


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_roberta import RobertaTokenizer


__UpperCamelCase				:				List[Any]  				= logging.get_logger(__name__)

__UpperCamelCase				:				Optional[Any]  				= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase				:				Dict  				= {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCamelCase				:				Optional[int]  				= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}

class 					__magic_name__		(   __lowerCAmelCase):
     A:						List[Any]								=					VOCAB_FILES_NAMES
     A:						Tuple								=					PRETRAINED_VOCAB_FILES_MAP
     A:						Any								=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:						str								=					['input_ids', 'attention_mask']
     A:						Union[str, Any]								=					RobertaTokenizer


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





      '''simple docstring'''






      super().__init__(
          lowerCamelCase__							,		lowerCamelCase__							,		tokenizer_file=lowerCamelCase__							,		errors=lowerCamelCase__							,		bos_token=lowerCamelCase__							,		eos_token=lowerCamelCase__							,		sep_token=lowerCamelCase__							,		cls_token=lowerCamelCase__							,		unk_token=lowerCamelCase__							,		pad_token=lowerCamelCase__							,		mask_token=lowerCamelCase__							,		add_prefix_space=lowerCamelCase__							,		trim_offsets=lowerCamelCase__							,		**lowerCamelCase__							,		)

      UpperCamelCase__    :			List[Any]      				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''							,		lowerCamelCase__	) != add_prefix_space:
       UpperCamelCase__    :			Union[str, Any]      				=      getattr(lowerCamelCase__							,		pre_tok_state.pop('''type'''	)	)
       UpperCamelCase__    :			Optional[Any]      				=      add_prefix_space
       UpperCamelCase__    :			str      				=      pre_tok_class(**lowerCamelCase__	)

      UpperCamelCase__    :			Tuple      				=      add_prefix_space

      UpperCamelCase__    :			Union[str, Any]      				=      '''post_processor'''
      UpperCamelCase__    :			Tuple      				=      getattr(self.backend_tokenizer							,		lowerCamelCase__							,		lowerCamelCase__	)
      if tokenizer_component_instance:
       UpperCamelCase__    :			Union[str, Any]      				=      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__    :			List[Any]      				=      tuple(state['''sep''']	)
       if "cls" in state:
        UpperCamelCase__    :			Any      				=      tuple(state['''cls''']	)

       UpperCamelCase__    :			Dict      				=      False

       if state.get('''add_prefix_space'''							,		lowerCamelCase__	) != add_prefix_space:
        UpperCamelCase__    :			List[str]      				=      add_prefix_space
        UpperCamelCase__    :			List[str]      				=      True

       if state.get('''trim_offsets'''							,		lowerCamelCase__	) != trim_offsets:
        UpperCamelCase__    :			List[str]      				=      trim_offsets
        UpperCamelCase__    :			Dict      				=      True

       if changes_to_apply:
        UpperCamelCase__    :			Optional[Any]      				=      getattr(lowerCamelCase__							,		state.pop('''type'''	)	)
        UpperCamelCase__    :			Dict      				=      component_class(**lowerCamelCase__	)
        setattr(self.backend_tokenizer							,		lowerCamelCase__							,		lowerCamelCase__	)


     @property
     def    UpperCAmelCase__		(					self							:   Any	)     ->					str:





      '''simple docstring'''






      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    UpperCAmelCase__		(					self							:   int							,		lowerCamelCase__							:   Optional[Any]	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      AddedToken(lowerCamelCase__							,		lstrip=lowerCamelCase__							,		rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__							,		lowerCamelCase__	) else value
      UpperCamelCase__    :			List[str]      				=      value


     def    UpperCAmelCase__		(					self							:   List[str]							,		*lowerCamelCase__							:   List[str]							,		**lowerCamelCase__							:   Union[str, Any]	)     ->					BatchEncoding:





      '''simple docstring'''






      UpperCamelCase__    :			List[str]      				=      kwargs.get('''is_split_into_words'''							,		lowerCamelCase__	)
      assert self.add_prefix_space or not is_split_into_words, (
          F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*lowerCamelCase__							,		**lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   Optional[int]							,		*lowerCamelCase__							:   List[str]							,		**lowerCamelCase__							:   List[str]	)     ->					BatchEncoding:





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      kwargs.get('''is_split_into_words'''							,		lowerCamelCase__	)

      assert self.add_prefix_space or not is_split_into_words, (
          F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*lowerCamelCase__							,		**lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   Optional[int]							,		lowerCamelCase__							:   str							,		lowerCamelCase__							:   Union[str, Any] = None	)     ->					Tuple[str]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      self._tokenizer.model.save(lowerCamelCase__							,		name=lowerCamelCase__	)
      return tuple(lowerCamelCase__	)


     def    UpperCAmelCase__		(					self							:   Optional[Any]							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   Any=None	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[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    UpperCAmelCase__		(					self							:   Optional[int]							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   Tuple = None	)     ->					List[int]:





      '''simple docstring'''






      UpperCamelCase__    :			Dict      				=      [self.sep_token_id]
      UpperCamelCase__    :			Optional[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]







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       'xmod'



 def __init__(self ,   A=30_522 ,   A=768 ,   A=12 ,   A=12 ,   A=3_072 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=2 ,   A=0.02 ,   A=1E-12 ,   A=1 ,   A=0 ,   A=2 ,   A="absolute" ,   A=True ,   A=None ,   A=False ,   A=2 ,   A=False ,   A=True ,   A=True ,   A=("en_XX",) ,   A=None ,   **A ,   )    ->     List[str]:


      """simple docstring"""



      super().__init__(pad_token_id=A ,   bos_token_id=A ,   eos_token_id=A ,   **A       )

      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  intermediate_size
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  max_position_embeddings
      _a      =  type_vocab_size
      _a      =  initializer_range
      _a      =  layer_norm_eps
      _a      =  position_embedding_type
      _a      =  use_cache
      _a      =  classifier_dropout
      _a      =  pre_norm
      _a      =  adapter_reduction_factor
      _a      =  adapter_layer_norm
      _a      =  adapter_reuse_layer_norm
      _a      =  ln_before_adapter
      _a      =  list(A       )
      _a      =  default_language






class __A			(  A						):



 '''simple docstring'''







 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      if self.task == "multiple-choice":
           _a      =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
           _a      =  {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]       )



