
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__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							int     	=				BioGptTokenizer
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				False
   def 				lowerCAmelCase      (self			: List[str]       ):
    super().setUp()

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

    __a			:  Tuple		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''vocab_file''']       )
    __a			:  List[Any]		    =  os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['''merges_file''']       )
    with open(self.vocab_file ,    '''w'''       ) as fp:
     fp.write(json.dumps(snake_case_       )       )
    with open(self.merges_file ,    '''w'''       ) as fp:
     fp.write('''\n'''.join(snake_case_       )       )
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: Union[str, Any]       ):
    __a			:  List[Any]		    =  '''lower newer'''
    __a			:  List[Any]		    =  '''lower newer'''
    return input_text, output_text
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Optional[Any]		    =  BioGptTokenizer(self.vocab_file ,    self.merges_file       )

    __a			:  Dict		    =  '''lower'''
    __a			:  List[str]		    =  ['''low''', '''er</w>''']
    __a			:  Any		    =  tokenizer.tokenize(snake_case_       )
    self.assertListEqual(snake_case_ ,    snake_case_       )

    __a			:  str		    =  tokens + ['''<unk>''']
    __a			:  Dict		    =  [1_4, 1_5, 2_0]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_       ) ,    snake_case_       )







   @slow
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Union[str, Any]		    =  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''       )

    __a			:  Optional[Any]		    =  tokenizer.encode('''sequence builders''' ,    add_special_tokens=snake_case_       )
    __a			:  int		    =  tokenizer.encode('''multi-sequence build''' ,    add_special_tokens=snake_case_       )

    __a			:  List[str]		    =  tokenizer.build_inputs_with_special_tokens(snake_case_       )
    __a			:  Dict		    =  tokenizer.build_inputs_with_special_tokens(snake_case_ ,    snake_case_       )

    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_tf_available, is_torch_available


lowercase__          ={
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecAudioForAudioFrameClassification',
          'Data2VecAudioForCTC',
          'Data2VecAudioForSequenceClassification',
          'Data2VecAudioForXVector',
          'Data2VecAudioModel',
          'Data2VecAudioPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecTextForCausalLM',
          'Data2VecTextForMaskedLM',
          'Data2VecTextForMultipleChoice',
          'Data2VecTextForQuestionAnswering',
          'Data2VecTextForSequenceClassification',
          'Data2VecTextForTokenClassification',
          'Data2VecTextModel',
          'Data2VecTextPreTrainedModel',
      ]
      lowercase__          =[
          'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Data2VecVisionForImageClassification',
          'Data2VecVisionForMaskedImageModeling',
          'Data2VecVisionForSemanticSegmentation',
          'Data2VecVisionModel',
          'Data2VecVisionPreTrainedModel',
      ]

if is_tf_available():
      lowercase__          =[
          'TFData2VecVisionForImageClassification',
          'TFData2VecVisionForSemanticSegmentation',
          'TFData2VecVisionModel',
          'TFData2VecVisionPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
      from .configuration_dataavec_text import (
          DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecTextConfig,
          DataaVecTextOnnxConfig,
      )
      from .configuration_dataavec_vision import (
          DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DataaVecVisionConfig,
          DataaVecVisionOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dataavec_audio import (
                DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecAudioForAudioFrameClassification,
                DataaVecAudioForCTC,
                DataaVecAudioForSequenceClassification,
                DataaVecAudioForXVector,
                DataaVecAudioModel,
                DataaVecAudioPreTrainedModel,
            )
            from .modeling_dataavec_text import (
                DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecTextForCausalLM,
                DataaVecTextForMaskedLM,
                DataaVecTextForMultipleChoice,
                DataaVecTextForQuestionAnswering,
                DataaVecTextForSequenceClassification,
                DataaVecTextForTokenClassification,
                DataaVecTextModel,
                DataaVecTextPreTrainedModel,
            )
            from .modeling_dataavec_vision import (
                DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
                DataaVecVisionForImageClassification,
                DataaVecVisionForMaskedImageModeling,
                DataaVecVisionForSemanticSegmentation,
                DataaVecVisionModel,
                DataaVecVisionPreTrainedModel,
            )
      if is_tf_available():
            from .modeling_tf_dataavec_vision import (
                TFDataaVecVisionForImageClassification,
                TFDataaVecVisionForSemanticSegmentation,
                TFDataaVecVisionModel,
                TFDataaVecVisionPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
