






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(    __lowerCamelCase			):

  """simple docstring"""







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

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      **SCREAMING_SNAKE_CASE_	,      ):
       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						):
       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_						):
       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_						):
       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

from collections import Counter
from random import random




class      __A:

  """simple docstring"""







  def __init__(self						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       if nodea not in self.connections:
            self.add_node(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    probability







  def 	UpperCAmelCase_ (self						):
       return list(self.connections						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       UpperCamelCase__				=    random()

       for dest in self.connections[node]:
            current_probability += self.connections[node][dest]
            if current_probability > random_value:
                 return dest
       return ""






def 						__magic_name__			(   __a      :	str	,		__a      :	list[tuple[str, str, float]]	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    MarkovChainGraphUndirectedUnweighted()

     for nodea, nodea, probability in transitions:
          graph.add_transition_probability(__a	,		__a	,		__a						)

     UpperCamelCase__				=    Counter(graph.get_nodes()						)
     UpperCamelCase__				=    start

     for _ in range(__a						):
          UpperCamelCase__				=    graph.transition(__a						)
          visited[node] += 1

     return visited


if __name__ == "__main__":
    import doctest

    doctest.testmod()





