
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			: Optional[Any]    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       """laion/clap-htsat-unfused"""
    __magic_name__				     =       tempfile.mkdtemp()
  def 							_lowercase							( self			: str			,							**UpperCamelCase__			: Optional[Any]    )			->	Dict:







    """simple docstring"""






    return RobertaTokenizer.from_pretrained(self.checkpoint			,							**UpperCamelCase__    )
  def 							_lowercase							( self			: str			,							**UpperCamelCase__			: List[str]    )			->	List[str]:







    """simple docstring"""






    return ClapFeatureExtractor.from_pretrained(self.checkpoint			,							**UpperCamelCase__    )
  def 							_lowercase							( self			: Union[str, Any]    )			->	Union[str, Any]:







    """simple docstring"""






    shutil.rmtree(self.tmpdirname    )
  def 							_lowercase							( self			: int    )			->	int:







    """simple docstring"""






    __magic_name__				     =       self.get_tokenizer()
    __magic_name__				     =       self.get_feature_extractor()

    __magic_name__				     =       ClapProcessor(tokenizer=UpperCamelCase__			,							feature_extractor=UpperCamelCase__    )

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

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

    self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor.to_json_string()    )
    self.assertIsInstance(processor.feature_extractor			,							UpperCamelCase__    )
  def 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






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

    __magic_name__				     =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""    )
    __magic_name__				     =       self.get_feature_extractor(do_normalize=UpperCamelCase__			,							padding_value=1.0    )

    __magic_name__				     =       ClapProcessor.from_pretrained(
        self.tmpdirname			,							bos_token="""(BOS)"""			,							eos_token="""(EOS)"""			,							do_normalize=UpperCamelCase__			,							padding_value=1.0    )

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

    self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor_add_kwargs.to_json_string()    )
    self.assertIsInstance(processor.feature_extractor			,							UpperCamelCase__    )
  def 							_lowercase							( self			: List[Any]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.get_feature_extractor()
    __magic_name__				     =       self.get_tokenizer()

    __magic_name__				     =       ClapProcessor(tokenizer=UpperCamelCase__			,							feature_extractor=UpperCamelCase__    )

    __magic_name__				     =       floats_list((3, 1000)    )

    __magic_name__				     =       feature_extractor(UpperCamelCase__			,							return_tensors="""np"""    )
    __magic_name__				     =       processor(audios=UpperCamelCase__			,							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			: Optional[int]    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       self.get_feature_extractor()
    __magic_name__				     =       self.get_tokenizer()

    __magic_name__				     =       ClapProcessor(tokenizer=UpperCamelCase__			,							feature_extractor=UpperCamelCase__    )

    __magic_name__				     =       """This is a test string"""

    __magic_name__				     =       processor(text=UpperCamelCase__    )

    __magic_name__				     =       tokenizer(UpperCamelCase__    )

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]    )
  def 							_lowercase							( self			: List[str]    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       self.get_feature_extractor()
    __magic_name__				     =       self.get_tokenizer()

    __magic_name__				     =       ClapProcessor(tokenizer=UpperCamelCase__			,							feature_extractor=UpperCamelCase__    )

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

    __magic_name__				     =       processor.batch_decode(UpperCamelCase__    )
    __magic_name__				     =       tokenizer.batch_decode(UpperCamelCase__    )

    self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__    )





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







    """simple docstring"""






    __magic_name__				     =       self.get_feature_extractor()
    __magic_name__				     =       self.get_tokenizer()

    __magic_name__				     =       ClapProcessor(tokenizer=UpperCamelCase__			,							feature_extractor=UpperCamelCase__    )

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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""biogpt"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Optional[Any]=4_2384			,							UpperCamelCase__			: Union[str, Any]=1024			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: Union[str, Any]=16			,							UpperCamelCase__			: Tuple=4096			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: str=1024			,							UpperCamelCase__			: Any=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=True			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Dict=0			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: Optional[int]			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       scale_embedding
    __magic_name__				     =       use_cache
    __magic_name__				     =       layerdrop
    __magic_name__				     =       activation_dropout
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )

