



'''simple docstring'''





import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase											=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=BartphoTokenizer
  a_												=False
  a_												=True




  def 						_lowercase     ( self      :	Optional[int]   )					->					Any:
    super().setUp()

    __lowerCamelCase       :       List[str]           = ['▁This', '▁is', '▁a', '▁t', 'est']
    __lowerCamelCase       :       str           = dict(zip(_a			,	range(len(_a   )   )   )   )
    __lowerCamelCase       :       List[str]           = {'unk_token': '<unk>'}

    __lowerCamelCase       :       List[Any]           = os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['monolingual_vocab_file']   )
    with open(self.monolingual_vocab_file			,	'w'			,	encoding='utf-8'   ) as fp:
      for token in vocab_tokens:
        fp.write(f'{token} {vocab_tokens[token]}\n'   )

    __lowerCamelCase       :       List[str]           = BartphoTokenizer(_a			,	self.monolingual_vocab_file			,	**self.special_tokens_map   )
    tokenizer.save_pretrained(self.tmpdirname   )




  def 						_lowercase     ( self      :	str			,	**_a      :	Union[str, Any]   )					->					Optional[int]:
    kwargs.update(self.special_tokens_map   )
    return BartphoTokenizer.from_pretrained(self.tmpdirname			,	**_a   )




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	str   )					->					Tuple:
    __lowerCamelCase       :       List[Any]           = 'This is a là test'
    __lowerCamelCase       :       Tuple           = 'This is a<unk><unk> test'
    return input_text, output_text




  def 						_lowercase     ( self      :	Any   )					->					Tuple:
    __lowerCamelCase       :       Any           = BartphoTokenizer(_a			,	self.monolingual_vocab_file			,	**self.special_tokens_map   )
    __lowerCamelCase       :       List[Any]           = 'This is a là test'
    __lowerCamelCase       :       Optional[Any]           = '▁This ▁is ▁a ▁l à ▁t est'.split()
    __lowerCamelCase       :       Optional[Any]           = tokenizer.tokenize(_a   )
    self.assertListEqual(_a			,	_a   )

    __lowerCamelCase       :       str           = tokens + [tokenizer.unk_token]
    __lowerCamelCase       :       List[str]           = [4, 5, 6, 3, 3, 7, 8, 3]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_a   )			,	_a   )











'''simple docstring'''





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											=						{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""xlm-roberta"""




  def __init__( self      :	Any			,	_a      :	Optional[Any]=3_0522			,	_a      :	Optional[Any]=768			,	_a      :	int=12			,	_a      :	Tuple=12			,	_a      :	str=3072			,	_a      :	List[Any]="gelu"			,	_a      :	int=0.1			,	_a      :	Optional[int]=0.1			,	_a      :	Optional[int]=512			,	_a      :	List[Any]=2			,	_a      :	Optional[Any]=0.02			,	_a      :	str=1e-12			,	_a      :	str=1			,	_a      :	str=0			,	_a      :	Optional[Any]=2			,	_a      :	Optional[int]="absolute"			,	_a      :	int=True			,	_a      :	Tuple=None			,	**_a      :	Any			,	)					->					Dict:
    super().__init__(pad_token_id=_a			,	bos_token_id=_a			,	eos_token_id=_a			,	**_a   )

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






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




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






