





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
    import torch

class    lowerCAmelCase							(					TensorFormatter[Mapping, """torch.Tensor""", Mapping]			):
 def __init__(      self  :      str			,     UpperCAmelCase  :      str=None			,     **UpperCAmelCase  :      int	)			->	Optional[Any]:
     super().__init__(features=UpperCAmelCase	)
     lowerCamelCase__    :      int					 =		torch_tensor_kwargs
     import torch  # noqa import torch at initialization
 def      A_	(      self  :      Union[str, Any]			,     UpperCAmelCase  :      List[Any]	)			->	Optional[Any]:
     import torch

     if isinstance(UpperCAmelCase			,     UpperCAmelCase	) and column:
         if all(
             isinstance(UpperCAmelCase			,     torch.Tensor	) and x.shape == column[0].shape and x.dtype == column[0].dtype
             for x in column	):
             return torch.stack(UpperCAmelCase	)
     return column
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Dict	)			->	List[Any]:
     import torch

     if isinstance(UpperCAmelCase			,     (str, bytes, type(UpperCAmelCase	))	):
         return value
     elif isinstance(UpperCAmelCase			,     (np.character, np.ndarray)	) and np.issubdtype(value.dtype			,     np.character	):
         return value.tolist()

     lowerCamelCase__    :      Union[str, Any]					 =		{}

     if isinstance(UpperCAmelCase			,     (np.number, np.ndarray)	) and np.issubdtype(value.dtype			,     np.integer	):
         lowerCamelCase__    :      Optional[Any]					 =		{'dtype': torch.intaa}
     elif isinstance(UpperCAmelCase			,     (np.number, np.ndarray)	) and np.issubdtype(value.dtype			,     np.floating	):
         lowerCamelCase__    :      List[Any]					 =		{'dtype': torch.floataa}
     elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(UpperCAmelCase			,     PIL.Image.Image	):
             lowerCamelCase__    :      List[str]					 =		np.asarray(UpperCAmelCase	)
     return torch.tensor(UpperCAmelCase			,     **{**default_dtype, **self.torch_tensor_kwargs}	)
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Tuple	)			->	Optional[Any]:
     import torch

     # support for torch, tf, jax etc.
     if hasattr(UpperCAmelCase			,     '__array__'	) and not isinstance(UpperCAmelCase			,     torch.Tensor	):
         lowerCamelCase__    :      Optional[int]					 =		data_struct.__array__()
     # support for nested types like struct of list of struct
     if isinstance(UpperCAmelCase			,     np.ndarray	):
         if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
             return self._consolidate([self.recursive_tensorize(UpperCAmelCase	) for substruct in data_struct]	)
     elif isinstance(UpperCAmelCase			,     (list, tuple)	):
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase	) for substruct in data_struct]	)
     return self._tensorize(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      dict	)			->	List[str]:
     return map_nested(self._recursive_tensorize			,     UpperCAmelCase			,     map_list=UpperCAmelCase	)
 def      A_	(      self  :      Tuple			,     UpperCAmelCase  :      pa.Table	)			->	Mapping:
     lowerCamelCase__    :      Tuple					 =		self.numpy_arrow_extractor().extract_row(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		self.python_features_decoder.decode_row(UpperCAmelCase	)
     return self.recursive_tensorize(UpperCAmelCase	)
 def      A_	(      self  :      List[str]			,     UpperCAmelCase  :      pa.Table	)			->	"torch.Tensor":
     lowerCamelCase__    :      Optional[int]					 =		self.numpy_arrow_extractor().extract_column(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		self.python_features_decoder.decode_column(UpperCAmelCase			,     pa_table.column_names[0]	)
     lowerCamelCase__    :      int					 =		self.recursive_tensorize(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		self._consolidate(UpperCAmelCase	)
     return column







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







"""simple docstring"""




from __future__ import annotations

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

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

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


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


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


