





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Tuple         =   '''lilt'''

       def __init__(		self: str					,   _SCREAMING_SNAKE_CASE: str=30522					,   _SCREAMING_SNAKE_CASE: int=768					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=12					,   _SCREAMING_SNAKE_CASE: Optional[int]=12					,   _SCREAMING_SNAKE_CASE: Optional[int]=3072					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="gelu"					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: str=0.1					,   _SCREAMING_SNAKE_CASE: List[Any]=512					,   _SCREAMING_SNAKE_CASE: Dict=2					,   _SCREAMING_SNAKE_CASE: Tuple=0.02					,   _SCREAMING_SNAKE_CASE: Optional[Any]=1e-12					,   _SCREAMING_SNAKE_CASE: str=0					,   _SCREAMING_SNAKE_CASE: Dict="absolute"					,   _SCREAMING_SNAKE_CASE: Dict=None					,   _SCREAMING_SNAKE_CASE: int=4					,   _SCREAMING_SNAKE_CASE: Optional[Any]=1024					,   **_SCREAMING_SNAKE_CASE: Optional[Any]					,   )       -> Optional[int]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =       vocab_size
              UpperCamelCase_          =       hidden_size
              UpperCamelCase_          =       num_hidden_layers
              UpperCamelCase_          =       num_attention_heads
              UpperCamelCase_          =       hidden_act
              UpperCamelCase_          =       intermediate_size
              UpperCamelCase_          =       hidden_dropout_prob
              UpperCamelCase_          =       attention_probs_dropout_prob
              UpperCamelCase_          =       max_position_embeddings
              UpperCamelCase_          =       type_vocab_size
              UpperCamelCase_          =       initializer_range
              UpperCamelCase_          =       layer_norm_eps
              UpperCamelCase_          =       position_embedding_type
              UpperCamelCase_          =       classifier_dropout
              UpperCamelCase_          =       channel_shrink_ratio
              UpperCamelCase_          =       max_ad_position_embeddings








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   XLMTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

       def lowercase (		self: Dict		)       -> Union[str, Any]:




              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase_          =  [
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  "<unk>",
              ]
              UpperCamelCase_          =  dict(zip(_SCREAMING_SNAKE_CASE					,   range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_          =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_          =  os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file					,   "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file					,   "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "lower newer"
              UpperCamelCase_          =  "lower newer"
              return input_text, output_text

       def lowercase (		self: int		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer(self.vocab_file					,   self.merges_file		)

              UpperCamelCase_          =  "lower"
              UpperCamelCase_          =  ["low", "er</w>"]
              UpperCamelCase_          =  tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokens + ["<unk>"]
              UpperCamelCase_          =  [14, 15, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase (		self: Optional[int]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  XLMTokenizer.from_pretrained("xlm-mlm-en-2048"		)

              UpperCamelCase_          =  tokenizer.encode("sequence builders"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.encode("multi-sequence build"					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [1]
              assert encoded_pair == [0] + text + [1] + text_a + [1]

