




'''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


_SCREAMING_SNAKE_CASE							:       Dict						=			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE							:       List[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",
    }
}

_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			{
    "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 			_snake_case		(     lowerCamelCase__							):
			lowerCAmelCase_					:				Optional[int]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Union[str, Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Any							=   ['input_ids', 'attention_mask']




			def __init__( self    ,							a__    ,							a__=False    ,							a__=False    ,							a__=False    ,							a__=None    ,							a__=None    ,							a__=None    ,							a__=None    ,							a__ = None    ,							**a__    ,							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										snake_case_      =				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"							)
																	snake_case_      =				"None"

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

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

										snake_case_      =				do_lower_case
										snake_case_      =				remove_space
										snake_case_      =				keep_accents
										snake_case_      =				vocab_file

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(lowercase__							)

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

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




			def __getstate__( self							)    ->    int:

										'''simple docstring'''




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




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

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

										snake_case_      =				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							)    ->    Dict:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.non_printing_characters_re.sub(""    ,							lowercase__							)

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

										# NFC Unicode normalization
										snake_case_      =				unicodedata.normalize("NFC"    ,							lowercase__							)
										return text




			def        lowerCAmelCase__    ( self    ,							a__    ,							**a__							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.preprocess_text(lowercase__							)
										return self.sp_model.encode(lowercase__    ,							out_type=lowercase__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										return self.sp_model.PieceToId(lowercase__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




										return self.sp_model.IdToPiece(lowercase__							)




			@staticmethod
			def        lowerCAmelCase__    ( a__							)    ->    str:

										'''simple docstring'''




										return out_string




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										snake_case_      =				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(lowercase__							) + token
																								snake_case_      =				True
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(lowercase__							)
																								snake_case_      =				False
										out_string += self.sp_model.decode(lowercase__							)

										return out_string




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    int:

										'''simple docstring'''




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

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

										return (out_vocab_file,)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = False							)    ->    Optional[Any]:

										'''simple docstring'''




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

										if return_tensors is True or return_tensors == "pt":
																	snake_case_      =				torch.tensor(lowercase__							)

										return token_ids




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




										return self.sp_model.decode(lowercase__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




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










'''simple docstring'''





def 					UpperCamelCase_(     snake_case							:	int = 1_0_0_0					):

							'''simple docstring'''






							snake_case_      =				3
							snake_case_      =				0
							while a < n:
														if a % 3 == 0 or a % 5 == 0:
																					result += a
														elif a % 1_5 == 0:
																					result -= a
														a += 1
							return result


if __name__ == "__main__":
		print(F"{solution() = }")




