




# 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,
)


UpperCamelCase								=     {
    '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:
     UpperCamelCase								=     [
         'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TvltModel',
         'TvltForPreTraining',
         'TvltForAudioVisualClassification',
         'TvltPreTrainedModel',
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=     ['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

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






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      EsmTokenizer


    def    a      (       self     :     str   )      ->       Tuple:
         super().setUp()
         lowerCAmelCase__		= tempfile.mkdtemp()
         # fmt: off
         lowerCAmelCase__		= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
         # fmt: on
         lowerCAmelCase__		= os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["vocab_file"]   )
         with open(self.vocab_file   ,	"w"   ,	encoding="utf-8"   ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )


    def    a      (       self     :     Dict   ,	**SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**SCREAMING_SNAKE_CASE__   )]


    def    a      (       self     :     List[str]   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Tuple:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("LAGVS"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["L", "A", "G", "V", "S"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[4, 5, 6, 7, 8]   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= "LAGVS"
         self.assertListEqual(tokenizer.encode(SCREAMING_SNAKE_CASE__   )   ,	[0, 4, 5, 6, 7, 8, 2]   )


    def    a      (       self     :     Union[str, Any]   )      ->       str:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]   )


    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]   )


    def    a      (       self     :     str   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__   )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                   lowerCAmelCase__		= "<unk>"
                   lowerCAmelCase__		= "<mask>"

                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
