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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_  :	str 				=					"▁"

lowerCamelCase_  :	int 				=					{"vocab_file": "spiece.model"}

lowerCamelCase_  :	Tuple 				=					{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

lowerCamelCase_  :	List[str] 				=					{
    "google/pegasus-xsum": 5_12,
}


lowerCamelCase_  :	int 				=					logging.get_logger(__name__)

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Any    =	VOCAB_FILES_NAMES

			__UpperCamelCase :  Optional[int]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Tuple    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  int    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  List[Any]    =	["""input_ids""", """attention_mask"""]

			def __init__(    self	:    int  ,		snake_case_	:    Optional[int]  ,		snake_case_	:    Optional[int]="<pad>"  ,		snake_case_	:    List[str]="</s>"  ,		snake_case_	:    List[str]="<unk>"  ,		snake_case_	:    Optional[Any]="<mask_2>"  ,		snake_case_	:    int="<mask_1>"  ,		snake_case_	:    List[str]=None  ,		snake_case_	:    List[Any]=103  ,		snake_case_	:    Tuple = None  ,		**snake_case_	:    Tuple  ,		):
							UpperCamelCase_:							str		=  offset
							if additional_special_tokens is not None:
											if not isinstance(_a  ,		_a ):
															raise TypeError(
															    f'''additional_special_tokens should be of type {type(_a )}, but is'''
															    f''' {type(_a )}''' )

											UpperCamelCase_:							str		=  (
											    ([mask_token_sent] + additional_special_tokens)
											    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
											    else additional_special_tokens
											)
											# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
											additional_special_tokens_extended += [
											    f'''<unk_{i}>''' for i in range(len(_a )  ,		self.offset - 1 )
											]

											if len(set(_a ) ) != len(_a ):
															raise ValueError(
															    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
															    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
											UpperCamelCase_:							Tuple		=  additional_special_tokens_extended
							else:
											UpperCamelCase_:							Tuple		=  [mask_token_sent] if mask_token_sent is not None else []
											additional_special_tokens += [f'''<unk_{i}>''' for i in range(2  ,		self.offset )]

							UpperCamelCase_:							str		=  {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_a  ,		unk_token=_a  ,		mask_token=_a  ,		pad_token=_a  ,		mask_token_sent=_a  ,		offset=_a  ,		additional_special_tokens=_a  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**_a  ,		)
							UpperCamelCase_:							Any		=  mask_token_sent
							UpperCamelCase_:							List[str]		=  vocab_file
							UpperCamelCase_:							List[Any]		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(_a )

							# add special tokens to encoder dict
							UpperCamelCase_:							Dict[int, str]		=  {
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
											self.encoder.update(
											    {
											        2: self.mask_token_sent,
											        3: self.mask_token,
											    } )

							if self.offset > 0:
											# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
											# mask_token_sent is already added to list -> so start at 1
											self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1  ,		self.offset - 1 )} )

							UpperCamelCase_:							Dict[str, int]		=  {v: k for k, v in self.encoder.items()}

			@property
			def  lowerCAmelCase__		(    self	:    Any ):
							return len(self.sp_model ) + self.offset

			def  lowerCAmelCase__		(    self	:    Dict ):
							UpperCamelCase_:							Optional[int]		=  {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
							vocab.update(self.added_tokens_encoder )
							return vocab

			def __getstate__(    self	:    Optional[int] ):
							UpperCamelCase_:							Dict		=  self.__dict__.copy()
							UpperCamelCase_:							int		=  None
							return state

			def __setstate__(    self	:    str  ,		snake_case_	:    Union[str, Any] ):
							UpperCamelCase_:							List[Any]		=  d

							# for backward compatibility
							if not hasattr(self  ,		"""sp_model_kwargs""" ):
											UpperCamelCase_:							Optional[int]		=  {}

							UpperCamelCase_:							List[str]		=  spm.SentencePieceProcessor(**self.sp_model_kwargs )
							self.sp_model.Load(self.vocab_file )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    str ):
							return self.sp_model.encode(_a  ,		out_type=_a )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    int ):
							if token in self.decoder:
											return self.decoder[token]
							elif token in self.added_tokens_decoder:
											return self.added_tokens_decoder[token]
							UpperCamelCase_:							Any		=  self.sp_model.piece_to_id(_a )
							return sp_id + self.offset

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    Optional[Any] ):
							if index in self.encoder:
											return self.encoder[index]
							elif index in self.added_tokens_encoder:
											return self.added_tokens_encoder[index]
							else:
											UpperCamelCase_:							str		=  self.sp_model.IdToPiece(index - self.offset )
							return token

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    Optional[Any] ):
							UpperCamelCase_:							Union[str, Any]		=  []
							UpperCamelCase_:							int		=  ""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(_a ) + token
															UpperCamelCase_:							Optional[int]		=  []
											else:
															current_sub_tokens.append(_a )
							out_string += self.sp_model.decode(_a )
							return out_string.strip()

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    int=False ):
							return 1

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Any ):
							UpperCamelCase_:							Union[str, Any]		=  set(self.all_special_ids )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Union[str, Any] = None  ,		snake_case_	:    Any = False ):
							if already_has_special_tokens:
											return self._special_token_mask(_a )
							elif token_ids_a is None:
											return self._special_token_mask(_a ) + [1]
							else:
											return self._special_token_mask(token_ids_a + token_ids_a ) + [1]

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Optional[Any]=None ):
							if token_ids_a is None:
											return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    Union[str, Any]  ,		snake_case_	:    Any = None ):
							if not os.path.isdir(_a ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
											return
							UpperCamelCase_:							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 ) and os.path.isfile(self.vocab_file ):
											copyfile(self.vocab_file  ,		_a )
							elif not os.path.isfile(self.vocab_file ):
											with open(_a  ,		"""wb""" ) as fi:
															UpperCamelCase_:							Union[str, Any]		=  self.sp_model.serialized_model_proto()
															fi.write(_a )

							return (out_vocab_file,)








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


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Optional[int] 				=					{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""ctrl"""
			__UpperCamelCase :  Dict    =	["""past_key_values"""]
			__UpperCamelCase :  List[str]    =	{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}

			def __init__(    self	:    Dict  ,		snake_case_	:    Any=24_6534  ,		snake_case_	:    Dict=256  ,		snake_case_	:    str=1280  ,		snake_case_	:    Optional[int]=8192  ,		snake_case_	:    Union[str, Any]=48  ,		snake_case_	:    Any=16  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    Optional[Any]=0.02  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  vocab_size
							UpperCamelCase_:							Union[str, Any]		=  n_positions
							UpperCamelCase_:							Optional[int]		=  n_embd
							UpperCamelCase_:							int		=  n_layer
							UpperCamelCase_:							str		=  n_head
							UpperCamelCase_:							Optional[int]		=  dff
							UpperCamelCase_:							Optional[Any]		=  resid_pdrop
							UpperCamelCase_:							Union[str, Any]		=  embd_pdrop
							UpperCamelCase_:							List[str]		=  layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]		=  initializer_range

							UpperCamelCase_:							Optional[Any]		=  use_cache

							super().__init__(**snake_case_ )






