




def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[int] = 4_00_00_00	):


      '''simple docstring'''



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

      return total


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










'''simple docstring'''






import warnings

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




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

		def __init__(   self					:   int  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Dict=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

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

					__snake_case					:      List[str]							      =     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__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[int]:


					"""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:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if text is not None and images is not None:
								__snake_case					:      Union[str, Any]							      =     image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**__magic_name__			)  ,      tensor_type=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]  ,      *__magic_name__					:   List[Any]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   List[str]  ,      *__magic_name__					:   Tuple  ,      **__magic_name__					:   List[Any]			)					->    int:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

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


