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 						snake_case_ (	unittest.TestCase ):
   __A			:	Tuple        =       EsmTokenizer






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		str:
       super().setUp()
       lowercase__				:     Any					      =       tempfile.mkdtemp()
       # fmt: off
       lowercase__				:     List[str]					      =       ["<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
       lowercase__				:     Dict					      =       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    __UpperCamelCase  (   self       :  Tuple			,		**lowercase_       :  Tuple						)      ->		List[PreTrainedTokenizerBase]:
       return [self.get_tokenizer(**lowercase_						)]






   def    __UpperCamelCase  (   self       :  List[str]			,		**lowercase_       :  Optional[int]						)      ->		PreTrainedTokenizer:
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**lowercase_						)






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

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






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       lowercase__				:     List[Any]					      =       self.tokenizer_class(self.vocab_file						)

       lowercase__				:     Optional[Any]					      =       "LAGVS"
       self.assertListEqual(tokenizer.encode(lowercase_						)			,		[0, 4, 5, 6, 7, 8, 2]						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       self.tokenizer_class(self.vocab_file						)

       lowercase__				:     Dict					      =       ["LAGVS", "WCB"]
       lowercase__				:     Optional[Any]					      =       tokenizer(lowercase_			,		padding=lowercase_						)["input_ids"]

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






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       lowercase__				:     Tuple					      =       self.tokenizer_class(self.vocab_file						)

       lowercase__				:     str					      =       ["LAGVS", "WCB"]
       lowercase__				:     Optional[Any]					      =       tokenizer(lowercase_			,		padding=lowercase_						)["input_ids"]

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







   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		str:
       lowercase__				:     Any					      =       self.get_tokenizers(fast=lowercase_						)
       for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
               lowercase__				:     str					      =       "<unk>"
               lowercase__				:     str					      =       "<mask>"

               lowercase__				:     str					      =       tokenizer.tokenize(lowercase_						)
               lowercase__				:     Dict					      =       tokenizer.tokenize(lowercase_						)

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




from typing import TYPE_CHECKING

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


UpperCamelCase													=		{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''FlavaForPreTraining''',
          '''FlavaImageCodebook''',
          '''FlavaImageModel''',
          '''FlavaModel''',
          '''FlavaMultimodalModel''',
          '''FlavaPreTrainedModel''',
          '''FlavaTextModel''',
      ]

if TYPE_CHECKING:
      from .configuration_flava import (
          FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FlavaConfig,
          FlavaImageCodebookConfig,
          FlavaImageConfig,
          FlavaMultimodalConfig,
          FlavaTextConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_flava import FlavaFeatureExtractor
            from .image_processing_flava import FlavaImageProcessor
            from .processing_flava import FlavaProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flava import (
                FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlavaForPreTraining,
                FlavaImageCodebook,
                FlavaImageModel,
                FlavaModel,
                FlavaMultimodalModel,
                FlavaPreTrainedModel,
                FlavaTextModel,
            )

else:
      import sys

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



