



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


__snake_case     :Union[str, Any]			       =logging.get_logger(__name__)

__snake_case     :List[Any]			       ={
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Dict								=     'nllb-moe'
				A_   :     Optional[int]								=     ['past_key_values']
				A_   :     Any								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


				def __init__(							self      :  int						, __UpperCamelCase      :  int=128_112						, __UpperCamelCase      :  str=1_024						, __UpperCamelCase      :  Optional[Any]=12						, __UpperCamelCase      :  str=4_096						, __UpperCamelCase      :  Tuple=16						, __UpperCamelCase      :  List[str]=12						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=16						, __UpperCamelCase      :  str=0.0_5						, __UpperCamelCase      :  Optional[Any]=0.0_5						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  Any="relu"						, __UpperCamelCase      :  Any=1_024						, __UpperCamelCase      :  Dict=0.1						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  int=0.0						, __UpperCamelCase      :  int=0.0_2						, __UpperCamelCase      :  str=2						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Dict=False						, __UpperCamelCase      :  List[Any]="float32"						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  Any=128						, __UpperCamelCase      :  Optional[int]=64						, __UpperCamelCase      :  List[str]=4						, __UpperCamelCase      :  Dict=4						, __UpperCamelCase      :  int=0.0_0_1						, __UpperCamelCase      :  Dict=0.0_0_1						, __UpperCamelCase      :  Optional[Any]="all"						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  Any=1.0						, __UpperCamelCase      :  List[str]=0.2						, __UpperCamelCase      :  str=1						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  Dict=False						, **__UpperCamelCase      :  Optional[Any]						, )		->     Optional[int]:
											A      						=    vocab_size
											A      						=    max_position_embeddings
											A      						=    d_model
											A      						=    encoder_ffn_dim
											A      						=    encoder_layers
											A      						=    encoder_attention_heads
											A      						=    decoder_ffn_dim
											A      						=    decoder_layers
											A      						=    decoder_attention_heads
											A      						=    dropout
											A      						=    attention_dropout
											A      						=    activation_dropout
											A      						=    activation_function
											A      						=    init_std
											A      						=    encoder_layerdrop
											A      						=    decoder_layerdrop
											A      						=    use_cache
											A      						=    encoder_layers
											A      						=    scale_embedding  # scale factor will be sqrt(d_model) if True
											A      						=    router_z_loss_coef
											A      						=    router_aux_loss_coef
											A      						=    decoder_sparse_step
											A      						=    encoder_sparse_step
											A      						=    num_experts
											A      						=    expert_capacity
											A      						=    router_bias
											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      						=    router_dtype

											A      						=    router_ignore_padding_tokens
											A      						=    batch_prioritized_routing
											A      						=    second_expert_policy
											A      						=    normalize_router_prob_before_dropping
											A      						=    moe_eval_capacity_token_fraction
											A      						=    moe_token_dropout
											A      						=    output_router_logits
											super().__init__(
											    pad_token_id=__UpperCamelCase						, bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, is_encoder_decoder=__UpperCamelCase						, decoder_start_token_id=__UpperCamelCase						, **__UpperCamelCase						, )



'''simple docstring'''
from __future__ import annotations

import math


def      a (     __a					,   __a					,   __a					,   __a					,   __a  )    ->      int:



					'''simple docstring'''






					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''  )

					if len(__a  ) == 0:
										raise ValueError('''Scores cannot be empty'''  )

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )

					return min(
					    minimax(depth + 1					,   node_index * 2					,   __a					,   __a					,   __a  )					,   minimax(depth + 1					,   node_index * 2 + 1					,   __a					,   __a					,   __a  )					,   )


def      a (     )    ->      None:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					[90, 23, 6, 33, 21, 65, 123, 34423]
					UpperCamelCase__  :List[Any]				   =					math.log(len(__a  )					,   2  )
					print('''Optimal value : '''					,   end=''''''  )
					print(minimax(0					,   0					,   __a					,   __a					,   __a  )  )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()