

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


_a:     Dict							=      logging.get_logger(__name__)
_a:     Any							=      {"""vocab_file""": """spiece.model"""}

_a:     Dict							=      {
    """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""",
    }
}

_a:     Tuple							=      {
    """AI-Sweden/gpt-sw3-126m""": 2048,
    """AI-Sweden/gpt-sw3-350m""": 2048,
    """AI-Sweden/gpt-sw3-1.6b""": 2048,
    """AI-Sweden/gpt-sw3-6.7b""": 2048,
    """AI-Sweden/gpt-sw3-20b""": 2048,
}

class     __UpperCamelCase     (       SCREAMING_SNAKE_CASE__    ):
	SCREAMING_SNAKE_CASE__     					=  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__     					=  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__     					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__     					=  ['''input_ids''', '''attention_mask''']




	def __init__(		self						:    str		,						lowerCAmelCase						:    Dict		,						lowerCAmelCase						:    List[Any]=False		,						lowerCAmelCase						:    Dict=False		,						lowerCAmelCase						:    str=False		,						lowerCAmelCase						:    str=None		,						lowerCAmelCase						:    int=None		,						lowerCAmelCase						:    Any=None		,						lowerCAmelCase						:    List[str]=None		,						lowerCAmelCase						:    List[str] = None		,						**lowerCAmelCase						:    Dict		,						):


		'''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=__lowerCAmelCase		,						remove_space=__lowerCAmelCase		,						keep_accents=__lowerCAmelCase		,						bos_token=__lowerCAmelCase		,						eos_token=__lowerCAmelCase		,						unk_token=__lowerCAmelCase		,						pad_token=__lowerCAmelCase		,						sp_model_kwargs=self.sp_model_kwargs		,						**__lowerCAmelCase		,						)

		UpperCAmelCase_												=      do_lower_case
		UpperCAmelCase_												=      remove_space
		UpperCAmelCase_												=      keep_accents
		UpperCAmelCase_												=      vocab_file

		UpperCAmelCase_												=      spm.SentencePieceProcessor(**self.sp_model_kwargs				)
		self.sp_model.Load(__lowerCAmelCase				)

		# 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(__lowerCAmelCase		,						list(range(0		,						9				)				) + list(range(11		,						32				)				) + list(range(127		,						160				)				) + [160, 173, 8_203]				)				)}]"				)




	def __getstate__(		self						:    str				):


		'''simple docstring'''







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




	def __setstate__(		self						:    List[str]		,						lowerCAmelCase						:    str				):


		'''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						:    Tuple				):


		'''simple docstring'''







		return len(self.sp_model				)




	def    __A       (		self						:    Optional[int]		,						lowerCAmelCase						:    List[Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.non_printing_characters_re.sub(""		,						__lowerCAmelCase				)

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

		# NFC Unicode normalization
		UpperCAmelCase_												=      unicodedata.normalize("NFC"		,						__lowerCAmelCase				)
		return text




	def    __A       (		self						:    Tuple		,						lowerCAmelCase						:    List[str]		,						**lowerCAmelCase						:    Any				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.preprocess_text(__lowerCAmelCase				)
		return self.sp_model.encode(__lowerCAmelCase		,						out_type=__lowerCAmelCase				)




	def    __A       (		self						:    List[str]		,						lowerCAmelCase						:    Optional[Any]				):


		'''simple docstring'''







		return self.sp_model.PieceToId(__lowerCAmelCase				)




	def    __A       (		self						:    Union[str, Any]		,						lowerCAmelCase						:    Any				):


		'''simple docstring'''







		return self.sp_model.IdToPiece(__lowerCAmelCase				)




	@staticmethod
	def    __A       (		lowerCAmelCase						:    Optional[Any]				):


		'''simple docstring'''







		return out_string




	def    __A       (		self						:    Any		,						lowerCAmelCase						:    List[Any]				):


		'''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(__lowerCAmelCase				) + token
				UpperCAmelCase_												=      True
				UpperCAmelCase_												=      []
			else:
				current_sub_tokens.append(__lowerCAmelCase				)
				UpperCAmelCase_												=      False
		out_string += self.sp_model.decode(__lowerCAmelCase				)

		return out_string




	def    __A       (		self						:    Union[str, Any]				):


		'''simple docstring'''







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




	def    __A       (		self						:    Tuple		,						lowerCAmelCase						:    Union[str, Any]		,						lowerCAmelCase						:    Tuple = None				):


		'''simple docstring'''







		if not os.path.isdir(__lowerCAmelCase				):
			logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
			return
		UpperCAmelCase_												=      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:
				UpperCAmelCase_												=      self.sp_model.serialized_model_proto()
				fi.write(__lowerCAmelCase				)

		return (out_vocab_file,)




	def    __A       (		self						:    Union[str, Any]		,						lowerCAmelCase						:    Dict		,						lowerCAmelCase						:    int = False				):


		'''simple docstring'''







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

		if return_tensors is True or return_tensors == "pt":
			UpperCAmelCase_												=      torch.tensor(__lowerCAmelCase				)

		return token_ids




	def    __A       (		self						:    Dict		,						lowerCAmelCase						:    Optional[int]				):


		'''simple docstring'''







		return self.sp_model.decode(__lowerCAmelCase				)




	def    __A       (		self						:    Any		,						lowerCAmelCase						:    str				):


		'''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(__lowerCAmelCase				) + F"{self.bos_token}Bot:"
		)
		return self.encode(text=__lowerCAmelCase				)







'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @staticmethod
 @abstractmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase			):
       raise NotImplementedError()





 @abstractmethod
 def 	_UpperCAmelCase							(				self			):
       raise NotImplementedError()






