






import warnings

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


__lowerCamelCase :      str										=				logging.get_logger(__name__)

__lowerCamelCase :      Optional[int]										=				{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		"xlnet"
					a_     =		["mems"]
					a_     =		{
					    "n_token": "vocab_size",  # Backward compatibility
					    "hidden_size": "d_model",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}



					def __init__( self					:  Tuple							,		__A					:  List[Any]=3_2_0_0_0							,		__A					:  Any=1_0_2_4							,		__A					:  Dict=2_4							,		__A					:  Dict=1_6							,		__A					:  Union[str, Any]=4_0_9_6							,		__A					:  int="gelu"							,		__A					:  Tuple=True							,		__A					:  Tuple="bi"							,		__A					:  List[Any]=0.0_2							,		__A					:  Union[str, Any]=1e-1_2							,		__A					:  Any=0.1							,		__A					:  Any=5_1_2							,		__A					:  Optional[Any]=None							,		__A					:  Optional[Any]=True							,		__A					:  Optional[Any]=False							,		__A					:  Dict=False							,		__A					:  int=-1							,		__A					:  str=False							,		__A					:  List[str]="last"							,		__A					:  List[Any]=True							,		__A					:  List[str]="tanh"							,		__A					:  Optional[Any]=0.1							,		__A					:  Dict=5							,		__A					:  int=5							,		__A					:  List[Any]=5							,		__A					:  Any=1							,		__A					:  int=2							,		**__A					:  int							,		):
						snake_case__			:				List[str]       =  vocab_size
						snake_case__			:				int       =  d_model
						snake_case__			:				Optional[int]       =  n_layer
						snake_case__			:				Tuple       =  n_head
						if d_model % n_head != 0:
							raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''	)
						if "d_head" in kwargs:
							if kwargs["d_head"] != d_model // n_head:
								raise ValueError(
								    f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''	)
						snake_case__			:				Optional[int]       =  d_model // n_head
						snake_case__			:				Any       =  ff_activation
						snake_case__			:				Optional[int]       =  d_inner
						snake_case__			:				Union[str, Any]       =  untie_r
						snake_case__			:				Optional[int]       =  attn_type

						snake_case__			:				List[str]       =  initializer_range
						snake_case__			:				int       =  layer_norm_eps

						snake_case__			:				Optional[Any]       =  dropout
						snake_case__			:				Tuple       =  mem_len
						snake_case__			:				Union[str, Any]       =  reuse_len
						snake_case__			:				List[str]       =  bi_data
						snake_case__			:				List[str]       =  clamp_len
						snake_case__			:				Any       =  same_length

						snake_case__			:				Tuple       =  summary_type
						snake_case__			:				List[Any]       =  summary_use_proj
						snake_case__			:				List[str]       =  summary_activation
						snake_case__			:				Optional[Any]       =  summary_last_dropout
						snake_case__			:				Any       =  start_n_top
						snake_case__			:				Optional[Any]       =  end_n_top

						snake_case__			:				Union[str, Any]       =  bos_token_id
						snake_case__			:				Union[str, Any]       =  pad_token_id
						snake_case__			:				Optional[int]       =  eos_token_id

						if "use_cache" in kwargs:
							warnings.warn(
							    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
							    " instead."							,		__A							,		)
							snake_case__			:				Tuple       =  kwargs["use_cache"]

						snake_case__			:				Optional[Any]       =  use_mems_eval
						snake_case__			:				List[str]       =  use_mems_train
						super().__init__(pad_token_id=__A							,		bos_token_id=__A							,		eos_token_id=__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  Union[str, Any]	):
						logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''	)
						return -1






					@max_position_embeddings.setter
					def 	_lowercase					( self					:  str							,		__A					:  Dict	):
						# Message copied from Transformer-XL documentation
						raise NotImplementedError(
						    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''	)











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



