



'''simple docstring'''







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       lowerCamelCase_							(	TensorFormatter[Mapping, np.ndarray, Mapping]  ):






   '''simple docstring'''

   def __init__(  self     ,						__UpperCamelCase=None     ,						**__UpperCamelCase ):
       super().__init__(features=__UpperCamelCase )
       UpperCamelCase__      =			np_array_kwargs


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       if isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           if column and all(
               isinstance(__UpperCamelCase     ,						np.ndarray ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
               return np.stack(__UpperCamelCase )
           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(__UpperCamelCase )     ,						dtype=__UpperCamelCase )
               UpperCamelCase__      =			column
               return out
       return column


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       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
       elif isinstance(__UpperCamelCase     ,						np.number ):
           return value

       UpperCamelCase__      =			{}

       if isinstance(__UpperCamelCase     ,						np.ndarray ) and np.issubdtype(value.dtype     ,						np.integer ):
           UpperCamelCase__      =			{"dtype": np.intaa}
       elif isinstance(__UpperCamelCase     ,						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(__UpperCamelCase     ,						PIL.Image.Image ):
               return np.asarray(__UpperCamelCase     ,						**self.np_array_kwargs )

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       # support for torch, tf, jax etc.
       if config.TORCH_AVAILABLE and "torch" in sys.modules:
           import torch

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


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       return map_nested(self._recursive_tensorize     ,						__UpperCamelCase     ,						map_list=__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_row(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_row(__UpperCamelCase )
       return self.recursive_tensorize(__UpperCamelCase )


   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_column(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_column(__UpperCamelCase     ,						pa_table.column_names[0] )
       UpperCamelCase__      =			self.recursive_tensorize(__UpperCamelCase )
       UpperCamelCase__      =			self._consolidate(__UpperCamelCase )
       return column







   def 	_UpperCamelCase       (  self     ,						__UpperCamelCase ):
       UpperCamelCase__      =			self.numpy_arrow_extractor().extract_batch(__UpperCamelCase )
       UpperCamelCase__      =			self.python_features_decoder.decode_batch(__UpperCamelCase )
       UpperCamelCase__      =			self.recursive_tensorize(__UpperCamelCase )
       for column_name in batch:
           UpperCamelCase__      =			self._consolidate(batch[column_name] )
       return batch









'''simple docstring'''







def 						snake_case__ (lowerCAmelCase_	):
    for i, _ in enumerate(lowerCAmelCase_	):
        if (i % 2 == 1) == (nums[i - 1] > nums[i]):
            UpperCamelCase__    ,  UpperCamelCase__      =			nums[i], nums[i - 1]

    return nums


if __name__ == "__main__":
      print('Enter the array elements:')
      lowercase__     :  Optional[int]    					=			list(map(int, input().split()))
      print('The unsorted array is:')
      print(array)
      print('Array after Wiggle sort:')
      print(wiggle_sort(array))




