

import warnings

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


a_     =			logging.get_logger(__name__)

a_     =			{
    """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 _UpperCamelCase							(							UpperCAmelCase__      ):
			'''simple docstring'''







			lowerCamelCase__							="""xlnet"""
			lowerCamelCase__							=["""mems"""]
			lowerCamelCase__							={
			    """n_token""": """vocab_size""",  # Backward compatibility
			    """hidden_size""": """d_model""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}
			def __init__(	self							:       List[str]					,					a							:       Tuple=3_2000					,					a							:       List[Any]=1024					,					a							:       List[str]=24					,					a							:       str=16					,					a							:       Any=4096					,					a							:       str="gelu"					,					a							:       Union[str, Any]=True					,					a							:       str="bi"					,					a							:       Optional[int]=0.02					,					a							:       Tuple=1e-12					,					a							:       List[str]=0.1					,					a							:       Dict=512					,					a							:       Union[str, Any]=None					,					a							:       Tuple=True					,					a							:       Union[str, Any]=False					,					a							:       Any=False					,					a							:       List[Any]=-1					,					a							:       Optional[int]=False					,					a							:       Optional[Any]="last"					,					a							:       Optional[int]=True					,					a							:       Dict="tanh"					,					a							:       Dict=0.1					,					a							:       List[str]=5					,					a							:       Tuple=5					,					a							:       List[Any]=5					,					a							:       Any=1					,					a							:       Optional[int]=2					,					**a							:       int					,					)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				vocab_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				d_model
				SCREAMING_SNAKE_CASE      :					Tuple    =				n_layer
				SCREAMING_SNAKE_CASE      :					List[Any]    =				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})"		)
				SCREAMING_SNAKE_CASE      :					Any    =				d_model // n_head
				SCREAMING_SNAKE_CASE      :					List[Any]    =				ff_activation
				SCREAMING_SNAKE_CASE      :					int    =				d_inner
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				untie_r
				SCREAMING_SNAKE_CASE      :					int    =				attn_type

				SCREAMING_SNAKE_CASE      :					int    =				initializer_range
				SCREAMING_SNAKE_CASE      :					Tuple    =				layer_norm_eps

				SCREAMING_SNAKE_CASE      :					List[Any]    =				dropout
				SCREAMING_SNAKE_CASE      :					Tuple    =				mem_len
				SCREAMING_SNAKE_CASE      :					int    =				reuse_len
				SCREAMING_SNAKE_CASE      :					List[Any]    =				bi_data
				SCREAMING_SNAKE_CASE      :					Tuple    =				clamp_len
				SCREAMING_SNAKE_CASE      :					List[Any]    =				same_length

				SCREAMING_SNAKE_CASE      :					int    =				summary_type
				SCREAMING_SNAKE_CASE      :					str    =				summary_use_proj
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				summary_activation
				SCREAMING_SNAKE_CASE      :					Any    =				summary_last_dropout
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				start_n_top
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				end_n_top

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				bos_token_id
				SCREAMING_SNAKE_CASE      :					Tuple    =				pad_token_id
				SCREAMING_SNAKE_CASE      :					str    =				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."					,					__lowercase					,					)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				kwargs['''use_cache''']

				SCREAMING_SNAKE_CASE      :					List[str]    =				use_mems_eval
				SCREAMING_SNAKE_CASE      :					List[Any]    =				use_mems_train
				super().__init__(pad_token_id=__lowercase					,					bos_token_id=__lowercase					,					eos_token_id=__lowercase					,					**__lowercase		)
			@property
			def 							__UpperCamelCase       (	self							:       List[str]		)							->				List[Any]:

				"""simple docstring"""

				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 							__UpperCamelCase       (	self							:       int					,					a							:       List[Any]		)							->				Optional[int]:

				"""simple docstring"""

				raise NotImplementedError(
				    F"The model {self.model_type} is one of the few models that has no sequence length limit."		)



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_ = "cpu"       ,      lowerCAmelCase_ = None       )->       None:
				lowerCAmelCase_       :					str			 = torch.load(lowerCAmelCase_       ,      map_location=lowerCAmelCase_       )
				for k, v in tqdm(state_dict.items()       ):
								if not isinstance(lowerCAmelCase_       ,      torch.Tensor       ):
												raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''       )
								lowerCAmelCase_       :					int			 = v.half()
				if save_path is None:  # overwrite src_path
								lowerCAmelCase_       :					Tuple			 = src_path
				torch.save(lowerCAmelCase_       ,      lowerCAmelCase_       )


if __name__ == "__main__":
						fire.Fire(convert)