


'''simple docstring'''

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


lowerCAmelCase				:Optional[Any]        =      logging.get_logger(__name__)
lowerCAmelCase				:int        =      {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase				: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''',
    }
}

lowerCAmelCase				:Union[str, Any]        =      {
    '''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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Union[str, Any]				   =    VOCAB_FILES_NAMES
		A_     :      Optional[Any]				   =    PRETRAINED_VOCAB_FILES_MAP
		A_     :      List[Any]				   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_     :      Union[str, Any]				   =    ["""input_ids""", """attention_mask"""]







		def __init__( self		:						Optional[int]  ,					_A		:						Union[str, Any]  ,					_A		:						Optional[Any]=False  ,					_A		:						List[Any]=False  ,					_A		:						Optional[Any]=False  ,					_A		:						str=None  ,					_A		:						Any=None  ,					_A		:						Optional[int]=None  ,					_A		:						Dict=None  ,					_A		:						Optional[Dict[str, Any]] = None  ,					**_A		:						Dict  ,					)  ->			None:
			__magic_name__	:   Dict					     =					{} if sp_model_kwargs is None else sp_model_kwargs

			__magic_name__	:   Optional[int]					     =					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'      )
				__magic_name__	:   Optional[Any]					     =					'None'

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

			super().__init__(
			    do_lower_case=_A  ,					remove_space=_A  ,					keep_accents=_A  ,					bos_token=_A  ,					eos_token=_A  ,					unk_token=_A  ,					pad_token=_A  ,					sp_model_kwargs=self.sp_model_kwargs  ,					**_A  ,					)

			__magic_name__	:   Optional[int]					     =					do_lower_case
			__magic_name__	:   Optional[int]					     =					remove_space
			__magic_name__	:   Optional[int]					     =					keep_accents
			__magic_name__	:   int					     =					vocab_file

			__magic_name__	:   str					     =					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
			self.sp_model.Load(_A      )

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

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







		def __getstate__( self		:						int      )  ->			int:
			__magic_name__	:   List[str]					     =					self.__dict__.copy()
			__magic_name__	:   Any					     =					None
			return state







		def __setstate__( self		:						List[Any]  ,					_A		:						int      )  ->			List[Any]:
			__magic_name__	:   Union[str, Any]					     =					d

			# for backward compatibility
			if not hasattr(self  ,					'sp_model_kwargs'      ):
				__magic_name__	:   Union[str, Any]					     =					{}

			__magic_name__	:   Optional[Any]					     =					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        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			int:
			return len(self.sp_model      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			str:
			__magic_name__	:   Optional[int]					     =					self.non_printing_characters_re.sub(''  ,					_A      )

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

			# NFC Unicode normalization
			__magic_name__	:   List[Any]					     =					unicodedata.normalize('NFC'  ,					_A      )
			return text







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					**_A		:						Optional[int]      )  ->			List[str]:
			__magic_name__	:   Optional[Any]					     =					self.preprocess_text(_A      )
			return self.sp_model.encode(_A  ,					out_type=_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						str      )  ->			int:
			return self.sp_model.PieceToId(_A      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						int      )  ->			str:
			return self.sp_model.IdToPiece(_A      )







		@staticmethod
		def        __lowerCAmelCase		( _A		:						str      )  ->			str:
			return out_string







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]      )  ->			str:
			__magic_name__	:   Optional[int]					     =					[]
			__magic_name__	:   List[str]					     =					''
			__magic_name__	:   Dict					     =					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(_A      ) + token
					__magic_name__	:   Optional[Any]					     =					True
					__magic_name__	:   List[str]					     =					[]
				else:
					current_sub_tokens.append(_A      )
					__magic_name__	:   int					     =					False
			out_string += self.sp_model.decode(_A      )

			return out_string







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Dict[str, int]:
			__magic_name__	:   int					     =					{self.convert_ids_to_tokens(_A      ): i for i in range(self.vocab_size      )}
			vocab.update(self.added_tokens_encoder      )
			return vocab







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						str  ,					_A		:						Optional[str] = None      )  ->			Tuple[str]:
			if not os.path.isdir(_A      ):
				logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
				return
			__magic_name__	:   str					     =					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:
					__magic_name__	:   int					     =					self.sp_model.serialized_model_proto()
					fi.write(_A      )

			return (out_vocab_file,)







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Union[str, List[str]]  ,					_A		:						Union[str, bool] = False      )  ->			Union[List[int], List[List[int]], "torch.Tensor"]:

			if isinstance(_A  ,					_A      ):
				__magic_name__	:   Union[str, Any]					     =					self.preprocess_text(_A      )
				__magic_name__	:   int					     =					self.sp_model.encode(_A      )
			else:
				__magic_name__	:   Tuple					     =					[self.preprocess_text(_A      ) for t in text]
				__magic_name__	:   Union[str, Any]					     =					self.sp_model.encode(_A      )

			if return_tensors is True or return_tensors == "pt":
				__magic_name__	:   str					     =					torch.tensor(_A      )

			return token_ids







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						Union[int, List[int]]      )  ->			str:
			return self.sp_model.decode(_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						"Conversation"      )  ->			List[int]:
			__magic_name__	:   Optional[Any]					     =					[F'User: {text}' if is_user else F'Bot: {text}' for is_user, text in conversation.iter_texts()]
			__magic_name__	:   int					     =					(
			    F'{self.eos_token}{self.bos_token}' + F'{self.bos_token}'.join(_A      ) + F'{self.bos_token}Bot:'
			)
			return self.encode(text=_A      )



'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Any  ,					*_A		:						List[Any]  ,					**_A		:						str      )  ->			None:
			warnings.warn(
			    'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
			    ' use BeitImageProcessor instead.'  ,					_A  ,					)
			super().__init__(*_A  ,					**_A      )