



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:
						A__  :  Union[str, Any]								=					None


A__  :  Dict								=					logging.get_logger(__name__)

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

A__  :  Optional[int]								=					{
    '''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''',
    },
}

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

A__  :  List[str]								=					'''▁'''

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



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = VOCAB_FILES_NAMES
							_a			      = PRETRAINED_VOCAB_FILES_MAP
							_a			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_a			      = '''left'''
							_a			      = XLNetTokenizer




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

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

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




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

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

									return (out_vocab_file,)












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


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

_snake_case  :    Any						    =  {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """pegasus"""
  a_        = ["""past_key_values"""]
  a_        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]=5_0_2_6_5					,  lowerCAmelCase_ :		Union[str, Any]=1_0_2_4					,  lowerCAmelCase_ :		Union[str, Any]=1_2					,  lowerCAmelCase_ :		Dict=4_0_9_6					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		List[Any]=1_2					,  lowerCAmelCase_ :		Union[str, Any]=4_0_9_6					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Optional[int]=0.0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		Optional[int]=1_0_2_4					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		str=0.0					,  lowerCAmelCase_ :		Union[str, Any]=0.0					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		int=1					,  lowerCAmelCase_ :		Tuple=1					,  **lowerCAmelCase_ :		Tuple					,  )							->						List[str]:
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	d_model
     __lowerCAmelCase								=	encoder_ffn_dim
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	encoder_attention_heads
     __lowerCAmelCase								=	decoder_ffn_dim
     __lowerCAmelCase								=	decoder_layers
     __lowerCAmelCase								=	decoder_attention_heads
     __lowerCAmelCase								=	dropout
     __lowerCAmelCase								=	attention_dropout
     __lowerCAmelCase								=	activation_dropout
     __lowerCAmelCase								=	activation_function
     __lowerCAmelCase								=	init_std
     __lowerCAmelCase								=	encoder_layerdrop
     __lowerCAmelCase								=	decoder_layerdrop
     __lowerCAmelCase								=	use_cache
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase_					,  eos_token_id=lowerCAmelCase_					,  is_encoder_decoder=lowerCAmelCase_					,  decoder_start_token_id=lowerCAmelCase_					,  forced_eos_token_id=lowerCAmelCase_					,  **lowerCAmelCase_					,  )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return self.encoder_attention_heads






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     return self.d_model

