






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCamelCase__ =			logging.get_logger(__name__)


UpperCamelCase__ =			r'\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n'


class        _UpperCAmelCase						(		snake_case			):




    @add_start_docstrings(a			)
    def __call__(			self  :       Any  ,     a  :       torch.LongTensor  ,     a  :       torch.FloatTensor  ,     **a  :       Dict			):






       '''simple docstring'''






       raise NotImplementedError("StoppingCriteria needs to be subclassed"			)


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       str  ,     a  :       int  ,     a  :       Optional[int] = None			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     max_length
       lowercase_	:    List[Any]           =     max_position_embeddings





    @add_start_docstrings(a			)
    def __call__(			self  :       Union[str, Any]  ,     a  :       torch.LongTensor  ,     a  :       torch.FloatTensor  ,     **a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     input_ids.shape[-1]
       lowercase_	:    str           =     cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
          logger.warning_once(
              "This is a friendly reminder - the current text generation call will exceed the model's predefined "
              f"""maximum length ({self.max_position_embeddings}). Depending on the model, you may observe """
              "exceptions, performance degradation, or nothing at all."			)
       return is_done


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       List[Any]  ,     a  :       int  ,     a  :       int			):






       '''simple docstring'''






       warnings.warn(
           "The class `MaxNewTokensCriteria` is deprecated. "
           f"""Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` """
           "with `max_length = start_length + max_new_tokens` instead."  ,     a  ,     )
       lowercase_	:    List[Any]           =     start_length
       lowercase_	:    Tuple           =     max_new_tokens
       lowercase_	:    str           =     start_length + max_new_tokens





    @add_start_docstrings(a			)
    def __call__(			self  :       int  ,     a  :       torch.LongTensor  ,     a  :       torch.FloatTensor  ,     **a  :       Optional[int]			):






       '''simple docstring'''






       return input_ids.shape[-1] >= self.max_length


class        _UpperCAmelCase						(		snake_case			):




    def __init__(			self  :       Optional[int]  ,     a  :       float  ,     a  :       Optional[float] = None			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     max_time
       lowercase_	:    Optional[int]           =     time.time() if initial_timestamp is None else initial_timestamp





    @add_start_docstrings(a			)
    def __call__(			self  :       int  ,     a  :       torch.LongTensor  ,     a  :       torch.FloatTensor  ,     **a  :       Optional[Any]			):






       '''simple docstring'''






       return time.time() - self.initial_timestamp > self.max_time


class        _UpperCAmelCase						(		snake_case			):




    @add_start_docstrings(a			)
    def __call__(			self  :       List[Any]  ,     a  :       torch.LongTensor  ,     a  :       torch.FloatTensor  ,     **a  :       List[Any]			):






       '''simple docstring'''






       return any(criteria(a  ,     a			) for criteria in self			)





    @property
    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       for stopping_criterium in self:
          if isinstance(a  ,     a			):
             return stopping_criterium.max_length
          elif isinstance(a  ,     a			):
             return stopping_criterium.max_length
       return None




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     stopping_criteria.max_length
   lowercase_	:    List[str]           =     deepcopy(_UpperCamelCase							)
   if stopping_max_length is not None and stopping_max_length != max_length:
      warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"						,		_UpperCamelCase							)
   elif stopping_max_length is None:
      new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCamelCase							)							)
   return new_stopping_criteria












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

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


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




