



'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 				snake_case (__snake_case   ):
       lowerCAmelCase__  :Optional[Any]						   =					["image_processor", "tokenizer"]
       lowerCAmelCase__  :Dict						   =					"ChineseCLIPImageProcessor"
       lowerCAmelCase__  :Optional[int]						   =					("BertTokenizer", "BertTokenizerFast")



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,**UpperCAmelCase_      )  ->							str:
           lowercase__		       =						None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,_lowercase     ,)
               lowercase__		       =						kwargs.pop("feature_extractor"      )

           lowercase__		       =						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__(_lowercase     ,_lowercase      )
           lowercase__		       =						self.image_processor



       def __call__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,**UpperCAmelCase_      )  ->							str:

           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__		       =						self.tokenizer(_lowercase     ,return_tensors=_lowercase     ,**_lowercase      )

           if images is not None:
               lowercase__		       =						self.image_processor(_lowercase     ,return_tensors=_lowercase     ,**_lowercase      )

           if text is not None and images is not None:
               lowercase__		       =						image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowercase      )     ,tensor_type=_lowercase      )



       def 							_a      (				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Dict:
           return self.tokenizer.batch_decode(*_lowercase     ,**_lowercase      )



       def 							_a      (				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							int:
           return self.tokenizer.decode(*_lowercase     ,**_lowercase      )



       @property
       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.tokenizer.model_input_names
           lowercase__		       =						self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







       @property
       def 							_a      (				self      )  ->							Tuple:
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,_lowercase     ,)
           return self.image_processor_class









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						int = 4_000_000       ):





    '''simple docstring'''


    lowercase__		       =						[0, 1]
    lowercase__		       =						0
    while fib[i] <= n:
        fib.append(fib[i] + fib[i + 1]       )
        if fib[i + 2] > n:
            break
        i += 1
    lowercase__		       =						0
    for j in range(len(_snake_case       ) - 1       ):
        if fib[j] % 2 == 0:
            total += fib[j]

    return total


if __name__ == "__main__":
 print(f'''{solution() = }''')




