


'''simple docstring'''

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      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["image_processor", "tokenizer"]
  _lowerCamelCase		    =  "BlipImageProcessor"
  _lowerCamelCase		    =  "AutoTokenizer"






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  False
   super().__init__(__A			,  __A     )
   lowerCamelCase_      =  self.image_processor






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







   """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_      =  self.tokenizer
    lowerCamelCase_      =  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_      =  self.image_processor(__A			,  return_tensors=__A     )

   if text is not None:
    lowerCamelCase_      =  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_      =  None

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

   return encoding_image_processor






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*__A			,  **__A     )






  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   return self.tokenizer.decode(*__A			,  **__A     )







  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer.model_input_names
   lowerCamelCase_      =  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() = }''')



