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    lowercase__		(  __a):


						def __init__(			self      :							List[Any]       ,						UpperCamelCase__      :							Union[str, "sqlalchemy.sql.Selectable"]       ,						UpperCamelCase__      :							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       ,						UpperCamelCase__      :							Optional[Features] = None       ,						UpperCamelCase__      :							str = None       ,						UpperCamelCase__      :							bool = False       ,						**UpperCamelCase__      :							int       ,						):





												'''simple docstring'''





												super().__init__(features=_lowercase       ,						cache_dir=_lowercase       ,						keep_in_memory=_lowercase       ,						**_lowercase		)
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				Sql(
												    cache_dir=_lowercase       ,						features=_lowercase       ,						sql=_lowercase       ,						con=_lowercase       ,						**_lowercase       ,						)



						def 			__A			(			self      :							int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Tuple     							=				None
												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				None
												SCREAMING_SNAKE_CASE				:       Dict     							=				None
												SCREAMING_SNAKE_CASE				:       List[str]     							=				None

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

												# Build dataset for splits
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				self.builder.as_dataset(
												    split='''train'''       ,						verification_mode=_lowercase       ,						in_memory=self.keep_in_memory		)
												return dataset






class    lowercase__		:


						def __init__(			self      :							Dict       ,						UpperCamelCase__      :							Dataset       ,						UpperCamelCase__      :							str       ,						UpperCamelCase__      :							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       ,						UpperCamelCase__      :							Optional[int] = None       ,						UpperCamelCase__      :							Optional[int] = None       ,						**UpperCamelCase__      :							int       ,						):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE				:       Optional[Any]     							=				dataset
												SCREAMING_SNAKE_CASE				:       Any     							=				name
												SCREAMING_SNAKE_CASE				:       List[Any]     							=				con
												SCREAMING_SNAKE_CASE				:       Any     							=				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
												SCREAMING_SNAKE_CASE				:       int     							=				num_proc
												SCREAMING_SNAKE_CASE				:       Union[str, Any]     							=				to_sql_kwargs


						def 			__A			(			self      :							Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       str     							=				self.to_sql_kwargs.pop('''sql'''       ,						_lowercase		)
												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				self.to_sql_kwargs.pop('''con'''       ,						_lowercase		)
												SCREAMING_SNAKE_CASE				:       str     							=				self.to_sql_kwargs.pop('''index'''       ,						_lowercase		)

												SCREAMING_SNAKE_CASE				:       Optional[int]     							=				self._write(index=_lowercase       ,						**self.to_sql_kwargs		)
												return written


						def 			__A			(			self      :							Union[str, Any]       ,						UpperCamelCase__      :							List[str]		):





												'''simple docstring'''





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



						def 			__A			(			self      :							Optional[int]       ,						UpperCamelCase__      :							Optional[Any]       ,						**UpperCamelCase__      :							Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE				:       Tuple     							=				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:
																		SCREAMING_SNAKE_CASE				:       Dict     							=				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       ,						_lowercase       ,						_lowercase		)]       ,						)       ,						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






import random




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()