






'''simple docstring'''







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 					a__    (			lowerCamelCase ,						unittest.TestCase						):
     '''simple docstring'''







     _snake_case             =  SpeechaTextaTokenizer
     _snake_case             =  False






     def     _UpperCamelCase   (self    :	Any					)  ->       Optional[int]:
           super().setUp()

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

           __A		   =      {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

           __A		   =      tempfile.mkdtemp()
           __A		   =      os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["vocab_file"]					)
           __A		   =      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(lowercase__					) + "\n"					)

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






     def     _UpperCamelCase   (self    :	Tuple					)  ->       Union[str, Any]:
           __A		   =      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(lowercase__					)				,				14					)






     def     _UpperCamelCase   (self    :	Dict					)  ->       List[str]:
           self.assertEqual(self.get_tokenizer().vocab_size				,				14					)






     def     _UpperCamelCase   (self    :	Any					)  ->       List[Any]:
           __A		   =      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

           # make sure @@ is correctly concatenated
           __A		   =      [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
           __A		   =      tokenizer.decode(lowercase__					)

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






     def     _UpperCamelCase   (self    :	str					)  ->       List[Any]:
           __A		   =      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

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

                 # load tokenizer without merges file should not throw an error
                 __A		   =      SpeechaTextaTokenizer.from_pretrained(lowercase__					)

           with tempfile.TemporaryDirectory() as tmp_dirname:
                 # save tokenizer and load again
                 tokenizer.save_pretrained(lowercase__					)
                 __A		   =      SpeechaTextaTokenizer.from_pretrained(lowercase__					)

           self.assertIsNotNone(lowercase__					)






     def     _UpperCamelCase   (self    :	str					)  ->       Tuple:
           if not self.test_slow_tokenizer:
                 return

           __A		   =      inspect.signature(self.tokenizer_class.__init__					)
           __A		   =      self.get_tokenizer()

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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A      :       str      =	{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A      :       str      =	[
           'UMT5EncoderModel',
           'UMT5ForConditionalGeneration',
           'UMT5ForQuestionAnswering',
           'UMT5Model',
           'UMT5PreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_umta import UMTaConfig, UMTaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_umta import (
                  UMTaEncoderModel,
                  UMTaForConditionalGeneration,
                  UMTaForQuestionAnswering,
                  UMTaModel,
                  UMTaPreTrainedModel,
              )
else:
       import sys

       __A      :       Optional[int]      =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


