import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class    A_ (       a_ ):
      _SCREAMING_SNAKE_CASE    				=			["""image_processor""", """tokenizer"""]
      _SCREAMING_SNAKE_CASE    				=			"""ViltImageProcessor"""
      _SCREAMING_SNAKE_CASE    				=			("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=None     ,							**__SCREAMING_SNAKE_CASE :      Dict       ):
       __a       =  None
       if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."     ,							__SCREAMING_SNAKE_CASE     ,							)
        __a       =  kwargs.pop("feature_extractor"       )

       __a       =  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__(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
       __a       =  self.image_processor

      def __call__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Union[bool, str, PaddingStrategy] = False     ,							__SCREAMING_SNAKE_CASE :      Union[bool, str, TruncationStrategy] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      int = 0     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = None     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[str, TensorType]] = None     ,							**__SCREAMING_SNAKE_CASE :      Dict     ,							):
       __a       =  self.tokenizer(
           text=__SCREAMING_SNAKE_CASE     ,							add_special_tokens=__SCREAMING_SNAKE_CASE     ,							padding=__SCREAMING_SNAKE_CASE     ,							truncation=__SCREAMING_SNAKE_CASE     ,							max_length=__SCREAMING_SNAKE_CASE     ,							stride=__SCREAMING_SNAKE_CASE     ,							pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,							return_token_type_ids=__SCREAMING_SNAKE_CASE     ,							return_attention_mask=__SCREAMING_SNAKE_CASE     ,							return_overflowing_tokens=__SCREAMING_SNAKE_CASE     ,							return_special_tokens_mask=__SCREAMING_SNAKE_CASE     ,							return_offsets_mapping=__SCREAMING_SNAKE_CASE     ,							return_length=__SCREAMING_SNAKE_CASE     ,							verbose=__SCREAMING_SNAKE_CASE     ,							return_tensors=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE     ,							)
       # add pixel_values + pixel_mask
       __a       =  self.image_processor(__SCREAMING_SNAKE_CASE     ,							return_tensors=__SCREAMING_SNAKE_CASE       )
       encoding.update(__SCREAMING_SNAKE_CASE       )

       return encoding

      def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							*__SCREAMING_SNAKE_CASE :      str     ,							**__SCREAMING_SNAKE_CASE :      str       ):
       return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

      def 		_UpperCAmelCase   (		self :      Optional[int]     ,							*__SCREAMING_SNAKE_CASE :      Any     ,							**__SCREAMING_SNAKE_CASE :      Optional[int]       ):
       return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

      @property
      def 		_UpperCAmelCase   (		self :      Dict       ):
       __a       =  self.tokenizer.model_input_names
       __a       =  self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

      @property
      def 		_UpperCAmelCase   (		self :      Any       ):
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__SCREAMING_SNAKE_CASE     ,							)
       return self.image_processor_class

      @property
      def 		_UpperCAmelCase   (		self :      Tuple       ):
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__SCREAMING_SNAKE_CASE     ,							)
       return self.image_processor



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


