






from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge




@total_ordering
class     _lowerCAmelCase(	a_ ):





       """simple docstring"""







       def __lt__( self  ,      _lowerCamelCase							):
         return self[-1] < other[-1]





       def __eq__( self  ,      _lowerCamelCase							):
         return self[-1] == other[-1]







def   snake_case (UpperCAmelCase__  )	->    Tuple:






  """simple docstring"""




  UpperCamelCase_: int   				=    []
  # sort into stacks
  for element in collection:
    UpperCamelCase_: List[Any]   				=    Stack([element]  )
    UpperCamelCase_: Optional[int]   				=    bisect_left(lowerCamelCase__     ,   lowerCamelCase__  )
    if i != len(lowerCamelCase__  ):
      stacks[i].append(lowerCamelCase__  )
    else:
      stacks.append(lowerCamelCase__  )

    # use a heap-based merge to merge stack efficiently
  UpperCamelCase_: Tuple   				=    merge(*(reversed(lowerCamelCase__  ) for stack in stacks)  )
  return collection


if __name__ == "__main__":
   A_		:		int		     =			input('Enter numbers separated by a comma:\n').strip()
   A_		:		Union[str, Any]		     =			[int(item) for item in user_input.split(',')]
   print(patience_sort(unsorted))







import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()