code: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers import is_tf_available
  from transformers.testing_utils import require_tf
  
  
  if is_tf_available():
     import tensorflow as tf
  
     from transformers.activations_tf import get_tf_activation
  
  
  
  
  
  @require_tf
  class __snake_case		(					unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            _a          =				tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]       )
            _a          =				get_tf_activation("gelu"       )
            _a          =				get_tf_activation("gelu_10"       )
  
            _a          =				gelu(UpperCamelCase__       )
            _a          =				geluaa(UpperCamelCase__       )
  
            _a          =				tf.where(y_gelu_aa < 10.0				,  1.0				,  0.0       )
  
            self.assertEqual(tf.math.reduce_max(UpperCamelCase__       ).numpy().item()				,  10.0       )
            self.assertTrue(np.allclose(y_gelu * clipped_mask				,  y_gelu_aa * clipped_mask       )       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            get_tf_activation("gelu"       )
            get_tf_activation("gelu_10"       )
            get_tf_activation("gelu_fast"       )
            get_tf_activation("gelu_new"       )
            get_tf_activation("glu"       )
            get_tf_activation("mish"       )
            get_tf_activation("quick_gelu"       )
            get_tf_activation("relu"       )
            get_tf_activation("sigmoid"       )
            get_tf_activation("silu"       )
            get_tf_activation("swish"       )
            get_tf_activation("tanh"       )
            with self.assertRaises(UpperCamelCase__       ):
                   get_tf_activation("bogus"       )
            with self.assertRaises(UpperCamelCase__       ):
                   get_tf_activation(UpperCamelCase__       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
     lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'
  
  
  
  
     def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
            super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
            _a          =				self.feature_extractor
            _a          =				False
  
  
  
  
     def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*UpperCamelCase__				,  **UpperCamelCase__       )
  
            if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
                   _a          =				kwargs.pop("raw_speech"       )
            else:
                   _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
            if len(UpperCamelCase__       ) > 0:
                   _a          =				args[0]
                   _a          =				args[1:]
  
            if audio is None and text is None:
                   raise ValueError("You need to specify either an `audio` or `text` input to process."       )
  
            if audio is not None:
                   _a          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
            if text is not None:
                   _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )
  
            if text is None:
                   return inputs
            elif audio is None:
                   return encodings
            else:
                   _a          =				encodings["input_ids"]
                   return inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @contextmanager
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            warnings.warn(
                "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                "your audio inputs, or in a separate call."       )
            _a          =				True
            _a          =				self.tokenizer
            yield
            _a          =				self.feature_extractor
            _a          =				False
  