



"""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 				lowercase__       (   SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					['''image_processor''', '''tokenizer''']
       UpperCamelCase						=					'''BlipImageProcessor'''
       UpperCamelCase						=					('''BertTokenizer''', '''BertTokenizerFast''')



       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  Tuple   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							False
             super().__init__(_UpperCAmelCase		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							self.image_processor



       def __call__(							self      :  Optional[int]		,    _UpperCAmelCase      :  ImageInput = None		,    _UpperCAmelCase      :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  Union[bool, str, PaddingStrategy] = False		,    _UpperCAmelCase      :  Union[bool, str, TruncationStrategy] = None		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  int = 0		,    _UpperCAmelCase      :  Optional[int] = None		,    _UpperCAmelCase      :  Optional[bool] = None		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = False		,    _UpperCAmelCase      :  bool = True		,    _UpperCAmelCase      :  Optional[Union[str, TensorType]] = None		,    **_UpperCAmelCase      :  Dict		,    )			->						BatchEncoding:


             '''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:
                   UpperCAmelCase_      			=							self.tokenizer
                   UpperCAmelCase_      			=							self.tokenizer(
                       text=_UpperCAmelCase		,    add_special_tokens=_UpperCAmelCase		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=_UpperCAmelCase		,    stride=_UpperCAmelCase		,    pad_to_multiple_of=_UpperCAmelCase		,    return_attention_mask=_UpperCAmelCase		,    return_overflowing_tokens=_UpperCAmelCase		,    return_special_tokens_mask=_UpperCAmelCase		,    return_offsets_mapping=_UpperCAmelCase		,    return_token_type_ids=_UpperCAmelCase		,    return_length=_UpperCAmelCase		,    verbose=_UpperCAmelCase		,    return_tensors=_UpperCAmelCase		,    **_UpperCAmelCase		,    )
                   return text_encoding

             # add pixel_values
             UpperCAmelCase_      			=							self.image_processor(_UpperCAmelCase		,    return_tensors=_UpperCAmelCase   )

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

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

             return encoding_image_processor



       def 					lowercase__    (							self      :  Optional[int]		,    *_UpperCAmelCase      :  List[str]		,    **_UpperCAmelCase      :  str   )			->						Dict:


             '''simple docstring'''




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



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


             '''simple docstring'''




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







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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer.model_input_names
             UpperCAmelCase_      			=							self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )









"""simple docstring"""





def        a__     (				lowerCAmelCase__ = 2000000				):
      UpperCAmelCase_      			=							[0 for i in range(n + 1				)]
      UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							1

      for i in range(2     ,      int(n**0.5				) + 1				):
            if primality_list[i] == 0:
                  for j in range(i * i     ,      n + 1     ,      lowerCAmelCase__				):
                        UpperCAmelCase_      			=							1
      UpperCAmelCase_      			=							0
      for i in range(lowerCAmelCase__				):
            if primality_list[i] == 0:
                  sum_of_primes += i
      return sum_of_primes


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




