

import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
  import sqlitea

  import sqlalchemy


A__										=      datasets.utils.logging.get_logger(__name__)







@dataclass
class  UpperCamelCase_      (	datasets.BuilderConfig      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   None
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   None
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   None
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   True
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   None
  SCREAMING_SNAKE_CASE__					:			Any					    =   None
  SCREAMING_SNAKE_CASE__					:			str					    =   None
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   10_000
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   None



  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Union[str, Any]:

      if self.sql is None:
          raise ValueError("sql must be specified"						)
      if self.con is None:
          raise ValueError("con must be specified"						)



  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					List[Any]  ,	A_     :					List[Any] = None  ,	)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    config_kwargs.copy()
      # We need to stringify the Selectable object to make its hash deterministic

      # The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
      SCREAMING_SNAKE_CASE__         =    config_kwargs["sql"]
      if not isinstance(lowerCamelCase_  ,	lowerCamelCase_						):
          if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
              import sqlalchemy

              if isinstance(lowerCamelCase_  ,	sqlalchemy.sql.Selectable						):
                  SCREAMING_SNAKE_CASE__         =    sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
                  SCREAMING_SNAKE_CASE__         =    str(sql.compile(dialect=engine.dialect						)						)
                  SCREAMING_SNAKE_CASE__         =    sql_str
              else:
                  raise TypeError(
                      f"Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}"						)
          else:
              raise TypeError(
                  f"Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}"						)
      SCREAMING_SNAKE_CASE__         =    config_kwargs["con"]
      if not isinstance(lowerCamelCase_  ,	lowerCamelCase_						):
          SCREAMING_SNAKE_CASE__         =    id(lowerCamelCase_						)
          logger.info(
              f"SQL connection \'con\' of type {type(lowerCamelCase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead."						)

      return super().create_config_id(lowerCamelCase_  ,	custom_features=lowerCamelCase_						)







  @property
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    {
          "index_col": self.index_col,
          "columns": self.columns,
          "params": self.params,
          "coerce_float": self.coerce_float,
          "parse_dates": self.parse_dates,
      }
      return pd_read_sql_kwargs







class  UpperCamelCase_      (	datasets.ArrowBasedBuilder      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   SqlConfig



  def   _UpperCamelCase     (						self     :					str						)       ->			Tuple:
      return datasets.DatasetInfo(features=self.config.features						)



  def   _UpperCamelCase     (						self     :					int  ,	A_     :					Any						)       ->			str:
      return [datasets.SplitGenerator(name=datasets.Split.TRAIN  ,	gen_kwargs={}						)]



  def   _UpperCamelCase     (						self     :					int  ,	A_     :					List[str]						)       ->			List[str]:

      if self.config.features is not None:
          SCREAMING_SNAKE_CASE__         =    self.config.features.arrow_schema
          if all(not require_storage_cast(lowerCamelCase_						) for feature in self.config.features.values()						):
              # cheaper cast
              SCREAMING_SNAKE_CASE__         =    pa.Table.from_arrays([pa_table[field.name] for field in schema]  ,	schema=lowerCamelCase_						)
          else:
              # more expensive cast; allows str <-> int/float or str to Audio for example
              SCREAMING_SNAKE_CASE__         =    table_cast(lowerCamelCase_  ,	lowerCamelCase_						)
      return pa_table







  def   _UpperCamelCase     (						self     :					List[str]						)       ->			Tuple:
      SCREAMING_SNAKE_CASE__         =    self.config.chunksize
      SCREAMING_SNAKE_CASE__         =    pd.read_sql(
          self.config.sql  ,	self.config.con  ,	chunksize=lowerCamelCase_  ,	**self.config.pd_read_sql_kwargs						)
      SCREAMING_SNAKE_CASE__         =    [sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(lowerCamelCase_						):
          SCREAMING_SNAKE_CASE__         =    pa.Table.from_pandas(lowerCamelCase_						)
          yield chunk_idx, self._cast_table(lowerCamelCase_						)







_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
