





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class        __snake_case			(				__lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	SpeechaTextaTokenizer
       lowerCAmelCase__ 						=	False




       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         super().setUp()

         __snake_case:		Tuple					    =		"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """						)
         __snake_case:		Tuple					    =		["""he re</w> 123""", """here a 1456"""]
         __snake_case:		Optional[Any]					    =		dict(zip(A				,							range(len(A						)						)						)						)

         __snake_case:		List[Any]					    =		{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

         __snake_case:		List[str]					    =		tempfile.mkdtemp()
         __snake_case:		List[Any]					    =		os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES["""vocab_file"""]						)
         __snake_case:		Dict					    =		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(A						) + """\n"""						)

         with open(self.merges_file				,							"""w"""						) as fp:
           fp.write("""\n""".join(A						)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		str					    =		list(self.get_tokenizer().get_vocab().keys()						)

         self.assertEqual(vocab_keys[0]				,							"""<s>"""						)
         self.assertEqual(vocab_keys[1]				,							"""<pad>"""						)
         self.assertEqual(vocab_keys[-1]				,							"""vocab"""						)
         self.assertEqual(len(A						)				,							14						)




       def 				UpperCAmelCase__					(    self					:       str						):
         self.assertEqual(self.get_tokenizer().vocab_size				,							14						)




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Optional[Any]					    =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname						)

         # make sure @@ is correctly concatenated
         __snake_case:		str					    =		[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
         __snake_case:		Any					    =		tokenizer.decode(A						)

         self.assertTrue(output_string == """herecouple words ofthe"""						)




       def 				UpperCAmelCase__					(    self					:       List[Any]						):
         __snake_case:		str					    =		SpeechaTextaTokenizer.from_pretrained(self.tmpdirname						)

         with tempfile.TemporaryDirectory() as tmp_dirname:
           tokenizer.save_pretrained(A						)
           os.remove(os.path.join(A				,							"""merges.txt"""						)						)

           # load tokenizer without merges file should not throw an error
           __snake_case:		Tuple					    =		SpeechaTextaTokenizer.from_pretrained(A						)

         with tempfile.TemporaryDirectory() as tmp_dirname:
           # save tokenizer and load again
           tokenizer.save_pretrained(A						)
           __snake_case:		Optional[Any]					    =		SpeechaTextaTokenizer.from_pretrained(A						)

         self.assertIsNotNone(A						)






       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         if not self.test_slow_tokenizer:
           return

         __snake_case:		Optional[int]					    =		inspect.signature(self.tokenizer_class.__init__						)
         __snake_case:		int					    =		self.get_tokenizer()

         for parameter_name, parameter in signature.parameters.items():
           if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
             self.assertIn(A				,							tokenizer.init_kwargs						)













from typing import TYPE_CHECKING

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


__UpperCAmelCase			:  List[str]										=				{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  Any										=				["LevitFeatureExtractor"]
      __UpperCAmelCase			:  Tuple										=				["LevitImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LevitForImageClassification",
          "LevitForImageClassificationWithTeacher",
          "LevitModel",
          "LevitPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_levit import LevitFeatureExtractor
            from .image_processing_levit import LevitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_levit import (
                LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                LevitForImageClassification,
                LevitForImageClassificationWithTeacher,
                LevitModel,
                LevitPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






