

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ='''laion/clap-htsat-unfused'''
             __A    =tempfile.mkdtemp()

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return RobertaTokenizer.from_pretrained(self.checkpoint ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             return ClapFeatureExtractor.from_pretrained(self.checkpoint ,   **lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             shutil.rmtree(self.tmpdirname     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_tokenizer()
             __A    =self.get_feature_extractor()

             __A    =ClapProcessor(tokenizer=lowercase__ ,   feature_extractor=lowercase__     )

             processor.save_pretrained(self.tmpdirname     )
             __A    =ClapProcessor.from_pretrained(self.tmpdirname     )

             self.assertEqual(processor.tokenizer.get_vocab() ,   tokenizer.get_vocab()     )
             self.assertIsInstance(processor.tokenizer ,   lowercase__     )

             self.assertEqual(processor.feature_extractor.to_json_string() ,   feature_extractor.to_json_string()     )
             self.assertIsInstance(processor.feature_extractor ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =ClapProcessor(tokenizer=self.get_tokenizer() ,   feature_extractor=self.get_feature_extractor()     )
             processor.save_pretrained(self.tmpdirname     )

             __A    =self.get_tokenizer(bos_token='''(BOS)''' ,   eos_token='''(EOS)'''     )
             __A    =self.get_feature_extractor(do_normalize=lowercase__ ,   padding_value=1.0     )

             __A    =ClapProcessor.from_pretrained(
                 self.tmpdirname ,   bos_token='''(BOS)''' ,   eos_token='''(EOS)''' ,   do_normalize=lowercase__ ,   padding_value=1.0     )

             self.assertEqual(processor.tokenizer.get_vocab() ,   tokenizer_add_kwargs.get_vocab()     )
             self.assertIsInstance(processor.tokenizer ,   lowercase__     )

             self.assertEqual(processor.feature_extractor.to_json_string() ,   feature_extractor_add_kwargs.to_json_string()     )
             self.assertIsInstance(processor.feature_extractor ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_feature_extractor()
             __A    =self.get_tokenizer()

             __A    =ClapProcessor(tokenizer=lowercase__ ,   feature_extractor=lowercase__     )

             __A    =floats_list((3, 1_0_0_0)     )

             __A    =feature_extractor(lowercase__ ,   return_tensors='''np'''     )
             __A    =processor(audios=lowercase__ ,   return_tensors='''np'''     )

             for key in input_feat_extract.keys():
                   self.assertAlmostEqual(input_feat_extract[key].sum() ,   input_processor[key].sum() ,   delta=1E-2     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_feature_extractor()
             __A    =self.get_tokenizer()

             __A    =ClapProcessor(tokenizer=lowercase__ ,   feature_extractor=lowercase__     )

             __A    ='''This is a test string'''

             __A    =processor(text=lowercase__     )

             __A    =tokenizer(lowercase__     )

             for key in encoded_tok.keys():
                   self.assertListEqual(encoded_tok[key] ,   encoded_processor[key]     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_feature_extractor()
             __A    =self.get_tokenizer()

             __A    =ClapProcessor(tokenizer=lowercase__ ,   feature_extractor=lowercase__     )

             __A    =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             __A    =processor.batch_decode(lowercase__     )
             __A    =tokenizer.batch_decode(lowercase__     )

             self.assertListEqual(lowercase__ ,   lowercase__     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.get_feature_extractor()
             __A    =self.get_tokenizer()

             __A    =ClapProcessor(tokenizer=lowercase__ ,   feature_extractor=lowercase__     )

             self.assertListEqual(
                 processor.model_input_names[2:] ,   feature_extractor.model_input_names ,   msg='''`processor` and `feature_extractor` model input names do not match''' ,   )





from typing import TYPE_CHECKING

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


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

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


