
'''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


_lowercase    =		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Optional[Any]									=       BartphoTokenizer
						_lowercase		:				List[str]									=       False
						_lowercase		:				Optional[Any]									=       True




						def  _lowercase     ( self				):

												"""simple docstring"""



												super().setUp()

												_lowerCAmelCase  						=			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
												_lowerCAmelCase  						=			dict(zip(_lowercase   ,       range(len(_lowercase				)				)				)				)
												_lowerCAmelCase  						=			{"""unk_token""": """<unk>"""}

												_lowerCAmelCase  						=			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  						=			BartphoTokenizer(_lowercase   ,       self.monolingual_vocab_file   ,       **self.special_tokens_map				)
												tokenizer.save_pretrained(self.tmpdirname				)




						def  _lowercase     ( self   ,       **_lowercase				):

												"""simple docstring"""



												kwargs.update(self.special_tokens_map				)
												return BartphoTokenizer.from_pretrained(self.tmpdirname   ,       **_lowercase				)




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			"""This is a là test"""
												_lowerCAmelCase  						=			"""This is a<unk><unk> test"""
												return input_text, output_text




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BartphoTokenizer(_lowercase   ,       self.monolingual_vocab_file   ,       **self.special_tokens_map				)
												_lowerCAmelCase  						=			"""This is a là test"""
												_lowerCAmelCase  						=			"""▁This ▁is ▁a ▁l à ▁t est""".split()
												_lowerCAmelCase  						=			tokenizer.tokenize(_lowercase				)
												self.assertListEqual(_lowercase   ,       _lowercase				)

												_lowerCAmelCase  						=			tokens + [tokenizer.unk_token]
												_lowerCAmelCase  						=			[4, 5, 6, 3, 3, 7, 8, 3]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase				)   ,       _lowercase				)


'''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    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



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

												_lowerCAmelCase  						=			vocab_size
												_lowerCAmelCase  						=			hidden_size
												_lowerCAmelCase  						=			num_hidden_layers
												_lowerCAmelCase  						=			num_attention_heads
												_lowerCAmelCase  						=			intermediate_size
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			hidden_dropout_prob
												_lowerCAmelCase  						=			attention_probs_dropout_prob
												_lowerCAmelCase  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



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