'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.activations import gelu_new, gelu_python, get_activation




@require_torch
class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         UpperCAmelCase_           =   get_activation("gelu" )
         self.assertTrue(torch.allclose(gelu_python(__a )   ,			torch_builtin(__a ) ) )
         self.assertFalse(torch.allclose(gelu_python(__a )   ,			gelu_new(__a ) ) )



    def 					_lowercase						(self    :    List[str] ):
         UpperCAmelCase_           =   torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         UpperCAmelCase_           =   get_activation("gelu" )
         UpperCAmelCase_           =   get_activation("gelu_10" )

         UpperCAmelCase_           =   torch_builtin(__a )
         UpperCAmelCase_           =   geluaa(__a )

         UpperCAmelCase_           =   torch.where(y_gelu_aa < 10.0   ,			1   ,			0 )

         self.assertTrue(torch.max(__a ).item() == 10.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,			y_gelu_aa * clipped_mask ) )



    def 					_lowercase						(self    :    Optional[int] ):
         get_activation("gelu" )
         get_activation("gelu_10" )
         get_activation("gelu_fast" )
         get_activation("gelu_new" )
         get_activation("gelu_python" )
         get_activation("gelu_pytorch_tanh" )
         get_activation("linear" )
         get_activation("mish" )
         get_activation("quick_gelu" )
         get_activation("relu" )
         get_activation("sigmoid" )
         get_activation("silu" )
         get_activation("swish" )
         get_activation("tanh" )
         with self.assertRaises(__a ):
              get_activation("bogus" )
         with self.assertRaises(__a ):
              get_activation(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   get_activation("gelu" )
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   get_activation("gelu" )
         self.assertEqual(acta.a   ,			1 )
         with self.assertRaises(__a ):
              UpperCAmelCase_           =   acta.a





'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Tuple  ={}




class        __A      ( UpperCamelCase__	):
    a__       :						int          =  """llama"""
    a__       :						Any          =  ["""past_key_values"""]



    def __init__(self    :    List[str]   ,			__a    :    List[str]=32000   ,			__a    :    Tuple=4096   ,			__a    :    List[Any]=11008   ,			__a    :    Dict=32   ,			__a    :    Tuple=32   ,			__a    :    Any=None   ,			__a    :    Any="silu"   ,			__a    :    List[Any]=2048   ,			__a    :    List[Any]=0.02   ,			__a    :    str=1E-6   ,			__a    :    Optional[Any]=True   ,			__a    :    Union[str, Any]=0   ,			__a    :    Any=1   ,			__a    :    Dict=2   ,			__a    :    Dict=1   ,			__a    :    str=False   ,			__a    :    str=None   ,			**__a    :    Optional[Any]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
              UpperCAmelCase_           =   num_attention_heads

         UpperCAmelCase_           =   num_key_value_heads
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   initializer_range
         UpperCAmelCase_           =   rms_norm_eps
         UpperCAmelCase_           =   pretraining_tp
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   rope_scaling
         self._rope_scaling_validation()

         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			tie_word_embeddings=__a   ,			**__a   ,			)






    def 					_lowercase						(self    :    List[str] ):
         if self.rope_scaling is None:
              return

         if not isinstance(self.rope_scaling   ,			__a ) or len(self.rope_scaling ) != 2:
              raise ValueError(
                  "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
                  f"""got {self.rope_scaling}""" )
         UpperCAmelCase_           =   self.rope_scaling.get("type"   ,			__a )
         UpperCAmelCase_           =   self.rope_scaling.get("factor"   ,			__a )
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
              raise ValueError(
                  f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
         if rope_scaling_factor is None or not isinstance(__a   ,			__a ) or rope_scaling_factor <= 1.0:
              raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )




