




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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__				:      Optional[int]	   = logging.get_logger(__name__)

lowercase__				:      List[str]	   = "▁"

lowercase__				:      Optional[Any]	   = {"vocab_file": "sentencepiece.bpe.model"}

lowercase__				:      Tuple	   = {
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowercase__				:      Any	   = {
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}




class       a__	( UpperCamelCase__				):
     a		:      Union[str, Any]		 =     VOCAB_FILES_NAMES
     a		:      Optional[Any]		 =     PRETRAINED_VOCAB_FILES_MAP
     a		:      str		 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a		:      List[str]		 =     ["""input_ids""", """attention_mask"""]







     def __init__(     self  ,		A  ,		A="<s>"  ,		A="</s>"  ,		A="</s>"  ,		A="<s>"  ,		A="<unk>"  ,		A="<pad>"  ,		A="<mask>"  ,		A = None  ,		**A  ,		)  ->   None:



          '''simple docstring'''



          a     =				AddedToken(A  ,		lstrip=A  ,		rstrip=A			) if isinstance(A  ,		A			) else mask_token

          a     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=A  ,		eos_token=A  ,		unk_token=A  ,		sep_token=A  ,		cls_token=A  ,		pad_token=A  ,		mask_token=A  ,		sp_model_kwargs=self.sp_model_kwargs  ,		**A  ,		)

          a     =				spm.SentencePieceProcessor(**self.sp_model_kwargs			)
          self.sp_model.Load(str(A			)			)
          a     =				vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          a     =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          a     =				1

          a     =				len(self.sp_model			) + self.fairseq_offset
          a     =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







     def __getstate__(     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.__dict__.copy()
          a     =				None
          a     =				self.sp_model.serialized_model_proto()
          return state







     def __setstate__(     self  ,		A			)  ->   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.LoadFromSerializedProto(self.sp_model_proto			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   List[int]:



          '''simple docstring'''



          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          a     =				[self.cls_token_id]
          a     =				[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None  ,		A = False			)  ->   List[int]:



          '''simple docstring'''



          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=A  ,		token_ids_a=A  ,		already_has_special_tokens=A			)

          if token_ids_a is None:
               return [1] + ([0] * len(A			)) + [1]
          return [1] + ([0] * len(A			)) + [1, 1] + ([0] * len(A			)) + [1]







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   List[int]:



          '''simple docstring'''



          a     =				[self.sep_token_id]
          a     =				[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep			) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				{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  ,		A			)  ->   List[str]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          a     =				self.sp_model.PieceToId(A			)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Dict:



          '''simple docstring'''



          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				"".join(A			).replace(A  ,		" "			).strip()
          return out_string







     def       lowerCAmelCase_    (     self  ,		A  ,		A = None			)  ->   Tuple[str]:



          '''simple docstring'''



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

          return (out_vocab_file,)







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       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          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			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

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

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

