code: 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 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> str:
        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(lowerCAmelCase   )
        a__     		=     geluaa(lowerCAmelCase   )
  
        a__     		=     tf.where(y_gelu_aa < 10.0      ,      1.0      ,      0.0   )
  
        self.assertEqual(tf.math.reduce_max(lowerCAmelCase   ).numpy().item()      ,      10.0   )
        self.assertTrue(np.allclose(y_gelu * clipped_mask      ,      y_gelu_aa * clipped_mask   )   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> List[str]:
        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(lowerCAmelCase   ):
          get_tf_activation("bogus"   )
        with self.assertRaises(lowerCAmelCase   ):
          get_tf_activation(lowerCAmelCase   )
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  