'''simple docstring'''

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     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	int				     = EsmTokenizer
 def a (				self       :     List[str]							)  ->   int:
  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       :     Tuple				,	**SCREAMING_SNAKE_CASE__       :     str							)  ->   Optional[Any]:
  return [self.get_tokenizer(**_A							)]
 def a (				self       :     List[str]				,	**SCREAMING_SNAKE_CASE__       :     List[str]							)  ->   Union[str, Any]:
  return self.tokenizer_class.from_pretrained(self.tmpdirname				,	**_A							)
 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						self.tokenizer_class(self.vocab_file							)

  __lowerCAmelCase    							=						tokenizer.tokenize("""LAGVS"""							)
  self.assertListEqual(_A				,	["""L""", """A""", """G""", """V""", """S"""]							)
  self.assertListEqual(tokenizer.convert_tokens_to_ids(_A							)				,	[4, 5, 6, 7, 8]							)
 def a (				self       :     Union[str, Any]							)  ->   Tuple:
  __lowerCAmelCase    							=						self.tokenizer_class(self.vocab_file							)

  __lowerCAmelCase    							=						"LAGVS"
  self.assertListEqual(tokenizer.encode(_A							)				,	[0, 4, 5, 6, 7, 8, 2]							)
 def a (				self       :     Dict							)  ->   List[str]:
  __lowerCAmelCase    							=						self.tokenizer_class(self.vocab_file							)

  __lowerCAmelCase    							=						["LAGVS", "WCB"]
  __lowerCAmelCase    							=						tokenizer(_A				,	padding=_A							)["input_ids"]

  self.assertListEqual(_A				,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]							)
 def a (				self       :     Any							)  ->   Any:
  __lowerCAmelCase    							=						self.tokenizer_class(self.vocab_file							)

  __lowerCAmelCase    							=						["LAGVS", "WCB"]
  __lowerCAmelCase    							=						tokenizer(_A				,	padding=_A							)["input_ids"]

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





 def a (				self       :     Tuple							)  ->   Dict:
  __lowerCAmelCase    							=						self.get_tokenizers(fast=_A							)
  for tokenizer in tokenizers:
   with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
    __lowerCAmelCase    							=						"<unk>"
    __lowerCAmelCase    							=						"<mask>"

    __lowerCAmelCase    							=						tokenizer.tokenize(_A							)
    __lowerCAmelCase    							=						tokenizer.tokenize(_A							)

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







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






