





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  int							  =   """funnel"""
     __A	:  Union[str, Any]							  =   {
         """hidden_size""": """d_model""",
         """num_attention_heads""": """n_head""",
     }





     def __init__(     self				,							_UpperCamelCase=30522				,							_UpperCamelCase=[4, 4, 4]				,							_UpperCamelCase=None				,							_UpperCamelCase=2				,							_UpperCamelCase=768				,							_UpperCamelCase=12				,							_UpperCamelCase=64				,							_UpperCamelCase=3072				,							_UpperCamelCase="gelu_new"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.1				,							_UpperCamelCase=None				,							_UpperCamelCase=1e-9				,							_UpperCamelCase="mean"				,							_UpperCamelCase="relative_shift"				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   block_sizes
        _UpperCAmelCase           =   [1] * len(_UpperCamelCase					) if block_repeats is None else block_repeats
        assert len(_UpperCamelCase					) == len(
            self.block_repeats					), "`block_sizes` and `block_repeats` should have the same length."
        _UpperCAmelCase           =   num_decoder_layers
        _UpperCAmelCase           =   d_model
        _UpperCAmelCase           =   n_head
        _UpperCAmelCase           =   d_head
        _UpperCAmelCase           =   d_inner
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout
        _UpperCAmelCase           =   attention_dropout
        _UpperCAmelCase           =   activation_dropout
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   initializer_std
        _UpperCAmelCase           =   layer_norm_eps
        assert pooling_type in [
            "mean",
            "max",
        ], f'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
        _UpperCAmelCase           =   pooling_type
        assert attention_type in [
            "relative_shift",
            "factorized",
        ], f'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
        _UpperCAmelCase           =   attention_type
        _UpperCAmelCase           =   separate_cls
        _UpperCAmelCase           =   truncate_seq
        _UpperCAmelCase           =   pool_q_only

        super().__init__(**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        return sum(self.block_sizes					)





     @num_hidden_layers.setter
     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        raise NotImplementedError(
            '''This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`.'''					)





     @property
     def 		UpperCamelCase(     self					):
        return len(self.block_sizes					)





     @num_blocks.setter
     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        raise NotImplementedError('''This model does not support the setting of `num_blocks`. Please set `block_sizes`.'''					)






def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _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				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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