


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


__UpperCAmelCase : Tuple 			=				logging.get_logger(__name__)

__UpperCAmelCase : Optional[Any] 			=				{
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}



class    UpperCAmelCase_	(							a__):
       '''simple docstring'''
       __UpperCamelCase      :     Union[str, Any]							=					"open-llama"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=100_000 ,					__SCREAMING_SNAKE_CASE=4_096 ,					__SCREAMING_SNAKE_CASE=11_008 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE="silu" ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1e-6 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=0 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		vocab_size
              UpperCamelCase    :	Tuple   	=		max_position_embeddings
              UpperCamelCase    :	Optional[Any]   	=		hidden_size
              UpperCamelCase    :	Union[str, Any]   	=		intermediate_size
              UpperCamelCase    :	List[str]   	=		num_hidden_layers
              UpperCamelCase    :	Tuple   	=		num_attention_heads
              UpperCamelCase    :	Tuple   	=		hidden_act
              UpperCamelCase    :	str   	=		initializer_range
              UpperCamelCase    :	Dict   	=		rms_norm_eps
              UpperCamelCase    :	List[Any]   	=		use_cache
              UpperCamelCase    :	Optional[int]   	=		kwargs.pop(
                  '''use_memorry_efficient_attention''' ,					lowerCamelCase_						)
              UpperCamelCase    :	Any   	=		hidden_dropout_prob
              UpperCamelCase    :	Optional[Any]   	=		attention_dropout_prob
              UpperCamelCase    :	Tuple   	=		use_stable_embedding
              UpperCamelCase    :	int   	=		shared_input_output_embedding
              UpperCamelCase    :	Dict   	=		rope_scaling
              self._rope_scaling_validation()

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






       def  _lowercase   (     self						):




              """simple docstring"""




              if self.rope_scaling is None:
                     return

              if not isinstance(self.rope_scaling ,					lowerCamelCase_						) 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    :	Tuple   	=		self.rope_scaling.get('''type''' ,					lowerCamelCase_						)
              UpperCamelCase    :	Tuple   	=		self.rope_scaling.get('''factor''' ,					lowerCamelCase_						)
              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(lowerCamelCase_ ,					lowerCamelCase_						) 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 (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
  from .camera import create_pan_cameras
  from .pipeline_shap_e import ShapEPipeline
  from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
  from .renderer import (
      BoundingBoxVolume,
      ImportanceRaySampler,
      MLPNeRFModelOutput,
      MLPNeRSTFModel,
      ShapEParamsProjModel,
      ShapERenderer,
      StratifiedRaySampler,
      VoidNeRFModel,
  )

