






'''simple docstring'''






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class a_    :







   def __init__(self					,   __a=2					,   __a=3					,   __a=6_4					,   __a=None)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= np.random.default_rng(__a)
      __snake_case				:      Dict     							= length
      __snake_case				:      str     							= rng.normal(size=(length,)).astype(np.floataa)
      __snake_case				:      int     							= a * self.x + b + rng.normal(scale=0.1					,   size=(length,)).astype(np.floataa)







   def __len__(self)			->      Union[str, Any]:
      """simple docstring"""






      return self.length







   def __getitem__(self					,   __a)			->      Optional[int]:
      """simple docstring"""






      return {"x": self.x[i], "y": self.y[i]}




class a_    (    torch.nn.Module							):







   def __init__(self					,   __a=0					,   __a=0					,   __a=False)			->      Optional[Any]:
      """simple docstring"""






      super().__init__()
      __snake_case				:      Optional[Any]     							= torch.nn.Parameter(torch.tensor([2, 3]).float())
      __snake_case				:      List[Any]     							= torch.nn.Parameter(torch.tensor([2, 3]).float())
      __snake_case				:      str     							= True







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=None)			->      Optional[Any]:
      """simple docstring"""






      if self.first_batch:
         print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
         __snake_case				:      Optional[Any]     							= False
      return x * self.a[0] + self.b[0]




class a_    (    torch.nn.Module							):







   def __init__(self					,   __a=0					,   __a=0					,   __a=False)			->      List[str]:
      """simple docstring"""






      super().__init__()
      __snake_case				:      int     							= torch.nn.Parameter(torch.tensor(__a).float())
      __snake_case				:      Tuple     							= torch.nn.Parameter(torch.tensor(__a).float())
      __snake_case				:      List[str]     							= True







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=None)			->      List[Any]:
      """simple docstring"""






      if self.first_batch:
         print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
         __snake_case				:      Dict     							= False
      return x * self.a + self.b




def        _SCREAMING_SNAKE_CASE  (       A  :       Optional[int]  ,		A  :       int = 16  )    -> int:







   """simple docstring"""
   from datasets import load_dataset
   from transformers import AutoTokenizer

   __snake_case				:      List[str]     							= AutoTokenizer.from_pretrained('bert-base-cased'  )
   __snake_case				:      Optional[Any]     							= {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
   __snake_case				:      Tuple     							= load_dataset('csv'  ,		data_files=A  )
   __snake_case				:      Optional[int]     							= datasets['train'].unique('label'  )

   __snake_case				:      Optional[Any]     							= {v: i for i, v in enumerate(A  )}

   def tokenize_function(A  :       List[Any]  ):
      # max_length=None => use the model max length (it's actually the default)
      __snake_case				:      Tuple     							= tokenizer(
          examples['sentence1']  ,		examples['sentence2']  ,		truncation=A  ,		max_length=A  ,		padding='max_length'  )
      if "label" in examples:
         __snake_case				:      Tuple     							= [label_to_id[l] for l in examples['label']]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __snake_case				:      List[str]     							= datasets.map(
       A  ,		batched=A  ,		remove_columns=['sentence1', 'sentence2', 'label']  ,		)

   def collate_fn(A  :       Dict  ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(A  ,		padding='max_length'  ,		max_length=1_28  ,		return_tensors='pt'  )
      return tokenizer.pad(A  ,		padding='longest'  ,		return_tensors='pt'  )

   # Instantiate dataloaders.
   __snake_case				:      Tuple     							= DataLoader(tokenized_datasets['train']  ,		shuffle=A  ,		collate_fn=A  ,		batch_size=2  )
   __snake_case				:      Union[str, Any]     							= DataLoader(tokenized_datasets['validation']  ,		shuffle=A  ,		collate_fn=A  ,		batch_size=1  )

   return train_dataloader, eval_dataloader







'''simple docstring'''






def        _SCREAMING_SNAKE_CASE  (				A  :       list  )    -> list:







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

      for i in range(1  ,		len(A  ) - 1  ,		2  ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            __snake_case							,__snake_case				:      Tuple     							= input_list[i + 1], input_list[i]
            # swapping if elements not in order
            __snake_case				:      Any     							= False
   return input_list


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