





"""simple docstring"""






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 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  ["""image_processor""", """tokenizer"""]
       lowerCAmelCase__						 =  """FlavaImageProcessor"""
       lowerCAmelCase__						 =  ("""BertTokenizer""", """BertTokenizerFast""")






       def __init__(   self    ,  a__=None    ,  a__=None    ,  **a__     ):
              _UpperCAmelCase     =     None
              if "feature_extractor" in kwargs:
                     warnings.warn(
                         'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                         ' instead.'    ,  a__    ,  )
                     _UpperCAmelCase     =     kwargs.pop('feature_extractor'     )

              _UpperCAmelCase     =     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__(a__    ,  a__     )
              _UpperCAmelCase     =     self.image_processor






       def __call__(   self    ,  a__ = None    ,  a__ = None    ,  a__ = True    ,  a__ = False    ,  a__ = False    ,  a__ = None    ,  a__ = 0    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = True    ,  a__ = None    ,  **a__    ,  ):

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

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






       def 							__A (   self    ,  *a__    ,  **a__     ):
              return self.tokenizer.batch_decode(*a__    ,  **a__     )






       def 							__A (   self    ,  *a__    ,  **a__     ):
              return self.tokenizer.decode(*a__    ,  **a__     )






       @property
       def 							__A (   self     ):
              _UpperCAmelCase     =     self.tokenizer.model_input_names
              _UpperCAmelCase     =     self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






       @property
       def 							__A (   self     ):
              warnings.warn(
                  '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,  a__    ,  )
              return self.image_processor_class






       @property
       def 							__A (   self     ):
              warnings.warn(
                  '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,  a__    ,  )
              return self.image_processor













"""simple docstring"""






def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       list:






       """simple docstring"""




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

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


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






