


"""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__       )




"""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
