



"""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


A    =			logging.get_logger(__name__)
A    =			{'vocab_file': 'spiece.model'}

A    =			{
    '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    =			{
    '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      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Tuple 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Union[str, Any] 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	Any 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	Optional[Any] 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    int      ,							snake_case  :    List[str]      ,							snake_case  :    str=False      ,							snake_case  :    List[Any]=False      ,							snake_case  :    str=False      ,							snake_case  :    List[str]=None      ,							snake_case  :    List[str]=None      ,							snake_case  :    List[str]=None      ,							snake_case  :    int=None      ,							snake_case  :    Optional[Dict[str, Any]] = None      ,							**snake_case  :    List[str]      ,							)			->						None:






									'''simple docstring'''

									A         =					{} if sp_model_kwargs is None else sp_model_kwargs

									A         =					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'				)
																A         =					'None'

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

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

									A         =					do_lower_case
									A         =					remove_space
									A         =					keep_accents
									A         =					vocab_file

									A         =					spm.SentencePieceProcessor(**self.sp_model_kwargs				)
									self.sp_model.Load(snake_case				)

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

									# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
									A         =					re.compile(
									    f"""[{''.join(map(snake_case      ,							list(range(0      ,							9				)				) + list(range(11      ,							32				)				) + list(range(127      ,							160				)				) + [160, 173, 8_203]				)				)}]"""				)



		def __getstate__( self  :    Any				)			->						str:






									'''simple docstring'''

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



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






									'''simple docstring'''

									A         =					d

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

									A         =					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  :    str				)			->						int:






									'''simple docstring'''

									return len(self.sp_model				)



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    str				)			->						str:






									'''simple docstring'''

									A         =					self.non_printing_characters_re.sub(''      ,							snake_case				)

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

									# NFC Unicode normalization
									A         =					unicodedata.normalize('NFC'      ,							snake_case				)
									return text



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






									'''simple docstring'''

									A         =					self.preprocess_text(snake_case				)
									return self.sp_model.encode(snake_case      ,							out_type=snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    str				)			->						int:






									'''simple docstring'''

									return self.sp_model.PieceToId(snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    int				)			->						str:






									'''simple docstring'''

									return self.sp_model.IdToPiece(snake_case				)



		@staticmethod
		def 					A_		( snake_case  :    str				)			->						str:






									'''simple docstring'''

									return out_string



		def 					A_		( self  :    Any      ,							snake_case  :    List[str]				)			->						str:






									'''simple docstring'''

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

									return out_string



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






									'''simple docstring'''

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



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

									if not os.path.isdir(snake_case				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																return
									A         =					os.path.join(
									    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

									if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case				) and os.path.isfile(self.vocab_file				):
																copyfile(self.vocab_file      ,							snake_case				)
									elif not os.path.isfile(self.vocab_file				):
																with open(snake_case      ,							'wb'				) as fi:
																							A         =					self.sp_model.serialized_model_proto()
																							fi.write(snake_case				)

									return (out_vocab_file,)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Union[str, List[str]]      ,							snake_case  :    Union[str, bool] = False				)			->						Union[List[int], List[List[int]], "torch.Tensor"]:






									'''simple docstring'''

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

									if return_tensors is True or return_tensors == "pt":
																A         =					torch.tensor(snake_case				)

									return token_ids



		def 					A_		( self  :    Dict      ,							snake_case  :    Union[int, List[int]]				)			->						str:






									'''simple docstring'''

									return self.sp_model.decode(snake_case				)



		def 					A_		( self  :    int      ,							snake_case  :    "Conversation"				)			->						List[int]:






									'''simple docstring'''

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








"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							float:

							def get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			) -> str:
														A         =					[]
														A         =					min(len(_stra			)				,      len(_stra			)			) // 2
														for i, l in enumerate(_stra			):
																					A         =					int(max(0				,      i - limit			)			)
																					A         =					int(min(i + limit + 1				,      len(_stra			)			)			)
																					if l in _stra[left:right]:
																												matched.append(lowerCamelCase__			)
																												A         =					f"""{_stra[0:_stra.index(lowerCamelCase__			)]} {_stra[_stra.index(lowerCamelCase__			) + 1:]}"""

														return "".join(lowerCamelCase__			)

							# matching characters
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					len(lowerCamelCase__			)

							# transposition
							A         =					(
							    len([(ca, ca) for ca, ca in zip(lowerCamelCase__				,      lowerCamelCase__			) if ca != ca]			) // 2
							)

							if not match_count:
														A         =					0.0
							else:
														A         =					(
														    1
														    / 3
														    * (
														        match_count / len(lowerCamelCase__			)
														        + match_count / len(lowerCamelCase__			)
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							A         =					0
							for ca, ca in zip(stra[:4]				,      stra[:4]			):
														if ca == ca:
																					prefix_len += 1
														else:
																					break

							return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(jaro_winkler('hello', 'world'))



