

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     lowerCAmelCase				(    __UpperCamelCase   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	SpeechaTextaTokenizer
       snake_case    							=	False

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> List[str]:







        '''simple docstring'''

        super().setUp()

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

        lowerCamelCase      						=    {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

        lowerCamelCase      						=    tempfile.mkdtemp()
        lowerCamelCase      						=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['vocab_file']     )
        lowerCamelCase      						=    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(__snake_case     ) + '\n'     )

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

       def   lowerCamelCase__						(	self					:  Union[str, Any]     )   -> List[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    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(__snake_case     )						,	14     )

       def   lowerCamelCase__						(	self					:  Optional[Any]     )   -> Any:







        '''simple docstring'''

        self.assertEqual(self.get_tokenizer().vocab_size						,	14     )

       def   lowerCamelCase__						(	self					:  List[Any]     )   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

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

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

       def   lowerCamelCase__						(	self					:  List[str]     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    SpeechaTextaTokenizer.from_pretrained(self.tmpdirname     )

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

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

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

        self.assertIsNotNone(__snake_case     )





       def   lowerCamelCase__						(	self					:  List[Any]     )   -> Optional[int]:







        '''simple docstring'''

        if not self.test_slow_tokenizer:
         return

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

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase :	List[Any]     				=    {
    'configuration_switch_transformers': [
        'SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwitchTransformersConfig',
        'SwitchTransformersOnnxConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCAmelCase :	Tuple     				=    [
         'SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'SwitchTransformersEncoderModel',
         'SwitchTransformersForConditionalGeneration',
         'SwitchTransformersModel',
         'SwitchTransformersPreTrainedModel',
         'SwitchTransformersTop1Router',
         'SwitchTransformersSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

     _lowerCAmelCase :	List[Any]     				=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




