





'''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        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    ["""image_processor""", """tokenizer"""]
 __lowercase       		=    """BlipImageProcessor"""
 __lowercase       		=    ("""BertTokenizer""", """BertTokenizerFast""")
 def __init__(    self				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							)->							Any:
     A__    =      False
     super().__init__(lowercase_						,	lowercase_							)
     A__    =      self.image_processor
 def __call__(    self				:int						,	lowercase_				:ImageInput = None						,	lowercase_				:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						,	lowercase_				:bool = True						,	lowercase_				:Union[bool, str, PaddingStrategy] = False						,	lowercase_				:Union[bool, str, TruncationStrategy] = None						,	lowercase_				:Optional[int] = None						,	lowercase_				:int = 0						,	lowercase_				:Optional[int] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = False						,	lowercase_				:bool = True						,	lowercase_				:Optional[Union[str, TensorType]] = None						,	**lowercase_				:Dict						,	)->							BatchEncoding:
     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:
         A__    =      self.tokenizer
         A__    =      self.tokenizer(
             text=lowercase_						,	add_special_tokens=lowercase_						,	padding=lowercase_						,	truncation=lowercase_						,	max_length=lowercase_						,	stride=lowercase_						,	pad_to_multiple_of=lowercase_						,	return_attention_mask=lowercase_						,	return_overflowing_tokens=lowercase_						,	return_special_tokens_mask=lowercase_						,	return_offsets_mapping=lowercase_						,	return_token_type_ids=lowercase_						,	return_length=lowercase_						,	verbose=lowercase_						,	return_tensors=lowercase_						,	**lowercase_						,	)
         return text_encoding

     # add pixel_values
     A__    =      self.image_processor(lowercase_						,	return_tensors=lowercase_							)

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

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

     return encoding_image_processor
 def 					UpperCAmelCase_							(    self				:List[str]						,	*lowercase_				:Union[str, Any]						,	**lowercase_				:List[str]							)->							str:
     return self.tokenizer.batch_decode(*lowercase_						,	**lowercase_							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	*lowercase_				:Union[str, Any]						,	**lowercase_				:Optional[Any]							)->							int:
     return self.tokenizer.decode(*lowercase_						,	**lowercase_							)



 @property
 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     A__    =      self.tokenizer.model_input_names
     A__    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int = 1_00_00_00	):
    A__    =      set(range(3						,	_lowerCamelCase						,	2	)	)
    primes.add(2	)
    for p in range(3						,	_lowerCamelCase						,	2	):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p						,	_lowerCamelCase						,	_lowerCamelCase	)	)	)

    A__    =      [float(_lowerCamelCase	) for n in range(limit + 1	)]

    for p in primes:
        for n in range(_lowerCamelCase						,	limit + 1						,	_lowerCamelCase	):
            phi[n] *= 1 - 1 / p

    return int(sum(phi[2:]	)	)


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



