
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
				import torch


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


lowercase__							:							Optional[int]									=			logging.get_logger(__name__)
lowercase__							:							Dict									=			{"vocab_file": "spiece.model"}

lowercase__							:							Any									=			{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

lowercase__							:							Dict									=			{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  VOCAB_FILES_NAMES
							_snake_case     		=  PRETRAINED_VOCAB_FILES_MAP
							_snake_case     		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case     		=  ['input_ids', 'attention_mask']



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_ = None     ,	**SCREAMING_SNAKE_CASE_     ,	)->   None:





													'''simple docstring'''






													__UpperCamelCase       =			{} if sp_model_kwargs is None else sp_model_kwargs

													__UpperCamelCase       =			kwargs.get('''name_or_path'''			)
													if name_or_path is None:
																			logger.warning(
																			    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
																			    ''' you are testing the model, this can safely be ignored'''			)
																			__UpperCamelCase       =			'''None'''

													# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
													__UpperCamelCase       =			'''<|endoftext|>''' if eos_token is None else eos_token
													__UpperCamelCase       =			'''<unk>''' if unk_token is None else unk_token
													if "gpt-sw3-7b" in name_or_path:
																			__UpperCamelCase       =			unk_token if pad_token is None else pad_token
																			__UpperCamelCase       =			eos_token if bos_token is None else bos_token
													else:
																			__UpperCamelCase       =			'''<pad>''' if pad_token is None else pad_token
																			__UpperCamelCase       =			'''<s>''' if bos_token is None else bos_token

													super().__init__(
													    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_     ,	pad_token=SCREAMING_SNAKE_CASE_     ,	sp_model_kwargs=self.sp_model_kwargs     ,	**SCREAMING_SNAKE_CASE_     ,	)

													__UpperCamelCase       =			do_lower_case
													__UpperCamelCase       =			remove_space
													__UpperCamelCase       =			keep_accents
													__UpperCamelCase       =			vocab_file

													__UpperCamelCase       =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.Load(SCREAMING_SNAKE_CASE_			)

													# Used for whitespace normalization in input texts
													# fmt : off
													__UpperCamelCase       =			{''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
													# fmt : on

													# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
													__UpperCamelCase       =			re.compile(
													    F"[{''.join(map(SCREAMING_SNAKE_CASE_     ,	list(range(0     ,	9			)			) + list(range(11     ,	32			)			) + list(range(127     ,	160			)			) + [160, 173, 8203]			)			)}]"			)



							def __getstate__(  self			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			self.__dict__.copy()
													__UpperCamelCase       =			None
													return state



							def __setstate__(  self     ,	SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			d

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

													__UpperCamelCase       =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
													self.sp_model.Load(self.vocab_file			)



							@property
							# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return len(self.sp_model			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.non_printing_characters_re.sub(''''''     ,	SCREAMING_SNAKE_CASE_			)

													# Normalize whitespaces
													__UpperCamelCase       =			''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]			)

													# NFC Unicode normalization
													__UpperCamelCase       =			unicodedata.normalize('''NFC'''     ,	SCREAMING_SNAKE_CASE_			)
													return text



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			self.preprocess_text(SCREAMING_SNAKE_CASE_			)
													return self.sp_model.encode(SCREAMING_SNAKE_CASE_     ,	out_type=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_			)



							@staticmethod
							def     A__		(  SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return out_string



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			[]
													__UpperCamelCase       =			''''''
													__UpperCamelCase       =			False
													for token in tokens:
																			# make sure that special tokens are not decoded using sentencepiece model
																			if token in self.all_special_tokens:
																									# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																									if not prev_is_special:
																															out_string += " "

																									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_			) + token
																									__UpperCamelCase       =			True
																									__UpperCamelCase       =			[]
																			else:
																									current_sub_tokens.append(SCREAMING_SNAKE_CASE_			)
																									__UpperCamelCase       =			False
													out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_			)

													return out_string



							def     A__		(  self			)->   Dict[str, int]:





													'''simple docstring'''






													__UpperCamelCase       =			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			): i for i in range(self.vocab_size			)}
													vocab.update(self.added_tokens_encoder			)
													return vocab



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple[str]:





													'''simple docstring'''






													if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
																			logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
																			return
													__UpperCamelCase       =			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_			) and os.path.isfile(self.vocab_file			):
																			copyfile(self.vocab_file     ,	SCREAMING_SNAKE_CASE_			)
													elif not os.path.isfile(self.vocab_file			):
																			with open(SCREAMING_SNAKE_CASE_     ,	'''wb'''			) as fi:
																									__UpperCamelCase       =			self.sp_model.serialized_model_proto()
																									fi.write(SCREAMING_SNAKE_CASE_			)

													return (out_vocab_file,)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = False			)->   Union[List[int], List[List[int]], "torch.Tensor"]:





													'''simple docstring'''






													if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			self.preprocess_text(SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			self.sp_model.encode(SCREAMING_SNAKE_CASE_			)
													else:
																			__UpperCamelCase       =			[self.preprocess_text(SCREAMING_SNAKE_CASE_			) for t in text]
																			__UpperCamelCase       =			self.sp_model.encode(SCREAMING_SNAKE_CASE_			)

													if return_tensors is True or return_tensors == "pt":
																			__UpperCamelCase       =			torch.tensor(SCREAMING_SNAKE_CASE_			)

													return token_ids



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return self.sp_model.decode(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
													__UpperCamelCase       =			(
													    F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(SCREAMING_SNAKE_CASE_			) + F"{self.bos_token}Bot:"
													)
													return self.encode(text=SCREAMING_SNAKE_CASE_			)






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




