






'''simple docstring'''





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


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

__A							:							Union[str, Any]  							=		"""▁"""

__A							:							Optional[int]  							=		{"""vocab_file""": """sentencepiece.bpe.model"""}

__A							:							Optional[int]  							=		{
    """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"""
        ),
    }
}

__A							:							Any  							=		{
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							VOCAB_FILES_NAMES
    lowerCAmelCase__      				=							PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase__      				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase__      				=							["input_ids", "attention_mask"]

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Union[str, Any]="<s>"				,     __lowerCamelCase     :     Optional[Any]="</s>"				,     __lowerCamelCase     :     Optional[int]="</s>"				,     __lowerCamelCase     :     int="<s>"				,     __lowerCamelCase     :     int="<unk>"				,     __lowerCamelCase     :     int="<pad>"				,     __lowerCamelCase     :     str="<mask>"				,     __lowerCamelCase     :     Optional[Dict[str, Any]] = None				,     **__lowerCamelCase     :     str				,     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     AddedToken(__lowerCamelCase				,     lstrip=__lowerCamelCase				,     rstrip=__lowerCamelCase     ) if isinstance(__lowerCamelCase				,     __lowerCamelCase     ) else mask_token

     lowerCamelCase__  =     {} if sp_model_kwargs is None else sp_model_kwargs

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

     lowerCamelCase__  =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(str(__lowerCamelCase     )     )
     lowerCamelCase__  =     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
     lowerCamelCase__  =     {"<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
     lowerCamelCase__  =     1

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

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

     '''simple docstring'''




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

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

     '''simple docstring'''




     lowerCamelCase__  =     d

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

     lowerCamelCase__  =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None     )							->							List[int]:

     '''simple docstring'''




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

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None				,     __lowerCamelCase     :     bool = False     )							->							List[int]:

     '''simple docstring'''




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

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

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None     )							->							List[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [self.sep_token_id]
     lowerCamelCase__  =     [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    a__				(					self     :     Optional[int]     )							->							Union[str, Any]:

     '''simple docstring'''




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

    def    a__				(					self     :     str     )							->							Optional[int]:

     '''simple docstring'''




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

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

     '''simple docstring'''




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

    def    a__				(					self     :     int				,     __lowerCamelCase     :     Dict     )							->							List[Any]:

     '''simple docstring'''




     if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
     lowerCamelCase__  =     self.sp_model.PieceToId(__lowerCamelCase     )

     # 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    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Tuple     )							->							List[Any]:

     '''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    a__				(					self     :     List[str]				,     __lowerCamelCase     :     List[str]     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     "".join(__lowerCamelCase     ).replace(__lowerCamelCase				,     " "     ).strip()
     return out_string




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

     '''simple docstring'''




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

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

     return (out_vocab_file,)









'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
 import torch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"char"
    lowerCAmelCase__      				=							"bpe"
    lowerCAmelCase__      				=							"wp"
__A							:							Any  							=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							["image_processor", "char_tokenizer"]
    lowerCAmelCase__      				=							"ViTImageProcessor"
    lowerCAmelCase__      				=							"MgpstrTokenizer"

    def __init__(					self     :     Any				,     __lowerCamelCase     :     Optional[Any]=None				,     __lowerCamelCase     :     List[str]=None				,     **__lowerCamelCase     :     Any     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."				,     __lowerCamelCase				,     )
      lowerCamelCase__  =     kwargs.pop("feature_extractor"     )

     lowerCamelCase__  =     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."     )
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."     )

     lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("gpt2"     )
     lowerCamelCase__  =     AutoTokenizer.from_pretrained("bert-base-uncased"     )

     super().__init__(__lowerCamelCase				,     __lowerCamelCase     )

    def __call__(					self     :     List[str]				,     __lowerCamelCase     :     Union[str, Any]=None				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     str=None				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     if images is None and text is None:
      raise ValueError("You need to specify either an `images` or `text` input to process."     )

     if images is not None:
      lowerCamelCase__  =     self.image_processor(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )
     if text is not None:
      lowerCamelCase__  =     self.char_tokenizer(__lowerCamelCase				,     return_tensors=__lowerCamelCase				,     **__lowerCamelCase     )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      lowerCamelCase__  =     encodings["input_ids"]
      return inputs

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     sequences
     lowerCamelCase__  =     char_preds.size(0     )

     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "char"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "bpe"     )
     lowerCamelCase__  ,	lowerCamelCase__  =     self._decode_helper(__lowerCamelCase				,     "wp"     )

     lowerCamelCase__  =     []
     lowerCamelCase__  =     []
     for i in range(__lowerCamelCase     ):
      lowerCamelCase__  =     [char_scores[i], bpe_scores[i], wp_scores[i]]
      lowerCamelCase__  =     [char_strs[i], bpe_strs[i], wp_strs[i]]
      lowerCamelCase__  =     scores.index(max(__lowerCamelCase     )     )
      final_strs.append(strs[max_score_index]     )
      final_scores.append(scores[max_score_index]     )

     lowerCamelCase__  =     {}
     lowerCamelCase__  =     final_strs
     lowerCamelCase__  =     final_scores
     lowerCamelCase__  =     char_strs
     lowerCamelCase__  =     bpe_strs
     lowerCamelCase__  =     wp_strs
     return out

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     Any     )							->							Optional[int]:

     '''simple docstring'''




     if format == DecodeType.CHARACTER:
      lowerCamelCase__  =     self.char_decode
      lowerCamelCase__  =     1
      lowerCamelCase__  =     "[s]"
     elif format == DecodeType.BPE:
      lowerCamelCase__  =     self.bpe_decode
      lowerCamelCase__  =     2
      lowerCamelCase__  =     "#"
     elif format == DecodeType.WORDPIECE:
      lowerCamelCase__  =     self.wp_decode
      lowerCamelCase__  =     102
      lowerCamelCase__  =     "[SEP]"
     else:
      raise ValueError(f'''Format {format} is not supported.'''     )

     lowerCamelCase__  ,	lowerCamelCase__  =     [], []
     lowerCamelCase__  =     pred_logits.size(0     )
     lowerCamelCase__  =     pred_logits.size(1     )
     lowerCamelCase__  ,	lowerCamelCase__  =     pred_logits.topk(1				,     dim=-1				,     largest=__lowerCamelCase				,     sorted=__lowerCamelCase     )
     lowerCamelCase__  =     preds_index.view(-1				,     __lowerCamelCase     )[:, 1:]
     lowerCamelCase__  =     decoder(__lowerCamelCase     )
     lowerCamelCase__  ,	lowerCamelCase__  =     torch.nn.functional.softmax(__lowerCamelCase				,     dim=2     ).max(dim=2     )
     lowerCamelCase__  =     preds_max_prob[:, 1:]

     for index in range(__lowerCamelCase     ):
      lowerCamelCase__  =     preds_str[index].find(__lowerCamelCase     )
      lowerCamelCase__  =     preds_str[index][:pred_eos]
      lowerCamelCase__  =     preds_index[index].cpu().tolist()
      lowerCamelCase__  =     pred_index.index(__lowerCamelCase     ) if eos_token in pred_index else -1
      lowerCamelCase__  =     preds_max_prob[index][: pred_eos_index + 1]
      lowerCamelCase__  =     pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(__lowerCamelCase     )
      conf_scores.append(__lowerCamelCase     )

     return dec_strs, conf_scores

    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Tuple     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Tuple     )							->							Union[str, Any]:

     '''simple docstring'''




     return self.bpe_tokenizer.batch_decode(__lowerCamelCase     )




    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     int     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [seq.replace(" "				,     ""     ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase     )]
     return decode_strs

