






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


SCREAMING_SNAKE_CASE__   :      Dict				=		"""▁"""

SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{"""vocab_file""": """spiece.model"""}

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

SCREAMING_SNAKE_CASE__   :      Any				=		{
    """google/pegasus-xsum""": 5_12,
}


SCREAMING_SNAKE_CASE__   :      Tuple				=		logging.get_logger(__name__)
class     lowerCamelCase_					(   _a			):
				a__       							=	VOCAB_FILES_NAMES

				a__       							=	VOCAB_FILES_NAMES
				a__       							=	PRETRAINED_VOCAB_FILES_MAP
				a__       							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__       							=	["""input_ids""", """attention_mask"""]







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase="<pad>"							,       __lowerCAmelCase="</s>"							,       __lowerCAmelCase="<unk>"							,       __lowerCAmelCase="<mask_2>"							,       __lowerCAmelCase="<mask_1>"							,       __lowerCAmelCase=None							,       __lowerCAmelCase=1_0_3							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:str   =   offset
								if additional_special_tokens is not None:
												if not isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
																raise TypeError(
																    F'''additional_special_tokens should be of type {type(__lowerCAmelCase  )}, but is'''
																    F''' {type(__lowerCAmelCase  )}'''  )

												__magic_name__		:Any   =   (
												    ([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(__lowerCAmelCase  )							,       self.offset - 1  )
												]

												if len(set(__lowerCAmelCase  )  ) != len(__lowerCAmelCase  ):
																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}.'''  )
												__magic_name__		:str   =   additional_special_tokens_extended
								else:
												__magic_name__		:Dict   =   [mask_token_sent] if mask_token_sent is not None else []
												additional_special_tokens += [F'''<unk_{i}>''' for i in range(2							,       self.offset  )]

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

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

								# add special tokens to encoder dict
								__magic_name__		:int   =   {
								    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  )}  )

								__magic_name__		:Union[str, Any]   =   {v: k for k, v in self.encoder.items()}







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return len(self.sp_model  ) + self.offset







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   {self.convert_ids_to_tokens(__lowerCAmelCase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







				def __getstate__(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.__dict__.copy()
								__magic_name__		:Optional[Any]   =   None
								return state







				def __setstate__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   d

								# for backward compatibility
								if not hasattr(self							,       '''sp_model_kwargs'''  ):
												__magic_name__		:int   =   {}

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







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return self.sp_model.encode(__lowerCAmelCase							,       out_type=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if token in self.decoder:
												return self.decoder[token]
								elif token in self.added_tokens_decoder:
												return self.added_tokens_decoder[token]
								__magic_name__		:Union[str, Any]   =   self.sp_model.piece_to_id(__lowerCAmelCase  )
								return sp_id + self.offset







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if index in self.encoder:
												return self.encoder[index]
								elif index in self.added_tokens_encoder:
												return self.added_tokens_encoder[index]
								else:
												__magic_name__		:str   =   self.sp_model.IdToPiece(index - self.offset  )
								return token







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   []
								__magic_name__		:str   =   ''''''
								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(__lowerCAmelCase  ) + token
																__magic_name__		:Optional[Any]   =   []
												else:
																current_sub_tokens.append(__lowerCAmelCase  )
								out_string += self.sp_model.decode(__lowerCAmelCase  )
								return out_string.strip()







				def       A						(    self							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								return 1







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase = False  ):
								"""simple docstring"""
								if already_has_special_tokens:
												return self._special_token_mask(__lowerCAmelCase  )
								elif token_ids_a is None:
												return self._special_token_mask(__lowerCAmelCase  ) + [1]
								else:
												return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								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       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None  ):
								"""simple docstring"""
								if not os.path.isdir(__lowerCAmelCase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
								__magic_name__		:Optional[int]   =   os.path.join(
								    __lowerCAmelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowerCAmelCase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file							,       __lowerCAmelCase  )
								elif not os.path.isfile(self.vocab_file  ):
												with open(__lowerCAmelCase							,       '''wb'''  ) as fi:
																__magic_name__		:Optional[int]   =   self.sp_model.serialized_model_proto()
																fi.write(__lowerCAmelCase  )

								return (out_vocab_file,)













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


UpperCamelCase__           =			logging.get_logger(__name__)

UpperCamelCase__           =			{
    "tanreinama/GPTSAN-2.8B-spout_is_uniform": (
        "https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"
    ),
}

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""gptsan-japanese"""
							snake_case  :   Any      =							[
							    """past_key_values""",
							]
							snake_case  :   str      =							{
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}






							def __init__(    self	,			__lowerCAmelCase=36000	,			__lowerCAmelCase=1280	,			__lowerCAmelCase=1024	,			__lowerCAmelCase=8192	,			__lowerCAmelCase=4096	,			__lowerCAmelCase=128	,			__lowerCAmelCase=10	,			__lowerCAmelCase=0	,			__lowerCAmelCase=16	,			__lowerCAmelCase=16	,			__lowerCAmelCase=128	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase=1E-5	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.0	,			__lowerCAmelCase="float32"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=False	,			__lowerCAmelCase=0.002	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			__lowerCAmelCase=35998	,			__lowerCAmelCase=35995	,			__lowerCAmelCase=35999	,			**__lowerCAmelCase	,			):
														UpperCamelCase__					  =      vocab_size
														UpperCamelCase__					  =      max_position_embeddings
														UpperCamelCase__					  =      d_model
														UpperCamelCase__					  =      d_ff
														UpperCamelCase__					  =      d_ext
														UpperCamelCase__					  =      d_spout
														UpperCamelCase__					  =      num_switch_layers
														UpperCamelCase__					  =      num_ext_layers
														UpperCamelCase__					  =      num_switch_layers + num_ext_layers
														UpperCamelCase__					  =      num_heads
														UpperCamelCase__					  =      num_experts
														UpperCamelCase__					  =      expert_capacity
														UpperCamelCase__					  =      dropout_rate
														UpperCamelCase__					  =      layer_norm_epsilon
														UpperCamelCase__					  =      router_bias
														UpperCamelCase__					  =      router_jitter_noise
														UpperCamelCase__					  =      router_dtype
														UpperCamelCase__					  =      router_ignore_padding_tokens
														UpperCamelCase__					  =      output_hidden_states
														UpperCamelCase__					  =      output_attentions
														UpperCamelCase__					  =      initializer_factor
														UpperCamelCase__					  =      output_router_logits
														UpperCamelCase__					  =      use_cache

														super().__init__(
														    separator_token_id=__lowerCAmelCase	,			pad_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	,			)




