





'''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  _A (					_A		):

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


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      Any							      =     None
					__snake_case					:      Optional[Any]							      =     None
					__snake_case					:      Tuple							      =     None
					__snake_case					:      List[Any]							      =     None

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

					# Build dataset for splits
					__snake_case					:      Any							      =     self.builder.as_dataset(
					    split="""train"""  ,      verification_mode=__lowerCAmelCase  ,      in_memory=self.keep_in_memory			)
					return dataset





class  _A :

		def __init__(   self					:   List[Any]  ,      __magic_name__					:   Dataset  ,      __magic_name__					:   str  ,      __magic_name__					:   Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   Optional[int] = None  ,      **__magic_name__					:   Optional[int]  ,      )					->    str:


					"""simple docstring"""

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

					__snake_case					:      Dict							      =     dataset
					__snake_case					:      int							      =     name
					__snake_case					:      Dict							      =     con
					__snake_case					:      str							      =     batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
					__snake_case					:      Optional[int]							      =     num_proc
					__snake_case					:      Optional[Any]							      =     to_sql_kwargs

		def 	lowercase__     (   self					:   str			)					->    int:


					"""simple docstring"""

					__snake_case					:      str							      =     self.to_sql_kwargs.pop("""sql"""  ,      __lowerCAmelCase			)
					__snake_case					:      List[str]							      =     self.to_sql_kwargs.pop("""con"""  ,      __lowerCAmelCase			)
					__snake_case					:      int							      =     self.to_sql_kwargs.pop("""index"""  ,      __lowerCAmelCase			)

					__snake_case					:      str							      =     self._write(index=__lowerCAmelCase  ,      **self.to_sql_kwargs			)
					return written

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Dict  ,      **__magic_name__					:   Optional[int]			)					->    int:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     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:
								__snake_case      ,     __snake_case					:      Optional[int]							      =     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







from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

UpperCAmelCase_		 =					TypeVar("""T""")

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (position - 1) // 2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     int:
   return (2 * position) + 2


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					Optional[int]      )   ->	None:
    _A      =       []
    _A      =       {}
    _A      =       0

 def __len__(    self					:					str      )   ->	int:
    return self.elements

 def __repr__(    self					:					Optional[int]      )   ->	str:
    return str(self.heap      )

 def     snake_case_      (    self					:					str      )   ->	bool:
    # Check if the priority queue is empty
    return self.elements == 0

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an element with given priority to the queue
    self.heap.append((elem, weight)      )
    _A      =       self.elements
    self.elements += 1
    self._bubble_up(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Tuple      )   ->	T:
    # Remove and return the element with lowest weight (highest priority)
    if self.elements > 1:
       self._swap_nodes(0			,						self.elements - 1      )
    _A  ,    _A      =       self.heap.pop()
    del self.position_map[elem]
    self.elements -= 1
    if self.elements > 0:
       _A  ,    _A      =       self.heap[0]
       self._bubble_down(__lowerCAmelCase      )
    return elem

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Update the weight of the given key
    _A      =       self.position_map[elem]
    _A      =       (elem, weight)
    if position > 0:
       _A      =       get_parent_position(__lowerCAmelCase      )
       _A  ,    _A      =       self.heap[parent_position]
       if parent_weight > weight:
          self._bubble_up(__lowerCAmelCase      )
       else:
          self._bubble_down(__lowerCAmelCase      )
    else:
       self._bubble_down(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (upward movement) [to be used internally
    # only]
    _A      =       self.position_map[elem]
    if curr_pos == 0:
       return None
    _A      =       get_parent_position(__lowerCAmelCase      )
    _A  ,    _A      =       self.heap[curr_pos]
    _A  ,    _A      =       self.heap[parent_position]
    if parent_weight > weight:
       self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
       return self._bubble_up(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					T      )   ->	None:
    # Place a node at the proper position (downward movement) [to be used
    # internally only]
    _A      =       self.position_map[elem]
    _A  ,    _A      =       self.heap[curr_pos]
    _A      =       get_child_left_position(__lowerCAmelCase      )
    _A      =       get_child_right_position(__lowerCAmelCase      )
    if child_left_position < self.elements and child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    if child_left_position < self.elements:
       _A  ,    _A      =       self.heap[child_left_position]
       if child_left_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    else:
       return None
    if child_right_position < self.elements:
       _A  ,    _A      =       self.heap[child_right_position]
       if child_right_weight < weight:
          self._swap_nodes(__lowerCAmelCase			,						__lowerCAmelCase      )
          return self._bubble_down(__lowerCAmelCase      )
    return None

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	None:
    # Swap the nodes at the given positions
    _A      =       self.heap[nodea_pos][0]
    _A      =       self.heap[nodea_pos][0]
    _A  ,    _A      =       (
        self.heap[nodea_pos],
        self.heap[nodea_pos],
    )
    _A      =       nodea_pos
    _A      =       nodea_pos


class 	lowerCamelCase__	(       Generic[T]):



 """simple docstring"""

 def __init__(    self					:					str      )   ->	None:
    _A      =       {}
    _A      =       0

 def __repr__(    self					:					str      )   ->	str:
    return str(self.connections      )

 def __len__(    self					:					Dict      )   ->	int:
    return self.nodes

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					T      )   ->	None:
    # Add a node in the graph if it is not in the graph
    if node not in self.connections:
       _A      =       {}
       self.nodes += 1

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					T			,						__lowerCAmelCase					:					int      )   ->	None:
    # Add an edge between 2 nodes in the graph
    self.add_node(__lowerCAmelCase      )
    self.add_node(__lowerCAmelCase      )
    _A      =       weight
    _A      =       weight

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:GraphUndirectedWeighted[T]	,   )  ->     tuple[dict[T, int], dict[T, T | None]]:
   _A      =       {node: maxsize for node in graph.connections}
   _A      =       {node: None for node in graph.connections}

   _A      =       MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(_snake_case	,   _snake_case				)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   _A      =       priority_queue.extract_min()
   _A      =       0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         _A      =       dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(_snake_case	,   dist[neighbour]				)
         _A      =       node

    # running prim's algorithm
   while not priority_queue.is_empty():
      _A      =       priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            _A      =       dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(_snake_case	,   dist[neighbour]				)
            _A      =       node
   return dist, parent



