





"""simple docstring"""




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


A   : List[str]			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''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   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   '''funnel'''
			__lowerCamelCase     :	Any          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''n_head''',
			}






			def __init__(      self       :   Optional[Any]							,     __lowerCAmelCase       :   List[Any]=3_05_22							,     __lowerCAmelCase       :   Dict=[4, 4, 4]							,     __lowerCAmelCase       :   Any=None							,     __lowerCAmelCase       :   Tuple=2							,     __lowerCAmelCase       :   Optional[Any]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Tuple=64							,     __lowerCAmelCase       :   List[str]=30_72							,     __lowerCAmelCase       :   List[Any]="gelu_new"							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   List[str]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.1							,     __lowerCAmelCase       :   str=None							,     __lowerCAmelCase       :   List[str]=1e-9							,     __lowerCAmelCase       :   Optional[Any]="mean"							,     __lowerCAmelCase       :   Dict="relative_shift"							,     __lowerCAmelCase       :   List[Any]=True							,     __lowerCAmelCase       :   str=True							,     __lowerCAmelCase       :   Optional[int]=True							,     **__lowerCAmelCase       :   Any							,     )     ->     int:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       block_sizes
					A__  				=       [1] * len(__lowerCAmelCase       ) if block_repeats is None else block_repeats
					assert len(__lowerCAmelCase       ) == len(
					    self.block_repeats       ), "`block_sizes` and `block_repeats` should have the same length."
					A__  				=       num_decoder_layers
					A__  				=       d_model
					A__  				=       n_head
					A__  				=       d_head
					A__  				=       d_inner
					A__  				=       hidden_act
					A__  				=       hidden_dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       initializer_range
					A__  				=       initializer_std
					A__  				=       layer_norm_eps
					assert pooling_type in [
					    "mean",
					    "max",
					], f'Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'
					A__  				=       pooling_type
					assert attention_type in [
					    "relative_shift",
					    "factorized",
					], f'Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'
					A__  				=       attention_type
					A__  				=       separate_cls
					A__  				=       truncate_seq
					A__  				=       pool_q_only

					super().__init__(**__lowerCAmelCase       )






			@property
			def        a_	(      self       :   int       )     ->     Optional[int]:







					"""simple docstring"""






					return sum(self.block_sizes       )






			@num_hidden_layers.setter
			def        a_	(      self       :   int							,     __lowerCAmelCase       :   str       )     ->     List[Any]:







					"""simple docstring"""






					raise NotImplementedError(
					    """This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."""       )






			@property
			def        a_	(      self       :   Tuple       )     ->     Dict:







					"""simple docstring"""






					return len(self.block_sizes       )







			@num_blocks.setter
			def        a_	(      self       :   Union[str, Any]							,     __lowerCAmelCase       :   List[Any]       )     ->     List[str]:







					"""simple docstring"""






					raise NotImplementedError("""This model does not support the setting of `num_blocks`. Please set `block_sizes`."""       )










def        __lowerCamelCase (							__a       :int = 3   ,					__a       :int = 7   ,					__a       :int = 1_0_0_0_0_0_0   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       1

		for current_denominator in range(1   ,					limit + 1   ):
				A__  				=       current_denominator * numerator // denominator
				if current_denominator % denominator == 0:
						current_numerator -= 1
				if current_numerator * max_denominator > current_denominator * max_numerator:
						A__  				=       current_numerator
						A__  				=       current_denominator
		return max_numerator


if __name__ == "__main__":
					print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))



