


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   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    BioGptTokenizer
       snake_case      =    False



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         super().setUp()

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

         A_      :				Any		     =					os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['''vocab_file''']		)
         A_      :				Optional[Any]		     =					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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					'''lower newer'''
         A_      :				Optional[Any]		     =					'''lower newer'''
         return input_text, output_text



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					BioGptTokenizer(self.vocab_file				,       self.merges_file		)

         A_      :				Union[str, Any]		     =					'''lower'''
         A_      :				str		     =					['''low''', '''er</w>''']
         A_      :				Dict		     =					tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					tokens + ['''<unk>''']
         A_      :				Dict		     =					[14, 15, 20]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)				,       _SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					BioGptTokenizer.from_pretrained('''microsoft/biogpt'''		)

         A_      :				List[str]		     =					tokenizer.encode('''sequence builders'''				,       add_special_tokens=_SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					tokenizer.encode('''multi-sequence build'''				,       add_special_tokens=_SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE				,       _SCREAMING_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,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




