



"""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


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		"▁"

lowerCamelCase      =		{"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase      =		{
    "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"
        ),
    }
}

lowerCamelCase      =		{
    "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    lowercase__     (					_UpperCAmelCase   ):







       '''simple docstring'''


       UpperCamelCase		=  VOCAB_FILES_NAMES
       UpperCamelCase		=  PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase		=  ['''input_ids''', '''attention_mask''']



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


             '''simple docstring'''




             UpperCAmelCase_    				=							AddedToken(A_	,       lstrip=A_	,       rstrip=A_					) if isinstance(A_	,       A_					) else mask_token

             UpperCAmelCase_    				=							{} 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_	,       )

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

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



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


             '''simple docstring'''




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



       def __setstate__(							self    :						Dict	,       _UpperCAmelCase    :						Any					)				->		Optional[Any]:


             '''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.LoadFromSerializedProto(self.sp_model_proto					)



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						List[int]	,       _UpperCAmelCase    :						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]
             UpperCAmelCase_    				=							[self.cls_token_id]
             UpperCAmelCase_    				=							[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep



       def   lowercase__       (							self    :						Dict	,       _UpperCAmelCase    :						List[int]	,       _UpperCAmelCase    :						Optional[List[int]] = None	,       _UpperCAmelCase    :						bool = 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   lowercase__       (							self    :						List[Any]	,       _UpperCAmelCase    :						List[int]	,       _UpperCAmelCase    :						Optional[List[int]] = None					)				->		List[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							[self.sep_token_id]
             UpperCAmelCase_    				=							[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   lowercase__       (							self    :						Union[str, Any]					)				->		List[Any]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Tuple					)				->		str:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						int	,       _UpperCAmelCase    :						str					)				->		List[str]:


             '''simple docstring'''




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



       def   lowercase__       (							self    :						Optional[Any]	,       _UpperCAmelCase    :						Union[str, Any]					)				->		Dict:


             '''simple docstring'''




             if token in self.fairseq_tokens_to_ids:
                   return self.fairseq_tokens_to_ids[token]
             UpperCAmelCase_    				=							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   lowercase__       (							self    :						Union[str, Any]	,       _UpperCAmelCase    :						List[str]					)				->		Tuple:


             '''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   lowercase__       (							self    :						str	,       _UpperCAmelCase    :						List[Any]					)				->		Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_    				=							"".join(A_					).replace(A_	,       " "					).strip()
             return out_string







       def   lowercase__       (							self    :						Any	,       _UpperCAmelCase    :						str	,       _UpperCAmelCase    :						Optional[str] = None					)				->		Tuple[str]:


             '''simple docstring'''




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

             return (out_vocab_file,)




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase      =		{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		["""PerceiverFeatureExtractor"""]
  lowerCamelCase      =		["""PerceiverImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase      =		[
      """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PerceiverForImageClassificationConvProcessing""",
      """PerceiverForImageClassificationFourier""",
      """PerceiverForImageClassificationLearned""",
      """PerceiverForMaskedLM""",
      """PerceiverForMultimodalAutoencoding""",
      """PerceiverForOpticalFlow""",
      """PerceiverForSequenceClassification""",
      """PerceiverLayer""",
      """PerceiverModel""",
      """PerceiverPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
  from .tokenization_perceiver import PerceiverTokenizer

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_perceiver import PerceiverFeatureExtractor
    from .image_processing_perceiver import PerceiverImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_perceiver import (
        PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PerceiverForImageClassificationConvProcessing,
        PerceiverForImageClassificationFourier,
        PerceiverForImageClassificationLearned,
        PerceiverForMaskedLM,
        PerceiverForMultimodalAutoencoding,
        PerceiverForOpticalFlow,
        PerceiverForSequenceClassification,
        PerceiverLayer,
        PerceiverModel,
        PerceiverPreTrainedModel,
    )

else:
  import sys

  lowerCamelCase      =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




