



from collections import OrderedDict
from typing import Mapping

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'camembert'







							def __init__(   self,			__a=3_0522,			__a=768,			__a=12,			__a=12,			__a=3072,			__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,			):

														'''simple docstring'''

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

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



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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





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


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



class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		BartphoTokenizer
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		True







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

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

														_lowerCAmelCase     :							Optional[int]    =    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     :							Optional[Any]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														tokenizer.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    "This is a là test"
														_lowerCAmelCase     :							Optional[int]    =    "This is a<unk><unk> test"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    BartphoTokenizer(__a,			self.monolingual_vocab_file,			**self.special_tokens_map)
														_lowerCAmelCase     :							List[Any]    =    "This is a là test"
														_lowerCAmelCase     :							str    =    "▁This ▁is ▁a ▁l à ▁t est".split()
														_lowerCAmelCase     :							str    =    tokenizer.tokenize(__a)
														self.assertListEqual(__a,			__a)

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