




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				BioGptTokenizer
    UpperCAmelCase__							  =				False


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Dict:





           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           A__							=   [
               '''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>''',
           ]
           A__							=   dict(zip(UpperCAmelCase__ ,			range(len(UpperCAmelCase__))))
           A__							=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           A__							=   '''lower newer'''
           A__							=   '''lower newer'''
           return input_text, output_text


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[Any]:





           '''simple docstring'''

           A__							=   BioGptTokenizer(self.vocab_file ,			self.merges_file)

           A__							=   '''lower'''
           A__							=   ['''low''', '''er</w>''']
           A__							=   tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           A__							=   tokens + ['''<unk>''']
           A__							=   [14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__) ,			UpperCAmelCase__)





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

           A__							=   tokenizer.encode('''sequence builders''' ,			add_special_tokens=UpperCAmelCase__)
           A__							=   tokenizer.encode('''multi-sequence build''' ,			add_special_tokens=UpperCAmelCase__)

           A__							=   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__)
           A__							=   tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__ ,			UpperCAmelCase__)

           self.assertTrue(encoded_sentence == [2] + text)
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

  _lowerCamelCase     :		List[str]								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


