


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    CTRLTokenizer
       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         A_      :				Tuple		     =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
         A_      :				Dict		     =					dict(zip(_SCREAMING_SNAKE_CASE				,       range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
         A_      :				Optional[int]		     =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
         A_      :				int		     =					{'''unk_token''': '''<unk>'''}

         A_      :				Tuple		     =					os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['''vocab_file''']		)
         A_      :				Tuple		     =					os.path.join(self.tmpdirname				,       VOCAB_FILES_NAMES['''merges_file''']		)
         with open(self.vocab_file				,       '''w'''				,       encoding='''utf-8'''		) as fp:
           fp.write(json.dumps(_SCREAMING_SNAKE_CASE		) + '''\n'''		)
         with open(self.merges_file				,       '''w'''				,       encoding='''utf-8'''		) as fp:
           fp.write('''\n'''.join(_SCREAMING_SNAKE_CASE		)		)



       def        _snake_case       (      self				,       **_SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         kwargs.update(self.special_tokens_map		)
         return CTRLTokenizer.from_pretrained(self.tmpdirname				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					'''adapt react readapt apt'''
         A_      :				str		     =					'''adapt react readapt apt'''
         return input_text, output_text







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




         '''simple docstring'''





         A_      :				List[Any]		     =					CTRLTokenizer(self.vocab_file				,       self.merges_file				,       **self.special_tokens_map		)
         A_      :				str		     =					'''adapt react readapt apt'''
         A_      :				Any		     =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
         A_      :				Union[str, Any]		     =					tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Any		     =					tokens + [tokenizer.unk_token]

         A_      :				Optional[Any]		     =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)				,       _SCREAMING_SNAKE_CASE		)








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__)




