





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


UpperCAmelCase						:   Optional[int]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   int			  =     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Dict					    =	'''switch_transformers'''
				UpperCamelCase    :  Dict					    =	['''past_key_values''']
				UpperCamelCase    :  Any					    =	{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



				def __init__(			self     ,					_A=32128     ,					_A=768     ,					_A=64     ,					_A=2048     ,					_A=64     ,					_A=12     ,					_A=3     ,					_A=12     ,					_A=3     ,					_A=12     ,					_A=8     ,					_A=False     ,					_A=0.0_1     ,					_A="float32"     ,					_A=False     ,					_A=32     ,					_A=128     ,					_A=0.1     ,					_A=1e-6     ,					_A=0.0_0_1     ,					_A=0.0_0_1     ,					_A=1.0     ,					_A="relu"     ,					_A=True     ,					_A=False     ,					_A=True     ,					_A=0     ,					_A=1     ,					**_A     ,					):
									__A	:						Dict       =				vocab_size
									__A	:						Any       =				d_model
									__A	:						Dict       =				d_kv
									__A	:						str       =				d_ff

									__A	:						Optional[Any]       =				num_sparse_encoder_layers

									__A	:						str       =				num_layers
									__A	:						Tuple       =				(
									    num_decoder_layers if num_decoder_layers is not None else self.num_layers
									)  # default = symmetry
									__A	:						List[Any]       =				num_sparse_decoder_layers

									# This tells us, each how many encoder layer we'll have to set a sparse layer.
									if self.num_sparse_encoder_layers > 0:
														__A	:						Union[str, Any]       =				self.num_layers // self.num_sparse_encoder_layers
									else:
														__A	:						Optional[Any]       =				self.num_layers  # HACK: this will create 0 sparse layers

									# This tells us, each how many encoder layer we'll have to set a sparse layer.
									if self.num_sparse_decoder_layers > 0:
														__A	:						Optional[Any]       =				self.num_decoder_layers // self.num_sparse_decoder_layers
									else:
														__A	:						Union[str, Any]       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

									__A	:						int       =				num_heads
									__A	:						List[str]       =				num_experts
									__A	:						Optional[Any]       =				expert_capacity
									__A	:						Optional[int]       =				router_bias
									__A	:						Dict       =				router_jitter_noise
									if router_dtype not in ["float32", "float16", "bfloat16"]:
														raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""     )
									__A	:						Any       =				router_dtype

									__A	:						str       =				router_ignore_padding_tokens
									__A	:						List[str]       =				relative_attention_num_buckets
									__A	:						List[str]       =				relative_attention_max_distance

									__A	:						List[str]       =				dropout_rate
									__A	:						int       =				layer_norm_epsilon
									__A	:						Optional[Any]       =				initializer_factor
									__A	:						Union[str, Any]       =				feed_forward_proj
									__A	:						Optional[int]       =				use_cache
									__A	:						int       =				add_router_probs

									__A	:						Optional[int]       =				router_z_loss_coef
									__A	:						Optional[int]       =				router_aux_loss_coef

									__A	:						List[str]       =				self.feed_forward_proj.split('-'     )
									__A	:						Any       =				act_info[-1]
									__A	:						int       =				act_info[0] == 'gated'

									if len(_A     ) > 1 and act_info[0] != "gated" or len(_A     ) > 2:
														raise ValueError(
														    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
														    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
														    '\'gated-gelu\' or \'relu\''     )

									# for backwards compatibility
									if feed_forward_proj == "gated-gelu":
														__A	:						Optional[int]       =				'gelu_new'

									super().__init__(
									    pad_token_id=_A     ,					eos_token_id=_A     ,					is_encoder_decoder=_A     ,					**_A     ,					)











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					return str(a  ) == str(a  )[::-1]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					return int(a  ) + int(str(a  )[::-1]  )







def  _SCREAMING_SNAKE_CASE				(   a = 1_00_00  )      ->      int:
					__A	:						int       =				[]
					for num in range(1					,						a  ):
										__A	:						List[str]       =				0
										__A	:						List[Any]       =				num
										while iterations < 50:
															__A	:						str       =				sum_reverse(a  )
															iterations += 1
															if is_palindrome(a  ):
																				break
										else:
															lychrel_nums.append(a  )
					return len(a  )


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




