





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 							lowercase						(							self					:						Union[str, Any]	)				->		Any:
     __lowerCAmelCase				      =      'laion/clap-htsat-unfused'
     __lowerCAmelCase				      =      tempfile.mkdtemp()






  def 							lowercase						(							self					:						Optional[int]   ,    **lowerCAmelCase_					:						Union[str, Any]	)				->		Optional[int]:
     return RobertaTokenizer.from_pretrained(self.checkpoint   ,    **lowerCAmelCase_	)






  def 							lowercase						(							self					:						str   ,    **lowerCAmelCase_					:						Optional[int]	)				->		List[str]:
     return ClapFeatureExtractor.from_pretrained(self.checkpoint   ,    **lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     shutil.rmtree(self.tmpdirname	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Dict:
     __lowerCAmelCase				      =      self.get_tokenizer()
     __lowerCAmelCase				      =      self.get_feature_extractor()

     __lowerCAmelCase				      =      ClapProcessor(tokenizer=lowerCAmelCase_   ,    feature_extractor=lowerCAmelCase_	)

     processor.save_pretrained(self.tmpdirname	)
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						str	)				->		List[Any]:
     __lowerCAmelCase				      =      ClapProcessor(tokenizer=self.get_tokenizer()   ,    feature_extractor=self.get_feature_extractor()	)
     processor.save_pretrained(self.tmpdirname	)

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

     __lowerCAmelCase				      =      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 							lowercase						(							self					:						Any	)				->		Dict:
     __lowerCAmelCase				      =      self.get_feature_extractor()
     __lowerCAmelCase				      =      self.get_tokenizer()

     __lowerCAmelCase				      =      ClapProcessor(tokenizer=lowerCAmelCase_   ,    feature_extractor=lowerCAmelCase_	)

     __lowerCAmelCase				      =      floats_list((3, 1_0_0_0)	)

     __lowerCAmelCase				      =      feature_extractor(lowerCAmelCase_   ,    return_tensors='np'	)
     __lowerCAmelCase				      =      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 							lowercase						(							self					:						Dict	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.get_feature_extractor()
     __lowerCAmelCase				      =      self.get_tokenizer()

     __lowerCAmelCase				      =      ClapProcessor(tokenizer=lowerCAmelCase_   ,    feature_extractor=lowerCAmelCase_	)

     __lowerCAmelCase				      =      'This is a test string'

     __lowerCAmelCase				      =      processor(text=lowerCAmelCase_	)

     __lowerCAmelCase				      =      tokenizer(lowerCAmelCase_	)

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






  def 							lowercase						(							self					:						Union[str, Any]	)				->		str:
     __lowerCAmelCase				      =      self.get_feature_extractor()
     __lowerCAmelCase				      =      self.get_tokenizer()

     __lowerCAmelCase				      =      ClapProcessor(tokenizer=lowerCAmelCase_   ,    feature_extractor=lowerCAmelCase_	)

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

     __lowerCAmelCase				      =      processor.batch_decode(lowerCAmelCase_	)
     __lowerCAmelCase				      =      tokenizer.batch_decode(lowerCAmelCase_	)

     self.assertListEqual(lowerCAmelCase_   ,    lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Dict:
     __lowerCAmelCase				      =      self.get_feature_extractor()
     __lowerCAmelCase				      =      self.get_tokenizer()

     __lowerCAmelCase				      =      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 __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



