



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_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  BartphoTokenizer
       UpperCAmelCase__    : Union[str, Any]       					=  False
       UpperCAmelCase__    : Dict       					=  True
       def  snake_case_	(      self	)					->		Tuple:
          super().setUp()

          UpperCamelCase   :  Union[str, Any]						     =     ['▁This', '▁is', '▁a', '▁t', 'est']
          UpperCamelCase   :  List[Any]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  Optional[Any]						     =     {'unk_token': '<unk>'}

          UpperCamelCase   :  str						     =     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"""	)

          UpperCamelCase   :  Tuple						     =     BartphoTokenizer(SCREAMING_SNAKE_CASE_,      self.monolingual_vocab_file,      **self.special_tokens_map	)
          tokenizer.save_pretrained(self.tmpdirname	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		List[str]:
          kwargs.update(self.special_tokens_map	)
          return BartphoTokenizer.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          UpperCamelCase   :  Dict						     =     'This is a là test'
          UpperCamelCase   :  List[Any]						     =     'This is a<unk><unk> test'
          return input_text, output_text






       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Dict						     =     BartphoTokenizer(SCREAMING_SNAKE_CASE_,      self.monolingual_vocab_file,      **self.special_tokens_map	)
          UpperCamelCase   :  Tuple						     =     'This is a là test'
          UpperCamelCase   :  List[Any]						     =     '▁This ▁is ▁a ▁l à ▁t est'.split()
          UpperCamelCase   :  str						     =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     tokens + [tokenizer.unk_token]
          UpperCamelCase   :  List[Any]						     =     [4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)








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),
              ]	)



