




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


lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''llama'''
 UpperCamelCase						       =						['''past_key_values''']

 def __init__(    self :  Dict						,							A_ :  List[Any]=32000						,							A_ :  Tuple=4096						,							A_ :  List[Any]=11008						,							A_ :  List[str]=32						,							A_ :  Optional[Any]=32						,							A_ :  int=None						,							A_ :  Any="silu"						,							A_ :  Union[str, Any]=2048						,							A_ :  List[str]=0.02						,							A_ :  Optional[int]=1E-6						,							A_ :  List[str]=True						,							A_ :  Optional[int]=0						,							A_ :  Optional[Any]=1						,							A_ :  Optional[int]=2						,							A_ :  int=1						,							A_ :  Tuple=False						,							A_ :  Tuple=None						,							**A_ :  Optional[int]						,							)       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   max_position_embeddings
       lowerCamelCase_							=   hidden_size
       lowerCamelCase_							=   intermediate_size
       lowerCamelCase_							=   num_hidden_layers
       lowerCamelCase_							=   num_attention_heads

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

       lowerCamelCase_							=   num_key_value_heads
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   rms_norm_eps
       lowerCamelCase_							=   pretraining_tp
       lowerCamelCase_							=   use_cache
       lowerCamelCase_							=   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     a__			(    self :  Optional[Any]  )       ->     Any:

       """simple docstring"""






       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}"""  )
       lowerCamelCase_							=   self.rope_scaling.get('type'						,							A_  )
       lowerCamelCase_							=   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}"""  )









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



