




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:
    snake_case_					:					Any       						=			None


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

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

snake_case_					:					List[str]       						=			{
    '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'
        ),
    },
}

snake_case_					:					int       						=			{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}







class __snake_case     (     SCREAMING_SNAKE_CASE__						):
   UpperCAmelCase__    :       Tuple		=    VOCAB_FILES_NAMES
   UpperCAmelCase__    :       Any		=    PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__    :       Union[str, Any]		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__    :       Tuple		=    ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__    :       Dict		=    XLMRobertaTokenizer







   def __init__( self						:		int   ,		_snake_case						:		int=None   ,		_snake_case						:		Optional[Any]=None   ,		_snake_case						:		Optional[Any]="<s>"   ,		_snake_case						:		Tuple="</s>"   ,		_snake_case						:		Dict="</s>"   ,		_snake_case						:		List[str]="<s>"   ,		_snake_case						:		List[str]="<unk>"   ,		_snake_case						:		Tuple="<pad>"   ,		_snake_case						:		List[Any]="<mask>"   ,		**_snake_case						:		List[str]   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				AddedToken(lowerCAmelCase_   ,		lstrip=lowerCAmelCase_   ,		rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_   ,		lowerCAmelCase_) else mask_token

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

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







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		List[int]   ,		_snake_case						:		Optional[List[int]] = None):

        """simple docstring"""


        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						:		Dict   ,		_snake_case						:		List[int]   ,		_snake_case						:		Optional[List[int]] = None):

        """simple docstring"""


        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						:		int   ,		_snake_case						:		str   ,		_snake_case						:		Optional[str] = None):

        """simple docstring"""


        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(lowerCAmelCase_):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
             return
        UpperCAmelCase_		=				os.path.join(
            lowerCAmelCase_   ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

        return (out_vocab_file,)








from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

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



