


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


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

_snake_case					:		Optional[int]      =    {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  "switch_transformers"
							__UpperCAmelCase   :							Optional[int]					  =  ["past_key_values"]
							__UpperCAmelCase   :							Optional[Any]					  =  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  str=32128   ,     lowerCamelCase	:  str=768   ,     lowerCamelCase	:  Any=64   ,     lowerCamelCase	:  Tuple=2048   ,     lowerCamelCase	:  List[str]=64   ,     lowerCamelCase	:  List[str]=12   ,     lowerCamelCase	:  List[Any]=3   ,     lowerCamelCase	:  List[Any]=12   ,     lowerCamelCase	:  List[str]=3   ,     lowerCamelCase	:  List[str]=12   ,     lowerCamelCase	:  List[str]=8   ,     lowerCamelCase	:  str=False   ,     lowerCamelCase	:  str=0.01   ,     lowerCamelCase	:  Union[str, Any]="float32"   ,     lowerCamelCase	:  Dict=False   ,     lowerCamelCase	:  Optional[Any]=32   ,     lowerCamelCase	:  int=128   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  List[Any]=1E-6   ,     lowerCamelCase	:  int=0.0_01   ,     lowerCamelCase	:  Union[str, Any]=0.0_01   ,     lowerCamelCase	:  str=1.0   ,     lowerCamelCase	:  str="relu"   ,     lowerCamelCase	:  Optional[Any]=True   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Union[str, Any]=1   ,     **lowerCamelCase	:  str   ,     )     ->   str:
														__snake_case		:      Union[str, Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       d_model
														__snake_case		:      List[str]										=       d_kv
														__snake_case		:      str										=       d_ff

														__snake_case		:      Dict										=       num_sparse_encoder_layers

														__snake_case		:      Dict										=       num_layers
														__snake_case		:      int										=       (
														    num_decoder_layers if num_decoder_layers is not None else self.num_layers
														)  # default = symmetry
														__snake_case		:      List[str]										=       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:
																					__snake_case		:      Union[str, Any]										=       self.num_layers // self.num_sparse_encoder_layers
														else:
																					__snake_case		:      int										=       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:
																					__snake_case		:      List[Any]										=       self.num_decoder_layers // self.num_sparse_decoder_layers
														else:
																					__snake_case		:      List[str]										=       self.num_decoder_layers  # HACK: this will create 0 sparse layers

														__snake_case		:      Any										=       num_heads
														__snake_case		:      Union[str, Any]										=       num_experts
														__snake_case		:      Union[str, Any]										=       expert_capacity
														__snake_case		:      Any										=       router_bias
														__snake_case		:      str										=       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}'    )
														__snake_case		:      Any										=       router_dtype

														__snake_case		:      Tuple										=       router_ignore_padding_tokens
														__snake_case		:      List[Any]										=       relative_attention_num_buckets
														__snake_case		:      Any										=       relative_attention_max_distance

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

														__snake_case		:      Any										=       router_z_loss_coef
														__snake_case		:      Optional[Any]										=       router_aux_loss_coef

														__snake_case		:      str										=       self.feed_forward_proj.split("-"    )
														__snake_case		:      str										=       act_info[-1]
														__snake_case		:      Union[str, Any]										=       act_info[0] == "gated"

														if len(lowerCamelCase    ) > 1 and act_info[0] != "gated" or len(lowerCamelCase    ) > 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":
																					__snake_case		:      int										=       "gelu_new"

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





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														if board[row][i] == 1:
																					return False
							for i in range(len(__lowerCamelCase     )     ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     -1							,     -1     )     ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     len(__lowerCamelCase     )     )     ):
														if board[i][j] == 1:
																					return False
							return True







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







def     lowerCAmelCase_					( __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														for j in range(len(__lowerCamelCase     )     ):
																					if board[i][j] == 1:
																												print("Q"							,     end=" "     )
																					else:
																												print("."							,     end=" "     )
														print()


# n=int(input("The no. of queens"))
_snake_case					:		List[str]      =    8
_snake_case					:		Optional[int]      =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("The total no. of solutions are :", len(solution))

