




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_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 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Dict   		=  VOCAB_FILES_NAMES
     lowerCAmelCase				:       Dict   		=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase				:       List[str]   		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase				:       List[str]   		=  ['input_ids', 'attention_mask']
     lowerCAmelCase				:       str   		=  XLMRobertaTokenizer
     def __init__(	self   :			Any					,    __snake_case   :			List[Any]=None					,    __snake_case   :			int=None					,    __snake_case   :			Optional[Any]="<s>"					,    __snake_case   :			Optional[int]="</s>"					,    __snake_case   :			str="</s>"					,    __snake_case   :			Optional[int]="<s>"					,    __snake_case   :			Optional[int]="<unk>"					,    __snake_case   :			str="<pad>"					,    __snake_case   :			Tuple="<mask>"					,    **__snake_case   :			Tuple					,    ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else mask_token

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

         UpperCAmelCase_							 =     vocab_file
         UpperCAmelCase_							 =     False if not self.vocab_file else True
     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			List[int]					,    __snake_case   :			Optional[List[int]] = None				):

         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   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			List[int]					,    __snake_case   :			Optional[List[int]] = None				):
         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]




     def   lowerCamelCase_   (	self   :			Any					,    __snake_case   :			str					,    __snake_case   :			Optional[str] = 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(__snake_case				):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
             return
         UpperCAmelCase_							 =     os.path.join(
             __snake_case					,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(__snake_case				):
             copyfile(self.vocab_file					,    __snake_case				)

         return (out_vocab_file,)











from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class 			a     (   _A			):

     '''simple docstring'''





     lowerCAmelCase				:       Any   		=  'MarkupLMFeatureExtractor'
     lowerCAmelCase				:       Optional[Any]   		=  ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
     lowerCAmelCase				:       List[Any]   		=  True
     def __call__(	self   :			Dict					,    __snake_case   :			Tuple=None					,    __snake_case   :			List[Any]=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Tuple=None					,    __snake_case   :			Any=None					,    __snake_case   :			bool = True					,    __snake_case   :			Union[bool, str, PaddingStrategy] = False					,    __snake_case   :			Union[bool, str, TruncationStrategy] = None					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			int = 0					,    __snake_case   :			Optional[int] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			Optional[bool] = None					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = False					,    __snake_case   :			bool = True					,    __snake_case   :			Optional[Union[str, TensorType]] = None					,    **__snake_case   :			Any					,    ):
         # first, create nodes and xpaths
         if self.parse_html:
             if html_strings is None:
                 raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''				)

             if nodes is not None or xpaths is not None or node_labels is not None:
                 raise ValueError(
                     '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''				)

             UpperCAmelCase_							 =     self.feature_extractor(__snake_case				)
             UpperCAmelCase_							 =     features['''nodes''']
             UpperCAmelCase_							 =     features['''xpaths''']
         else:
             if html_strings is not None:
                 raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''				)
             if nodes is None or xpaths is None:
                 raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''				)

        # # second, apply the tokenizer
         if questions is not None and self.parse_html:
             if isinstance(__snake_case					,    __snake_case				):
                 UpperCAmelCase_							 =     [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

         UpperCAmelCase_							 =     self.tokenizer(
             text=questions if questions is not None else nodes					,    text_pair=nodes if questions is not None else None					,    xpaths=__snake_case					,    node_labels=__snake_case					,    add_special_tokens=__snake_case					,    padding=__snake_case					,    truncation=__snake_case					,    max_length=__snake_case					,    stride=__snake_case					,    pad_to_multiple_of=__snake_case					,    return_token_type_ids=__snake_case					,    return_attention_mask=__snake_case					,    return_overflowing_tokens=__snake_case					,    return_special_tokens_mask=__snake_case					,    return_offsets_mapping=__snake_case					,    return_length=__snake_case					,    verbose=__snake_case					,    return_tensors=__snake_case					,    **__snake_case					,    )

         return encoded_inputs
     def   lowerCamelCase_   (	self   :			List[Any]					,    *__snake_case   :			Dict					,    **__snake_case   :			Optional[int]				):
         return self.tokenizer.batch_decode(*__snake_case					,    **__snake_case				)
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    *__snake_case   :			Any					,    **__snake_case   :			Dict				):
         return self.tokenizer.decode(*__snake_case					,    **__snake_case				)




     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     self.tokenizer.model_input_names
         return tokenizer_input_names





