



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase     :							Optional[Any]  							=				logging.get_logger(__name__)


__lowerCamelCase     :							List[str]  							=				"▁"

__lowerCamelCase     :							Union[str, Any]  							=				{"vocab_file": "spiece.model"}

__lowerCamelCase     :							int  							=				{
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

__lowerCamelCase     :							str  							=				{
    "google/reformer-crime-and-punishment": 524_288,
}





class  __magic_name__   (							A__						):
		lowercase      :       Tuple          =VOCAB_FILES_NAMES
		lowercase      :       Tuple          =PRETRAINED_VOCAB_FILES_MAP
		lowercase      :       str          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase      :       str          =['''input_ids''', '''attention_mask''']






		def __init__(						self      :     Tuple				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Any="</s>"				,		UpperCamelCase__      :     Optional[int]="<unk>"				,		UpperCamelCase__      :     int=[]				,		UpperCamelCase__      :     Optional[Dict[str, Any]] = None				,		**UpperCamelCase__      :     Union[str, Any]				,		)				->					None:
								'''simple docstring'''







								UpperCAmelCase         =     {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=UpperCamelCase__				,		unk_token=UpperCamelCase__				,		additional_special_tokens=UpperCamelCase__				,		sp_model_kwargs=self.sp_model_kwargs				,		**UpperCamelCase__				,		)

								UpperCAmelCase         =     vocab_file
								UpperCAmelCase         =     spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(UpperCamelCase__      )






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					str:
								'''simple docstring'''







								return self.sp_model.get_piece_size()






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[int]      )				->					Dict[str, int]:
								'''simple docstring'''







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






		def __getstate__(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







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






		def __setstate__(						self      :     int				,		UpperCamelCase__      :     str      )				->					List[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      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     str      )				->					List[str]:
								'''simple docstring'''







								return self.sp_model.encode(UpperCamelCase__				,		out_type=UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Any      )				->					str:
								'''simple docstring'''







								return self.sp_model.piece_to_id(UpperCamelCase__      )






		def SCREAMING_SNAKE_CASE_			(						self      :     str				,		UpperCamelCase__      :     Union[str, Any]      )				->					Any:
								'''simple docstring'''







								if index < self.sp_model.get_piece_size():
														UpperCAmelCase         =     self.sp_model.IdToPiece(UpperCamelCase__      )
								return token






		def SCREAMING_SNAKE_CASE_			(						self      :     int				,		UpperCamelCase__      :     Optional[int]      )				->					Optional[Any]:
								'''simple docstring'''







								UpperCAmelCase         =     []
								UpperCAmelCase         =     ""
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				out_string += self.sp_model.decode(UpperCamelCase__      ) + token
																				UpperCAmelCase         =     []
														else:
																				current_sub_tokens.append(UpperCamelCase__      )
								out_string += self.sp_model.decode(UpperCamelCase__      )
								return out_string.strip()







		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     str				,		UpperCamelCase__      :     Optional[str] = None      )				->					Tuple[str]:
								'''simple docstring'''







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

								return (out_vocab_file,)








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



