import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    unittest.TestCase		):
   __lowerCAmelCase											=							MPNetTokenizer
   __lowerCAmelCase											=							MPNetTokenizerFast
   __lowerCAmelCase											=							True
   __lowerCAmelCase											=							True







   def 	lowerCamelCase_  (    self	:	Any							):





    """simple docstring"""


    super().setUp()

    UpperCamelCase      =			[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """[PAD]""",
        """[MASK]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    UpperCamelCase      =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]							)
    with open(self.vocab_file      ,      """w"""      ,      encoding="""utf-8"""							) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	Union[str, Any]							):





    """simple docstring"""


    UpperCamelCase      =			"""UNwant\u00E9d,running"""
    UpperCamelCase      =			"""unwanted, running"""
    return input_text, output_text







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    UpperCamelCase      =			self.tokenizer_class(self.vocab_file							)

    UpperCamelCase      =			tokenizer.tokenize("""UNwant\u00E9d,running"""							)
    self.assertListEqual(lowerCamelCase_      ,      ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_							)      ,      [9, 6, 7, 12, 10, 11]							)







   @slow
   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    UpperCamelCase      =			self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""							)

    UpperCamelCase      =			tokenizer.encode("""sequence builders"""      ,      add_special_tokens=lowerCamelCase_							)
    UpperCamelCase      =			tokenizer.encode("""multi-sequence build"""      ,      add_special_tokens=lowerCamelCase_							)

    UpperCamelCase      =			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_							)
    UpperCamelCase      =			tokenizer.build_inputs_with_special_tokens(lowerCamelCase_      ,      lowerCamelCase_							)

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   		=			{
    """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/config.json""",
    """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json""",
    """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/config.json""",
    """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json""",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""splinter"""







   def __init__(    self	:	List[Any]      ,      lowerCamelCase_	:	int=3_0522      ,      lowerCamelCase_	:	Any=768      ,      lowerCamelCase_	:	List[Any]=12      ,      lowerCamelCase_	:	Tuple=12      ,      lowerCamelCase_	:	Tuple=3072      ,      lowerCamelCase_	:	Any="gelu"      ,      lowerCamelCase_	:	Union[str, Any]=0.1      ,      lowerCamelCase_	:	str=0.1      ,      lowerCamelCase_	:	int=512      ,      lowerCamelCase_	:	List[Any]=2      ,      lowerCamelCase_	:	List[Any]=0.0_2      ,      lowerCamelCase_	:	Dict=1E-12      ,      lowerCamelCase_	:	Tuple=True      ,      lowerCamelCase_	:	str=0      ,      lowerCamelCase_	:	Any=104      ,      **lowerCamelCase_	:	Dict      ,      ):





    """simple docstring"""


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

    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			type_vocab_size
    UpperCamelCase      =			layer_norm_eps
    UpperCamelCase      =			use_cache
    UpperCamelCase      =			question_token_id
