
"""simple docstring"""
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			,    a_=None			,    **a_ )				->				Tuple:
								super().__init__(features=snake_case__ )
								_UpperCAmelCase			=    torch_tensor_kwargs
								import torch  # noqa import torch at initialization



							def    _a (   self			,    a_ )				->				Any:
								import torch

								if isinstance(snake_case__			,    snake_case__ ) and column:
									if all(
									    isinstance(snake_case__			,    torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column ):
										return torch.stack(snake_case__ )
								return column



							def    _a (   self			,    a_ )				->				int:
								import torch

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

								_UpperCAmelCase			=    {}

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

									if isinstance(snake_case__			,    PIL.Image.Image ):
										_UpperCAmelCase			=    np.asarray(snake_case__ )
								return torch.tensor(snake_case__			,    **{**default_dtype, **self.torch_tensor_kwargs} )



							def    _a (   self			,    a_ )				->				Optional[Any]:
								import torch

								# support for torch, tf, jax etc.
								if hasattr(snake_case__			,    "__array__" ) and not isinstance(snake_case__			,    torch.Tensor ):
									_UpperCAmelCase			=    data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(snake_case__			,    np.ndarray ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
										return self._consolidate([self.recursive_tensorize(snake_case__ ) for substruct in data_struct] )
								elif isinstance(snake_case__			,    (list, tuple) ):
									return self._consolidate([self.recursive_tensorize(snake_case__ ) for substruct in data_struct] )
								return self._tensorize(snake_case__ )



							def    _a (   self			,    a_ )				->				Dict:
								return map_nested(self._recursive_tensorize			,    snake_case__			,    map_list=snake_case__ )



							def    _a (   self			,    a_ )				->				Dict:
								_UpperCAmelCase			=    self.numpy_arrow_extractor().extract_row(snake_case__ )
								_UpperCAmelCase			=    self.python_features_decoder.decode_row(snake_case__ )
								return self.recursive_tensorize(snake_case__ )



							def    _a (   self			,    a_ )				->				List[str]:
								_UpperCAmelCase			=    self.numpy_arrow_extractor().extract_column(snake_case__ )
								_UpperCAmelCase			=    self.python_features_decoder.decode_column(snake_case__			,    pa_table.column_names[0] )
								_UpperCAmelCase			=    self.recursive_tensorize(snake_case__ )
								_UpperCAmelCase			=    self._consolidate(snake_case__ )
								return column



							def    _a (   self			,    a_ )				->				List[str]:
								_UpperCAmelCase			=    self.numpy_arrow_extractor().extract_batch(snake_case__ )
								_UpperCAmelCase			=    self.python_features_decoder.decode_batch(snake_case__ )
								_UpperCAmelCase			=    self.recursive_tensorize(snake_case__ )
								for column_name in batch:
									_UpperCAmelCase			=    self._consolidate(batch[column_name] )
								return batch












"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : list				)->		list:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							False
     while is_sorted is False:  # Until all the indices are traversed keep looping
          UpperCAmelCase__						:    List[Any]							=							True
          for i in range(0			,					len(snake_case				) - 1			,					2				):  # iterating over all even indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    int							=							False

          for i in range(1			,					len(snake_case				) - 1			,					2				):  # iterating over all odd indices
               if input_list[i] > input_list[i + 1]:
                    UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							input_list[i + 1], input_list[i]
                    # swapping if elements not in order
                    UpperCAmelCase__						:    str							=							False
     return input_list


if __name__ == "__main__":
 print("""Enter list to be sorted""")
 _lowerCAmelCase	:	List[str]				=      [int(x) for x in input().split()]
 # inputing elements of the list in one line
 _lowerCAmelCase	:	Optional[int]				=      odd_even_sort(input_list)
 print("""The sorted list is""")
 print(sorted_list)


