




"""simple docstring"""





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


A				=							logging.get_logger(__name__)

A				=							{
    """tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
        """https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
    ),
}



class   a__					(						__magic_name__ ):
			lowercase_		=						"gptsan-japanese"
			lowercase_		=						[
			    "past_key_values",
			]
			lowercase_		=						{
			    "hidden_size": "d_model",
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     List[Any]=36000   ,	UpperCamelCase_   :     Optional[Any]=1280   ,	UpperCamelCase_   :     Optional[int]=1024   ,	UpperCamelCase_   :     Dict=8192   ,	UpperCamelCase_   :     Optional[int]=4096   ,	UpperCamelCase_   :     Union[str, Any]=128   ,	UpperCamelCase_   :     Dict=10   ,	UpperCamelCase_   :     Optional[Any]=0   ,	UpperCamelCase_   :     Union[str, Any]=16   ,	UpperCamelCase_   :     List[Any]=16   ,	UpperCamelCase_   :     List[str]=128   ,	UpperCamelCase_   :     List[str]=0.0   ,	UpperCamelCase_   :     str=1e-5   ,	UpperCamelCase_   :     Tuple=False   ,	UpperCamelCase_   :     Dict=0.0   ,	UpperCamelCase_   :     Tuple="float32"   ,	UpperCamelCase_   :     Optional[Any]=False   ,	UpperCamelCase_   :     Optional[Any]=False   ,	UpperCamelCase_   :     Optional[int]=False   ,	UpperCamelCase_   :     int=0.002   ,	UpperCamelCase_   :     List[str]=False   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     List[Any]=35998   ,	UpperCamelCase_   :     str=35995   ,	UpperCamelCase_   :     Optional[int]=35999   ,	**UpperCamelCase_   :     Dict   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       vocab_size
						__UpperCAmelCase						:			Union[str, Any]												=       max_position_embeddings
						__UpperCAmelCase						:			str												=       d_model
						__UpperCAmelCase						:			Optional[Any]												=       d_ff
						__UpperCAmelCase						:			Optional[Any]												=       d_ext
						__UpperCAmelCase						:			Any												=       d_spout
						__UpperCAmelCase						:			Dict												=       num_switch_layers
						__UpperCAmelCase						:			Any												=       num_ext_layers
						__UpperCAmelCase						:			List[str]												=       num_switch_layers + num_ext_layers
						__UpperCAmelCase						:			List[str]												=       num_heads
						__UpperCAmelCase						:			Optional[int]												=       num_experts
						__UpperCAmelCase						:			List[Any]												=       expert_capacity
						__UpperCAmelCase						:			List[str]												=       dropout_rate
						__UpperCAmelCase						:			Union[str, Any]												=       layer_norm_epsilon
						__UpperCAmelCase						:			List[Any]												=       router_bias
						__UpperCAmelCase						:			Tuple												=       router_jitter_noise
						__UpperCAmelCase						:			Any												=       router_dtype
						__UpperCAmelCase						:			List[str]												=       router_ignore_padding_tokens
						__UpperCAmelCase						:			int												=       output_hidden_states
						__UpperCAmelCase						:			Dict												=       output_attentions
						__UpperCAmelCase						:			List[Any]												=       initializer_factor
						__UpperCAmelCase						:			Dict												=       output_router_logits
						__UpperCAmelCase						:			Optional[int]												=       use_cache

						super().__init__(
						    separator_token_id=UpperCamelCase_   ,	pad_token_id=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_   ,	**UpperCamelCase_   ,	)








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	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()
