
import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin
@require_torch
class        SCREAMING_SNAKE_CASE     (unittest.TestCase    ,			UpperCAmelCase   ):


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





     """simple docstring"""







     lowercase__			    =						load_tool('text-to-speech' )
     self.tool.setup()


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





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						self.tool('hey' )
     lowercase__			    =						result.to_raw()
     self.assertTrue(
         torch.allclose(
             resulting_tensor[:3]    ,  torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] )    ,  ) )


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





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						self.tool('hey' )
     lowercase__			    =						result.to_raw()
     self.assertTrue(
         torch.allclose(
             resulting_tensor[:3]    ,  torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] )    ,  ) )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







     return self.tokenizer.decode(*a    ,  **a )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


