
'''simple docstring'''





from __future__ import annotations

def   A			(							A_				:       List[Any]     ,				A_				:       List[Any]							):
						snake_case							:  Union[str, Any]			=     0
						snake_case							:  Any			=     len(A_							) - 1
						while left <= right:
												snake_case							:  str			=     (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								snake_case							:  Any			=     middle + 1
																		break
												elif sorted_list[middle] < item:
																		snake_case							:  List[Any]			=     middle + 1
												else:
																		snake_case							:  str			=     middle - 1
						sorted_list.insert(A_     ,				A_							)
						return sorted_list

def   A			(							A_				:       Dict     ,				A_				:       Optional[int]							):
						snake_case							:  Any			=     []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0							)							)
												else:
																		result.append(right.pop(0							)							)
						return result + left + right

def   A			(							A_				:       Any							):
						snake_case							:  Any			=     len(A_							)
						if length <= 1:
												return list_ad
						snake_case							:  List[str]			=     length // 2
						return merge(sortlist_ad(list_ad[:middle]							)     ,				sortlist_ad(list_ad[middle:]							)							)

def   A			(							A_				:       list[int]							):

						if len(A_							) <= 1:
												return collection
						snake_case							:  List[Any]			=     []
						snake_case							:  Union[str, Any]			=     False
						for i in range(0     ,				len(A_							)     ,				2							):
												if i == len(A_							) - 1:
																		snake_case							:  Tuple			=     True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]							)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]							)
						snake_case							:  Dict			=     sortlist_ad(A_							)
						snake_case							:  List[Any]			=     [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]							)
						if has_last_odd_item:
												snake_case							:  Dict			=     collection[-1]
												snake_case							:  Optional[Any]			=     binary_search_insertion(A_     ,				A_							)
						snake_case							:  Union[str, Any]			=     False
						for i in range(len(A_							) - 1							):
												if result[i] == collection[-1] and has_last_odd_item:
																		snake_case							:  Dict			=     True
												snake_case							:  Any			=     sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		snake_case							:  Any			=     result[: i + 2] + binary_search_insertion(result[i + 2 :]     ,				A_							)
												else:
																		snake_case							:  Optional[int]			=     result[: i + 1] + binary_search_insertion(result[i + 1 :]     ,				A_							)

						return result


if __name__ == "__main__":
						UpperCAmelCase            = input("Enter numbers separated by a comma:\n").strip()
						UpperCAmelCase            = [int(item) for item in user_input.split(",")]
						print(merge_insertion_sort(unsorted))




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



