






"""simple docstring"""




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:
 _snake_case          =   None


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case          =   {
    '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',
    },
}

_snake_case          =   {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

_snake_case          =   '▁'

# Segments (not really needed)
_snake_case          =   0
_snake_case          =   1
_snake_case          =   2
_snake_case          =   3
_snake_case          =   4






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       Dict			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       Optional[int]			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Any			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase					:       str			=					'''left'''
 UpperCamelCase					:       Tuple			=					XLNetTokenizer


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						Optional[Any]=None							,						UpperCAmelCase__		:						Optional[int]=None							,						UpperCAmelCase__		:						Dict=False							,						UpperCAmelCase__		:						str=True							,						UpperCAmelCase__		:						Optional[int]=False							,						UpperCAmelCase__		:						List[Any]="<s>"							,						UpperCAmelCase__		:						Union[str, Any]="</s>"							,						UpperCAmelCase__		:						Optional[Any]="<unk>"							,						UpperCAmelCase__		:						Optional[Any]="<sep>"							,						UpperCAmelCase__		:						List[Any]="<pad>"							,						UpperCAmelCase__		:						Optional[Any]="<cls>"							,						UpperCAmelCase__		:						Dict="<mask>"							,						UpperCAmelCase__		:						Any=["<eop>", "<eod>"]							,						**UpperCAmelCase__		:						Any							,						)					->       Optional[int]:
   # Mask token behave like a normal word, i.e. include the space before it
   _a  :       Any             =	AddedToken(UpperCAmelCase__							,						lstrip=UpperCAmelCase__							,						rstrip=UpperCAmelCase__							) if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							) else mask_token

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

   _a  :       List[Any]             =	3
   _a  :       Dict             =	do_lower_case
   _a  :       str             =	remove_space
   _a  :       Any             =	keep_accents
   _a  :       Optional[Any]             =	vocab_file
   _a  :       Dict             =	False if not self.vocab_file else True


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       List[Any]             =	[self.sep_token_id]
   _a  :       int             =	[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		:						Any							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						Optional[List[int]] = None							)					->       List[int]:
   _a  :       Tuple             =	[self.sep_token_id]
   _a  :       str             =	[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		:						List[str]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   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(UpperCAmelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
   _a  :       str             =	os.path.join(
       UpperCAmelCase__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

   return (out_vocab_file,)









"""simple docstring"""




import warnings

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

