





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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Any       =       "nllb-moe"
						__magic_name__:			List[str]       =       ["past_key_values"]
						__magic_name__:			Union[str, Any]       =       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






						def __init__(      self      :      Tuple		,   _A      :      List[str]=128112		,   _A      :      str=1024		,   _A      :      Union[str, Any]=12		,   _A      :      Any=4096		,   _A      :      Optional[Any]=16		,   _A      :      Union[str, Any]=12		,   _A      :      Dict=4096		,   _A      :      Tuple=16		,   _A      :      str=0.0_5		,   _A      :      Dict=0.0_5		,   _A      :      int=True		,   _A      :      Union[str, Any]=True		,   _A      :      int="relu"		,   _A      :      Union[str, Any]=1024		,   _A      :      str=0.1		,   _A      :      Union[str, Any]=0.1		,   _A      :      str=0.0		,   _A      :      List[Any]=0.0_2		,   _A      :      Union[str, Any]=2		,   _A      :      List[Any]=True		,   _A      :      Union[str, Any]=False		,   _A      :      Dict="float32"		,   _A      :      Tuple=False		,   _A      :      Any=128		,   _A      :      Optional[int]=64		,   _A      :      Any=4		,   _A      :      str=4		,   _A      :      Optional[int]=0.0_0_1		,   _A      :      Dict=0.0_0_1		,   _A      :      Any="all"		,   _A      :      Tuple=False		,   _A      :      Tuple=False		,   _A      :      List[str]=1.0		,   _A      :      int=0.2		,   _A      :      List[str]=1		,   _A      :      int=0		,   _A      :      Union[str, Any]=2		,   _A      :      Union[str, Any]=False		,   **_A      :      List[Any]		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			Tuple											=	vocab_size
							snake_case_		:			List[str]											=	max_position_embeddings
							snake_case_		:			Tuple											=	d_model
							snake_case_		:			Optional[Any]											=	encoder_ffn_dim
							snake_case_		:			Union[str, Any]											=	encoder_layers
							snake_case_		:			Optional[Any]											=	encoder_attention_heads
							snake_case_		:			int											=	decoder_ffn_dim
							snake_case_		:			Tuple											=	decoder_layers
							snake_case_		:			int											=	decoder_attention_heads
							snake_case_		:			Any											=	dropout
							snake_case_		:			Tuple											=	attention_dropout
							snake_case_		:			Tuple											=	activation_dropout
							snake_case_		:			List[Any]											=	activation_function
							snake_case_		:			int											=	init_std
							snake_case_		:			List[Any]											=	encoder_layerdrop
							snake_case_		:			Any											=	decoder_layerdrop
							snake_case_		:			int											=	use_cache
							snake_case_		:			Optional[int]											=	encoder_layers
							snake_case_		:			str											=	scale_embedding  # scale factor will be sqrt(d_model) if True
							snake_case_		:			Dict											=	router_z_loss_coef
							snake_case_		:			Tuple											=	router_aux_loss_coef
							snake_case_		:			Tuple											=	decoder_sparse_step
							snake_case_		:			Dict											=	encoder_sparse_step
							snake_case_		:			int											=	num_experts
							snake_case_		:			Optional[Any]											=	expert_capacity
							snake_case_		:			Optional[Any]											=	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}"""	)
							snake_case_		:			Any											=	router_dtype

							snake_case_		:			Optional[int]											=	router_ignore_padding_tokens
							snake_case_		:			List[str]											=	batch_prioritized_routing
							snake_case_		:			List[Any]											=	second_expert_policy
							snake_case_		:			int											=	normalize_router_prob_before_dropping
							snake_case_		:			Dict											=	moe_eval_capacity_token_fraction
							snake_case_		:			int											=	moe_token_dropout
							snake_case_		:			Dict											=	output_router_logits
							super().__init__(
							    pad_token_id=_A		,   bos_token_id=_A		,   eos_token_id=_A		,   is_encoder_decoder=_A		,   decoder_start_token_id=_A		,   **_A		,   )








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

