


'''simple docstring'''







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


A				:					List[Any]    						=			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 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   "funnel"
    a													=   {
        "hidden_size": "d_model",
        "num_attention_heads": "n_head",
    }


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

           super().__init__(**SCREAMING_SNAKE_CASE)


    @property
    def        A   (  self      :							Any):
           return sum(self.block_sizes)


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


    @property
    def        A   (  self      :							List[Any]):
           return len(self.block_sizes)


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










'''simple docstring'''







def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations(lowerCamelCase					:      int       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              return sum(count_of_possible_combinations(target - item       ) for item in array       )

       return count_of_possible_combinations(lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):

       def count_of_possible_combinations_with_dp_array(
           lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]       ) -> int:
              if target < 0:
                     return 0
              if target == 0:
                     return 1
              if dp_array[target] != -1:
                     return dp_array[target]
              _A					:     Optional[Any]     = sum(
                  count_of_possible_combinations_with_dp_array(target - item  ,lowerCamelCase       )
                  for item in array       )
              _A					:     List[str]     = answer
              return answer

       _A					:     Optional[int]     = [-1] * (target + 1)
       return count_of_possible_combinations_with_dp_array(lowerCamelCase  ,lowerCamelCase       )






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      list[int]  ,lowerCamelCase					:      int       ):
       _A					:     Dict     = [0] * (target + 1)
       _A					:     List[str]     = 1

       for i in range(1  ,target + 1       ):
              for j in range(lowerCamelCase       ):
                     if i - array[j] >= 0:
                            dp_array[i] += dp_array[i - array[j]]

       return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   A				:					Dict    						=			3
   A				:					Union[str, Any]    						=			5
   A				:					Union[str, Any]    						=			[1, 2, 5]
   print(combination_sum_iv(n, array, target))






