


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  List[Any]				     =  BertweetTokenizer
       lowerCamelCase__:  str				     =  False

       def     UpperCAmelCase( self   :  Tuple     ):
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            a_     :  Any				      =     ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
            a_     :  Any				      =     dict(zip(lowerCamelCase_   ,				range(len(lowerCamelCase_     )     )     )     )
            a_     :  str				      =     ["""#version: 0.2""", """a m</w>"""]
            a_     :  int				      =     {"""unk_token""": """<unk>"""}

            a_     :  Optional[Any]				      =     os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""vocab_file"""]     )
            a_     :  List[str]				      =     os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES["""merges_file"""]     )
            with open(self.vocab_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
                 for token in vocab_tokens:
                      fp.write(F'''{token} {vocab_tokens[token]}\n'''     )
            with open(self.merges_file   ,				"""w"""   ,				encoding="""utf-8"""     ) as fp:
                 fp.write("""\n""".join(lowerCamelCase_     )     )

       def     UpperCAmelCase( self   :  List[str]   ,				**lowerCamelCase_   :  List[str]     ):
            kwargs.update(self.special_tokens_map     )
            return BertweetTokenizer.from_pretrained(self.tmpdirname   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int     ):
            a_     :  Dict				      =     """I am VinAI Research"""
            a_     :  Tuple				      =     """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
            return input_text, output_text

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Optional[Any]				      =     BertweetTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map     )
            a_     :  str				      =     """I am VinAI Research"""
            a_     :  Tuple				      =     """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
            a_     :  Any				      =     tokenizer.tokenize(lowerCamelCase_     )
            self.assertListEqual(lowerCamelCase_   ,				lowerCamelCase_     )

            a_     :  Optional[Any]				      =     tokens + [tokenizer.unk_token]

            a_     :  Union[str, Any]				      =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_     )   ,				lowerCamelCase_     )







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase   	=  {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TvltModel''',
           '''TvltForPreTraining''',
           '''TvltForAudioVisualClassification''',
           '''TvltPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''TvltImageProcessor''']


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

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



