





"""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_barthez import BarthezTokenizer
else:
    lowerCAmelCase_			=							None

lowerCAmelCase_			=							logging.get_logger(__name__)

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

lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_			=							{
    '''moussaKam/mbarthez''': 1_024,
    '''moussaKam/barthez''': 1_024,
    '''moussaKam/barthez-orangesum-title''': 1_024,
}

lowerCAmelCase_			=							'''▁'''



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]
       lowerCAmelCase__						 =  BarthezTokenizer






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

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

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






       def 							__A (   self    ,  a__    ,  a__ = 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 							__A (   self    ,  a__    ,  a__ = 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 							__A (   self    ,  a__    ,  a__ = 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(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__     ):
                     copyfile(self.vocab_file    ,  a__     )

              return (out_vocab_file,)













"""simple docstring"""






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """deberta-v2"""






       def __init__(   self    ,  a__=12_81_00    ,  a__=15_36    ,  a__=24    ,  a__=24    ,  a__=61_44    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=0    ,  a__=0.02    ,  a__=1E-7    ,  a__=False    ,  a__=-1    ,  a__=0    ,  a__=True    ,  a__=None    ,  a__=0    ,  a__="gelu"    ,  **a__    ,  ):
              super().__init__(**a__     )

              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     relative_attention
              _UpperCAmelCase     =     max_relative_positions
              _UpperCAmelCase     =     pad_token_id
              _UpperCAmelCase     =     position_biased_input

              # Backwards compatibility
              if type(a__     ) == str:
                     _UpperCAmelCase     =     [x.strip() for x in pos_att_type.lower().split('|'     )]

              _UpperCAmelCase     =     pos_att_type
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     layer_norm_eps

              _UpperCAmelCase     =     kwargs.get('pooler_hidden_size'    ,  a__     )
              _UpperCAmelCase     =     pooler_dropout
              _UpperCAmelCase     =     pooler_hidden_act



class 	lowerCAmelCase					(      snake_case							):






       @property
       def 							__A (   self     ):
              if self.task == "multiple-choice":
                     _UpperCAmelCase     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
              else:
                     _UpperCAmelCase     =     {0: 'batch', 1: 'sequence'}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
              else:
                     return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )






       @property
       def 							__A (   self     ):
              return 12






       def 							__A (   self    ,  a__    ,  a__ = -1    ,  a__ = -1    ,  a__ = -1    ,  a__ = False    ,  a__ = None    ,  a__ = 3    ,  a__ = 40    ,  a__ = 40    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     super().generate_dummy_inputs(preprocessor=a__    ,  framework=a__     )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs






