





'''simple docstring'''






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   _lowercase						(							a				):
   _UpperCamelCase       		=   ["""image_processor""", """tokenizer"""]
   _UpperCamelCase       		=   """BridgeTowerImageProcessor"""
   _UpperCamelCase       		=   ("""RobertaTokenizer""", """RobertaTokenizerFast""")


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase    ,   _UpperCAmelCase					)


   def __call__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = 0    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = False    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = None    ,   **_UpperCAmelCase    ,   ):
       A       :       Tuple							=						self.tokenizer(
           text=_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase    ,   padding=_UpperCAmelCase    ,   truncation=_UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   stride=_UpperCAmelCase    ,   pad_to_multiple_of=_UpperCAmelCase    ,   return_token_type_ids=_UpperCAmelCase    ,   return_attention_mask=_UpperCAmelCase    ,   return_overflowing_tokens=_UpperCAmelCase    ,   return_special_tokens_mask=_UpperCAmelCase    ,   return_offsets_mapping=_UpperCAmelCase    ,   return_length=_UpperCAmelCase    ,   verbose=_UpperCAmelCase    ,   return_tensors=_UpperCAmelCase    ,   **_UpperCAmelCase    ,   )
       # add pixel_values + pixel_mask
       A       :       Tuple							=						self.image_processor(
           _UpperCAmelCase    ,   return_tensors=_UpperCAmelCase    ,   do_normalize=_UpperCAmelCase    ,   do_center_crop=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       encoding.update(_UpperCAmelCase					)

       return encoding


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.batch_decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   @property
   def 					snake_case				( self					):
       A       :       int							=						self.tokenizer.model_input_names
       A       :       Optional[int]							=						self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)







'''simple docstring'''






from __future__ import annotations

import math
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[int]:

    if num <= 0:
        A       :       str							=						F'''{num}: Invalid input, please enter a positive integer.'''
        raise ValueError(UpperCamelCase__		)

    A       :       int							=						[True] * (num + 1)
    A       :       Dict							=						[]
    A       :       str							=						2
    A       :       Any							=						int(math.sqrt(UpperCamelCase__		)		)

    while start <= end:
        # If start is a prime
        if sieve[start] is True:
            prime.append(UpperCamelCase__		)

            # Set multiples of start be False
            for i in range(start * start    ,       num + 1    ,       UpperCamelCase__		):
                if sieve[i] is True:
                    A       :       Union[str, Any]							=						False

        start += 1

    for j in range(end + 1    ,       num + 1		):
        if sieve[j] is True:
            prime.append(UpperCamelCase__		)

    return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
