



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a = "▁",			__a = True,			__a = "<unk>",			__a = "</s>",			__a = "<pad>",			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    {
														    "pad": {"id": 0, "token": pad_token},
														    "eos": {"id": 1, "token": eos_token},
														    "unk": {"id": 2, "token": unk_token},
														}

														_lowerCAmelCase     :							str    =    [None] * len(self.special_tokens)
														for token_dict in self.special_tokens.values():
																					_lowerCAmelCase     :							Dict    =    token_dict["token"]

														_lowerCAmelCase     :							int    =    Tokenizer(Unigram())

														_lowerCAmelCase     :							Any    =    normalizers.Sequence(
														    [
														        normalizers.Nmt(),
														        normalizers.NFKC(),
														        normalizers.Replace(Regex(" {2,}"),			" "),
														        normalizers.Lowercase(),
														    ])
														_lowerCAmelCase     :							Tuple    =    pre_tokenizers.Sequence(
														    [
														        pre_tokenizers.Metaspace(replacement=__a,			add_prefix_space=__a),
														        pre_tokenizers.Digits(individual_digits=__a),
														        pre_tokenizers.Punctuation(),
														    ])
														_lowerCAmelCase     :							List[str]    =    decoders.Metaspace(replacement=__a,			add_prefix_space=__a)

														_lowerCAmelCase     :							Tuple    =    TemplateProcessing(
														    single=f"$A {self.special_tokens['eos']['token']}",			special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])],			)

														_lowerCAmelCase     :							List[Any]    =    {
														    "model": "SentencePieceUnigram",
														    "replacement": replacement,
														    "add_prefix_space": add_prefix_space,
														}

														super().__init__(__a,			__a)







							def    snake_case__      (   self,			__a,			__a = 8000,			__a = True,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    trainers.UnigramTrainer(
														    vocab_size=__a,			special_tokens=self.special_tokens_list,			show_progress=__a,			)

														if isinstance(__a,			__a):
																					_lowerCAmelCase     :							Union[str, Any]    =    [files]
														self._tokenizer.train(__a,			trainer=__a)

														self.add_unk_id()







							def    snake_case__      (   self,			__a,			__a = 8000,			__a = True,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    trainers.UnigramTrainer(
														    vocab_size=__a,			special_tokens=self.special_tokens_list,			show_progress=__a,			)

														self._tokenizer.train_from_iterator(__a,			trainer=__a)

														self.add_unk_id()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    json.loads(self._tokenizer.to_str())

														_lowerCAmelCase     :							Tuple    =    self.special_tokens["unk"]["id"]

														_lowerCAmelCase     :							Any    =    Tokenizer.from_str(json.dumps(__a))





'''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),
          ]       )



