



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       UpperCAmelCase__(							unittest.TestCase	):

   '''simple docstring'''




   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Dict			,				**lowerCAmelCase  :			Optional[int])	->				int:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str]			,				**lowerCAmelCase  :			Union[str, Any])	->				Union[str, Any]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				str:




         """simple docstring"""

         shutil.rmtree(self.tmpdirname)


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




         """simple docstring"""

         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_feature_extractor()

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

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

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

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Union[str, Any]:




         """simple docstring"""

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

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

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

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

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


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()

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

         lowercase__     =  floats_list((3, 10_00))

         lowercase__     =  feature_extractor(lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  processor(audios=lowerCAmelCase			,				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  :			Optional[int])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()

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

         lowercase__     =  'This is a test string'

         lowercase__     =  processor(text=lowerCAmelCase)

         lowercase__     =  tokenizer(lowerCAmelCase)

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


   def 			UpperCAmelCase		(						self  :			Dict)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()

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

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

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

         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)



   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

         lowercase__     =  self.get_feature_extractor()
         lowercase__     =  self.get_tokenizer()

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

         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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Any   	=			{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Optional[int]   	=			["ReformerTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Union[str, Any]   	=			["ReformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			[
           "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "ReformerAttention",
           "ReformerForMaskedLM",
           "ReformerForQuestionAnswering",
           "ReformerForSequenceClassification",
           "ReformerLayer",
           "ReformerModel",
           "ReformerModelWithLMHead",
           "ReformerPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer import ReformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_reformer_fast import ReformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_reformer import (
                  REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ReformerAttention,
                  ReformerForMaskedLM,
                  ReformerForQuestionAnswering,
                  ReformerForSequenceClassification,
                  ReformerLayer,
                  ReformerModel,
                  ReformerModelWithLMHead,
                  ReformerPreTrainedModel,
              )

else:
       import sys

       a__      :				Tuple   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
