


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    ):

   '''simple docstring'''




   __A :      List[str]     		=		["image_processor", "tokenizer"]
   __A :      int     		=		"BlipImageProcessor"
   __A :      Tuple     		=		("BertTokenizer", "BertTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				False
     super().__init__(__A						,				__A )
     lowerCamelCase :					Dict    =				self.image_processor






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






     """simple docstring"""





     if images is None and text is None:
       raise ValueError("You have to specify either images or text." )

     # Get only text
     if images is None:
       lowerCamelCase :					Optional[int]    =				self.tokenizer
       lowerCamelCase :					Any    =				self.tokenizer(
           text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_token_type_ids=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
       return text_encoding

     # add pixel_values
     lowerCamelCase :					int    =				self.image_processor(__A						,				return_tensors=__A )

     if text is not None:
       lowerCamelCase :					Optional[int]    =				self.tokenizer(
           text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_token_type_ids=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     else:
       lowerCamelCase :					List[str]    =				None

     if text_encoding is not None:
       encoding_image_processor.update(__A )

     return encoding_image_processor






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





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







def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

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

  return total


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



