
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_xlnet import XLNetTokenizer
else:
     lowerCamelCase__     :		str        = None


lowerCamelCase__     :		List[Any]        = logging.get_logger(__name__)

lowerCamelCase__     :		Any        = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__     :		str        = {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__     :		List[str]        = {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

lowerCamelCase__     :		str        = """▁"""

# Segments (not really needed)
lowerCamelCase__     :		Dict        = 0
lowerCamelCase__     :		str        = 1
lowerCamelCase__     :		int        = 2
lowerCamelCase__     :		Optional[Any]        = 3
lowerCamelCase__     :		Dict        = 4



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  Any	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  str	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  Optional[int]	   =      'left'
 __lowerCAmelCase	:  Dict	   =      XLNetTokenizer


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_="<sep>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="<cls>"       ,	SCREAMING_SNAKE_CASE_="<mask>"       ,	SCREAMING_SNAKE_CASE_=["<eop>", "<eod>"]       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   AddedToken(SCREAMING_SNAKE_CASE_       ,	lstrip=SCREAMING_SNAKE_CASE_       ,	rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else mask_token

   super().__init__(
       vocab_file=SCREAMING_SNAKE_CASE_       ,	tokenizer_file=SCREAMING_SNAKE_CASE_       ,	do_lower_case=SCREAMING_SNAKE_CASE_       ,	remove_space=SCREAMING_SNAKE_CASE_       ,	keep_accents=SCREAMING_SNAKE_CASE_       ,	bos_token=SCREAMING_SNAKE_CASE_       ,	eos_token=SCREAMING_SNAKE_CASE_       ,	unk_token=SCREAMING_SNAKE_CASE_       ,	sep_token=SCREAMING_SNAKE_CASE_       ,	pad_token=SCREAMING_SNAKE_CASE_       ,	cls_token=SCREAMING_SNAKE_CASE_       ,	mask_token=SCREAMING_SNAKE_CASE_       ,	additional_special_tokens=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: Union[str, Any]     		=   3
   lowercase__	: Optional[Any]     		=   do_lower_case
   lowercase__	: Optional[int]     		=   remove_space
   lowercase__	: Any     		=   keep_accents
   lowercase__	: Optional[int]     		=   vocab_file
   lowercase__	: Optional[Any]     		=   False if not self.vocab_file else True


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   [self.sep_token_id]
   lowercase__	: Tuple     		=   [self.cls_token_id]
   if token_ids_a is None:
     return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: Any     		=   [self.sep_token_id]
   lowercase__	: Optional[Any]     		=   [2]

   if token_ids_a is None:
     return len(token_ids_a + sep) * [0] + cls_segment_id
   return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if not self.can_save_slow_tokenizer:
     raise ValueError(
         """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
         """tokenizer.""")

   if not os.path.isdir(SCREAMING_SNAKE_CASE_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   lowercase__	: Optional[int]     		=   os.path.join(
       SCREAMING_SNAKE_CASE_       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

   if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE_):
     copyfile(self.vocab_file       ,	SCREAMING_SNAKE_CASE_)

   return (out_vocab_file,)











"""simple docstring"""






import warnings

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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Tuple =       {
    '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      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





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

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]=3_2000			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						Dict=24			,     _SCREAMING_SNAKE_CASE:						Optional[int]=16			,     _SCREAMING_SNAKE_CASE:						List[str]=4096			,     _SCREAMING_SNAKE_CASE:						Tuple="gelu"			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						List[Any]="bi"			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						List[str]=1e-12			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Any=512			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						List[Any]=-1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[int]="last"			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Dict="tanh"			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						Tuple=5			,     _SCREAMING_SNAKE_CASE:						List[str]=5			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=5			,     _SCREAMING_SNAKE_CASE:						Optional[int]=1			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			Optional[Any]:

				"""simple docstring"""

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

				__lowerCAmelCase :    Any				=			initializer_range
				__lowerCAmelCase :    List[Any]				=			layer_norm_eps

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

				__lowerCAmelCase :    List[str]				=			summary_type
				__lowerCAmelCase :    Optional[int]				=			summary_use_proj
				__lowerCAmelCase :    Tuple				=			summary_activation
				__lowerCAmelCase :    Dict				=			summary_last_dropout
				__lowerCAmelCase :    str				=			start_n_top
				__lowerCAmelCase :    List[str]				=			end_n_top

				__lowerCAmelCase :    str				=			bos_token_id
				__lowerCAmelCase :    Optional[Any]				=			pad_token_id
				__lowerCAmelCase :    Optional[Any]				=			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."			,     _SCREAMING_SNAKE_CASE			,     )
							__lowerCAmelCase :    Optional[int]				=			kwargs["use_cache"]

				__lowerCAmelCase :    Optional[int]				=			use_mems_eval
				__lowerCAmelCase :    Any				=			use_mems_train
				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			int:

				"""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 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

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