
'''simple docstring'''







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
    lowerCAmelCase_      =   None


lowerCAmelCase_      =   logging.get_logger(__name__)

lowerCAmelCase_      =   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

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"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

lowerCAmelCase_      =   {
    "xlm-roberta-base": 5_1_2,
    "xlm-roberta-large": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-dutch": 5_1_2,
    "xlm-roberta-large-finetuned-conll02-spanish": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-english": 5_1_2,
    "xlm-roberta-large-finetuned-conll03-german": 5_1_2,
}







class 		snake_case_       (						A__						):



   """simple docstring"""





   __lowerCAmelCase	:							Optional[Any]								=VOCAB_FILES_NAMES
   __lowerCAmelCase	:							Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase	:							List[str]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase	:							Optional[Any]								=['''input_ids''', '''attention_mask''']
   __lowerCAmelCase	:							Dict								=XLMRobertaTokenizer







   def __init__( self			,   UpperCamelCase=None			,   UpperCamelCase=None			,   UpperCamelCase="<s>"			,   UpperCamelCase="</s>"			,   UpperCamelCase="</s>"			,   UpperCamelCase="<s>"			,   UpperCamelCase="<unk>"			,   UpperCamelCase="<pad>"			,   UpperCamelCase="<mask>"			,   **UpperCamelCase			,   ):
          # Mask token behave like a normal word, i.e. include the space before it
          lowerCamelCase__				   =      AddedToken(UpperCamelCase			,   lstrip=UpperCamelCase			,   rstrip=UpperCamelCase) if isinstance(UpperCamelCase			,   UpperCamelCase) else mask_token

          super().__init__(
              UpperCamelCase			,   tokenizer_file=UpperCamelCase			,   bos_token=UpperCamelCase			,   eos_token=UpperCamelCase			,   sep_token=UpperCamelCase			,   cls_token=UpperCamelCase			,   unk_token=UpperCamelCase			,   pad_token=UpperCamelCase			,   mask_token=UpperCamelCase			,   **UpperCamelCase			,   )

          lowerCamelCase__				   =      vocab_file
          lowerCamelCase__				   =      False if not self.vocab_file else True







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase = None):

          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     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase = None):
          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]







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase = None):
          if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                     "tokenizer.")

          if not os.path.isdir(UpperCamelCase):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""")
                 return
          lowerCamelCase__				   =      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):
                 copyfile(self.vocab_file			,   UpperCamelCase)

          return (out_vocab_file,)



'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

