





"""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


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

_snake_case     :		List[str]       						= {
    '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',
    }
}

_snake_case     :		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 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							VOCAB_FILES_NAMES
					UpperCamelCase      =							PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase      =							['''input_ids''', '''attention_mask''']


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Union[str, Any]=False     ,       __UpperCamelCase			:Any=False     ,       __UpperCamelCase			:Any=False     ,       __UpperCamelCase			:Any=None     ,       __UpperCamelCase			:Dict=None     ,       __UpperCamelCase			:Optional[Any]=None     ,       __UpperCamelCase			:int=None     ,       __UpperCamelCase			:Optional[Dict[str, Any]] = None     ,       **__UpperCamelCase			:Optional[Any]     ,       ):
												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=__UpperCamelCase     ,       remove_space=__UpperCamelCase     ,       keep_accents=__UpperCamelCase     ,       bos_token=__UpperCamelCase     ,       eos_token=__UpperCamelCase     ,       unk_token=__UpperCamelCase     ,       pad_token=__UpperCamelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__UpperCamelCase     ,       )

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

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

												# 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(__UpperCamelCase     ,       list(range(0     ,       9		)		) + list(range(11     ,       32		)		) + list(range(1_27     ,       1_60		)		) + [1_60, 1_73, 82_03]		)		)}]"		)


					def __getstate__(					self			:str		):
												A		=  self.__dict__.copy()
												A		=  None
												return state


					def __setstate__(					self			:Tuple     ,       __UpperCamelCase			:Optional[Any]		):
												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 	lowerCamelCase       (					self			:int		):
												return len(self.sp_model		)


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:str		):
												A		=  self.non_printing_characters_re.sub(""     ,       __UpperCamelCase		)

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

												# NFC Unicode normalization
												A		=  unicodedata.normalize("NFC"     ,       __UpperCamelCase		)
												return text


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:str     ,       **__UpperCamelCase			:List[Any]		):
												A		=  self.preprocess_text(__UpperCamelCase		)
												return self.sp_model.encode(__UpperCamelCase     ,       out_type=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:str		):
												return self.sp_model.PieceToId(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:int		):
												return self.sp_model.IdToPiece(__UpperCamelCase		)


					@staticmethod
					def 	lowerCamelCase       (					__UpperCamelCase			:str		):
												return out_string


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:List[str]		):
												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(__UpperCamelCase		) + token
																										A		=  True
																										A		=  []
																			else:
																										current_sub_tokens.append(__UpperCamelCase		)
																										A		=  False
												out_string += self.sp_model.decode(__UpperCamelCase		)

												return out_string


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  {self.convert_ids_to_tokens(__UpperCamelCase		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Optional[str] = None		):
												if not os.path.isdir(__UpperCamelCase		):
																			logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																			return
												A		=  os.path.join(
												    __UpperCamelCase     ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

												return (out_vocab_file,)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Union[str, List[str]]     ,       __UpperCamelCase			:Union[str, bool] = False		):

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

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

												return token_ids


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Union[int, List[int]]		):
												return self.sp_model.decode(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:"Conversation"		):
												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(__UpperCamelCase		) + f"{self.bos_token}Bot:"
												)
												return self.encode(text=__UpperCamelCase		)









"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


