






import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        _UpperCAmelCase						(		_UpperCamelCase			):
    __lowerCamelCase:   Dict		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   int		       =    'FlavaImageProcessor'
    __lowerCamelCase:   Optional[Any]		       =    ('BertTokenizer', 'BertTokenizerFast')




    def __init__(			self  :       int  ,     a  :       Optional[Any]=None  ,     a  :       Optional[Any]=None  ,     **a  :       Dict			):






       '''simple docstring'''






       lowercase_	:    str           =     None
       if "feature_extractor" in kwargs:
          warnings.warn(
              "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
              " instead."  ,     _UpperCAmelCase  ,     )
          lowercase_	:    Optional[Any]           =     kwargs.pop("feature_extractor"			)

       lowercase_	:    Tuple           =     image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
          raise ValueError("You need to specify an `image_processor`."			)
       if tokenizer is None:
          raise ValueError("You need to specify a `tokenizer`."			)

       super().__init__(_UpperCAmelCase  ,     _UpperCAmelCase			)
       lowercase_	:    Optional[Any]           =     self.image_processor




    def __call__(			self  :       Optional[Any]  ,     a  :       Tuple = None  ,     a  :       int = None  ,     a  :       List[str] = True  ,     a  :       Optional[int] = False  ,     a  :       List[Any] = False  ,     a  :       str = None  ,     a  :       Union[str, Any] = 0  ,     a  :       Tuple = None  ,     a  :       Tuple = None  ,     a  :       Union[str, Any] = None  ,     a  :       List[Any] = None  ,     a  :       str = None  ,     a  :       Dict = False  ,     a  :       Optional[int] = False  ,     a  :       List[str] = False  ,     a  :       Optional[int] = False  ,     a  :       Optional[int] = True  ,     a  :       str = None  ,     **a  :       Tuple  ,     ):






       '''simple docstring'''






       if text is None and images is None:
          raise ValueError("You have to specify either text or images. Both cannot be none."			)

       if text is not None:
          lowercase_	:    List[str]           =     self.tokenizer(
              text=_UpperCAmelCase  ,     add_special_tokens=_UpperCAmelCase  ,     padding=_UpperCAmelCase  ,     truncation=_UpperCAmelCase  ,     max_length=_UpperCAmelCase  ,     stride=_UpperCAmelCase  ,     pad_to_multiple_of=_UpperCAmelCase  ,     return_token_type_ids=_UpperCAmelCase  ,     return_attention_mask=_UpperCAmelCase  ,     return_overflowing_tokens=_UpperCAmelCase  ,     return_special_tokens_mask=_UpperCAmelCase  ,     return_offsets_mapping=_UpperCAmelCase  ,     return_length=_UpperCAmelCase  ,     verbose=_UpperCAmelCase  ,     return_tensors=_UpperCAmelCase  ,     **_UpperCAmelCase  ,     )
       if images is not None:
          lowercase_	:    Any           =     self.image_processor(
              _UpperCAmelCase  ,     return_image_mask=_UpperCAmelCase  ,     return_codebook_pixels=_UpperCAmelCase  ,     return_tensors=_UpperCAmelCase  ,     **_UpperCAmelCase  ,     )

       if text is not None and images is not None:
          encoding.update(_UpperCAmelCase			)
          return encoding
       elif text is not None:
          return encoding
       else:
          return BatchEncoding(data=dict(**_UpperCAmelCase			)  ,     tensor_type=_UpperCAmelCase			)




    def 				lowerCAmelCase__				(			self  :       Optional[int]  ,     *a  :       Dict  ,     **a  :       Dict			):






       '''simple docstring'''






       return self.tokenizer.batch_decode(*_UpperCAmelCase  ,     **_UpperCAmelCase			)




    def 				lowerCAmelCase__				(			self  :       List[str]  ,     *a  :       List[Any]  ,     **a  :       Optional[int]			):






       '''simple docstring'''






       return self.tokenizer.decode(*_UpperCAmelCase  ,     **_UpperCAmelCase			)




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.tokenizer.model_input_names
       lowercase_	:    int           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,     _UpperCAmelCase  ,     )
       return self.image_processor_class





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






       '''simple docstring'''






       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,     _UpperCAmelCase  ,     )
       return self.image_processor












'''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)




