pop('''index'''			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Union[str, Any]      )   ->	Dict:
    _A  ,    _A  ,    _A      =       args
    _A      =       {**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
    _A      =       query_table(
        table=self.dataset.data			,						key=slice(__lowerCAmelCase			,						offset + self.batch_size      )			,						indices=self.dataset._indices			,						)
    _A      =       batch.to_pandas()
    _A      =       df.to_sql(self.name			,						self.con			,						index=__lowerCAmelCase			,						**__lowerCAmelCase      )
    return num_rows or len(__lowerCAmelCase      )

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					Dict			,						**__lowerCAmelCase					:					Optional[int]      )   ->	int:
    _A      =       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:
       _A  ,    _A      =       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








UpperCAmelCase_		 =					2_5_6
# Modulus to hash a string
UpperCAmelCase_		 =					1_0_0_0_0_0_3

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str				)  ->     bool:
   _A      =       len(_snake_case				)
   _A      =       len(_snake_case				)
   if p_len > t_len:
      return False

   _A      =       0
   _A      =       0
   _A      =       1

   # Calculating the hash of pattern and substring of text
   for i in range(_snake_case				):
      _A      =       (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
      _A      =       (ord(text[i]				) + text_hash * alphabet_size) % modulus
      if i == p_len - 1:
         continue
      _A      =       (modulus_power * alphabet_size) % modulus

   for i in range(0	,   t_len - p_len + 1				):
      if text_hash == p_hash and text[i : i + p_len] == pattern:
         return True
      if i == t_len - p_len:
         continue
      # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
      _A      =       (
          (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
          + ord(text[i + p_len]				)
      ) % modulus
   return False

def 	SCREAMING_SNAKE_CASE_							(	)  ->     None:
   _A      =       '''abc1abc12'''
   _A      =       '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   _A      =       '''alskfjaldsk23adsfabcabc'''
   assert rabin_karp(_snake_case	,   _snake_case				) and not rabin_karp(_snake_case	,   _snake_case				)

   # Test 2)
   _A      =       '''ABABX'''
   _A      =       '''ABABZABABYABABX'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 3)
   _A      =       '''AAAB'''
   _A      =       '''ABAAAAAB'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 4)
   _A      =       '''abcdabcy'''
   _A      =       '''abcxabcdabxabcdabcdabcy'''
   assert rabin_karp(_snake_case	,   _snake_case				)

   # Test 5)
   _A      =       '''Lü'''
   _A      =       '''Lüsai'''
   assert rabin_karp(_snake_case	,   _snake_case				)
   _A      =       '''Lue'''
   assert not rabin_karp(_snake_case	,   _snake_case				)
   print('''Success.'''				)


if __name__ == "__main__":
   test_rabin_karp()



