



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__UpperCAmelCase	       =	logging.get_logger(__name__)


__UpperCAmelCase	       =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

__UpperCAmelCase	       =	{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Any       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : List[Any]       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : List[str]       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : Optional[int]       					=  ["input_ids", "attention_mask"]
       UpperCAmelCase__    : Dict       					=  BartTokenizer
       def __init__(      self,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_="replace",      SCREAMING_SNAKE_CASE_="<s>",      SCREAMING_SNAKE_CASE_="</s>",      SCREAMING_SNAKE_CASE_="</s>",      SCREAMING_SNAKE_CASE_="<s>",      SCREAMING_SNAKE_CASE_="<unk>",      SCREAMING_SNAKE_CASE_="<pad>",      SCREAMING_SNAKE_CASE_="<mask>",      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[Any]:
          super().__init__(
              SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      tokenizer_file=SCREAMING_SNAKE_CASE_,      errors=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      sep_token=SCREAMING_SNAKE_CASE_,      cls_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      mask_token=SCREAMING_SNAKE_CASE_,      add_prefix_space=SCREAMING_SNAKE_CASE_,      trim_offsets=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  Optional[Any]						     =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
          if pre_tok_state.get('add_prefix_space',      SCREAMING_SNAKE_CASE_	) != add_prefix_space:
             UpperCamelCase   :  Optional[int]						     =     getattr(SCREAMING_SNAKE_CASE_,      pre_tok_state.pop('type'	)	)
             UpperCamelCase   :  Dict						     =     add_prefix_space
             UpperCamelCase   :  str						     =     pre_tok_class(**SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          UpperCamelCase   :  Optional[Any]						     =     'post_processor'
          UpperCamelCase   :  Any						     =     getattr(self.backend_tokenizer,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          if tokenizer_component_instance:
             UpperCamelCase   :  Dict						     =     json.loads(tokenizer_component_instance.__getstate__()	)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                UpperCamelCase   :  Tuple						     =     tuple(state['sep']	)
             if "cls" in state:
                UpperCamelCase   :  str						     =     tuple(state['cls']	)

             UpperCamelCase   :  Optional[int]						     =     False

             if state.get('add_prefix_space',      SCREAMING_SNAKE_CASE_	) != add_prefix_space:
                UpperCamelCase   :  Union[str, Any]						     =     add_prefix_space
                UpperCamelCase   :  Optional[int]						     =     True

             if state.get('trim_offsets',      SCREAMING_SNAKE_CASE_	) != trim_offsets:
                UpperCamelCase   :  Union[str, Any]						     =     trim_offsets
                UpperCamelCase   :  Union[str, Any]						     =     True

             if changes_to_apply:
                UpperCamelCase   :  int						     =     getattr(SCREAMING_SNAKE_CASE_,      state.pop('type'	)	)
                UpperCamelCase   :  Any						     =     component_class(**SCREAMING_SNAKE_CASE_	)
                setattr(self.backend_tokenizer,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
       @property
       def  snake_case_	(      self	)					->		str:
          if self._mask_token is None:
             if self.verbose:
                logger.error('Using mask_token, but it is not set yet.'	)
             return None
          return str(self._mask_token	)
       @mask_token.setter
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  str						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else value
          UpperCamelCase   :  Any						     =     value
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		BatchEncoding:
          UpperCamelCase   :  Optional[int]						     =     kwargs.get('is_split_into_words',      SCREAMING_SNAKE_CASE_	)

          if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 'to use it with pretokenized inputs.'	)

          return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		BatchEncoding:
          UpperCamelCase   :  List[str]						     =     kwargs.get('is_split_into_words',      SCREAMING_SNAKE_CASE_	)

          if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
                 'to use it with pretokenized inputs.'	)

          return super()._encode_plus(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          UpperCamelCase   :  List[Any]						     =     self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,      name=SCREAMING_SNAKE_CASE_	)
          return tuple(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None	)					->		List[str]:
          UpperCamelCase   :  int						     =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
             return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  List[Any]						     =     [self.sep_token_id]
          UpperCamelCase   :  List[Any]						     =     [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]








from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : int       					=  "data2vec-text"
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_0522,      SCREAMING_SNAKE_CASE_=768,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=12,      SCREAMING_SNAKE_CASE_=3072,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_="absolute",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[int]:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     vocab_size
          UpperCamelCase   :  Tuple						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[str]						     =     intermediate_size
          UpperCamelCase   :  Optional[int]						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  int						     =     max_position_embeddings
          UpperCamelCase   :  List[str]						     =     type_vocab_size
          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  List[str]						     =     layer_norm_eps
          UpperCamelCase   :  List[str]						     =     position_embedding_type
          UpperCamelCase   :  Any						     =     use_cache
          UpperCamelCase   :  Any						     =     classifier_dropout






class     lowerCAmelCase_		(  a__							):
       @property
       def  snake_case_	(      self	)					->		Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             UpperCamelCase   :  List[Any]						     =     {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
             UpperCamelCase   :  int						     =     {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ]	)



