





'''simple docstring'''

from __future__ import annotations


class 			SCREAMING_SNAKE_CASE__	:







					"""simple docstring"""






					def __init__(					self    :	Optional[Any]						,     UpperCAmelCase_    :	int = 0						):







							"""simple docstring"""
							__UpperCAmelCase     :				Optional[Any]					   =			value
							__UpperCAmelCase     :				Node | None					   =			None
							__UpperCAmelCase     :				Node | None					   =			None






def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):

		if treea is None:
				return treea
		if treea is None:
				return treea

		__UpperCAmelCase     :				Union[str, Any]					   =			treea.value + treea.value
		__UpperCAmelCase     :				List[Any]					   =			merge_two_binary_trees(treea.left,    treea.left   )
		__UpperCAmelCase     :				Union[str, Any]					   =			merge_two_binary_trees(treea.right,    treea.right   )
		return treea


def 		__UpperCamelCase		(       _UpperCAmelCase   ):

		if root:
				print(root.value   )
				print_preorder(root.left   )
				print_preorder(root.right   )


if __name__ == "__main__":
			lowerCAmelCase__	:					Tuple	     =       Node(1)
			lowerCAmelCase__	:					int	     =       Node(2)
			lowerCAmelCase__	:					Union[str, Any]	     =       Node(3)
			lowerCAmelCase__	:					Optional[int]	     =       Node(4)

			lowerCAmelCase__	:					Optional[Any]	     =       Node(2)
			lowerCAmelCase__	:					Tuple	     =       Node(4)
			lowerCAmelCase__	:					Union[str, Any]	     =       Node(6)
			lowerCAmelCase__	:					List[Any]	     =       Node(9)
			lowerCAmelCase__	:					Optional[Any]	     =       Node(5)

			print("Tree1 is: ")
			print_preorder(treea)
			print("Tree2 is: ")
			print_preorder(treea)
			lowerCAmelCase__	:					List[str]	     =       merge_two_binary_trees(treea, treea)
			print("Merged Tree is: ")
			print_preorder(merged_tree)












"""simple docstring"""


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


_UpperCamelCase				:    Optional[Any]     =						datasets.utils.logging.get_logger(__name__)






@dataclass
class 						snake_case  (  datasets.BuilderConfig       ):
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     True
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     None
    __magic_name__         =     1_0000
    __magic_name__         =     None





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					int				,		A	:					dict				,		A	:					Optional[datasets.Features] = None				,		):


      '''simple docstring'''




      a     :			str				=   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
      a     :			List[str]				=   config_kwargs['sql']
      if not isinstance(A				,		A			):
        if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
          import sqlalchemy

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

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





    @property
    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			int				=   {
          '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 						snake_case  (  datasets.ArrowBasedBuilder       ):
    __magic_name__         =     SqlConfig





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      return datasets.DatasetInfo(features=self.config.features			)





    def 				lowerCamelCase__      (							self	:					Dict				,		A	:					Optional[int]			):


      '''simple docstring'''




      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,		gen_kwargs={}			)]





    def 				lowerCamelCase__      (							self	:					Union[str, Any]				,		A	:					pa.Table			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			List[Any]				=   self.config.chunksize
      a     :			Union[str, Any]				=   pd.read_sql(
          self.config.sql				,		self.config.con				,		chunksize=A				,		**self.config.pd_read_sql_kwargs			)
      a     :			Optional[Any]				=   [sql_reader] if chunksize is None else sql_reader
      for chunk_idx, df in enumerate(A			):
        a     :			List[Any]				=   pa.Table.from_pandas(A			)
        yield chunk_idx, self._cast_table(A			)




