



import warnings

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


UpperCAmelCase_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''xlnet'''
   SCREAMING_SNAKE_CASE__							=     ['''mems''']
   SCREAMING_SNAKE_CASE__							=     {
       '''n_token''': '''vocab_size''',  # Backward compatibility
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }





   def __init__(	self       ,     _lowerCAmelCase=32_000       ,     _lowerCAmelCase=1_024       ,     _lowerCAmelCase=24       ,     _lowerCAmelCase=16       ,     _lowerCAmelCase=4_096       ,     _lowerCAmelCase="gelu"       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase="bi"       ,     _lowerCAmelCase=0.02       ,     _lowerCAmelCase=1E-12       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=512       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase=-1       ,     _lowerCAmelCase=False       ,     _lowerCAmelCase="last"       ,     _lowerCAmelCase=True       ,     _lowerCAmelCase="tanh"       ,     _lowerCAmelCase=0.1       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=5       ,     _lowerCAmelCase=1       ,     _lowerCAmelCase=2       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  vocab_size
         _lowerCAmelCase 			=  d_model
         _lowerCAmelCase 			=  n_layer
         _lowerCAmelCase 			=  n_head
         if d_model % n_head != 0:
               raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''			)
         if "d_head" in kwargs:
               if kwargs["d_head"] != d_model // n_head:
                     raise ValueError(
                         F'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'''			)
         _lowerCAmelCase 			=  d_model // n_head
         _lowerCAmelCase 			=  ff_activation
         _lowerCAmelCase 			=  d_inner
         _lowerCAmelCase 			=  untie_r
         _lowerCAmelCase 			=  attn_type

         _lowerCAmelCase 			=  initializer_range
         _lowerCAmelCase 			=  layer_norm_eps

         _lowerCAmelCase 			=  dropout
         _lowerCAmelCase 			=  mem_len
         _lowerCAmelCase 			=  reuse_len
         _lowerCAmelCase 			=  bi_data
         _lowerCAmelCase 			=  clamp_len
         _lowerCAmelCase 			=  same_length

         _lowerCAmelCase 			=  summary_type
         _lowerCAmelCase 			=  summary_use_proj
         _lowerCAmelCase 			=  summary_activation
         _lowerCAmelCase 			=  summary_last_dropout
         _lowerCAmelCase 			=  start_n_top
         _lowerCAmelCase 			=  end_n_top

         _lowerCAmelCase 			=  bos_token_id
         _lowerCAmelCase 			=  pad_token_id
         _lowerCAmelCase 			=  eos_token_id

         if "use_cache" in kwargs:
               warnings.warn(
                   '''The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'''
                   ''' instead.'''       ,     _UpperCAmelCase       ,     )
               _lowerCAmelCase 			=  kwargs['''use_cache''']

         _lowerCAmelCase 			=  use_mems_eval
         _lowerCAmelCase 			=  use_mems_train
         super().__init__(pad_token_id=_UpperCAmelCase       ,     bos_token_id=_UpperCAmelCase       ,     eos_token_id=_UpperCAmelCase       ,     **_UpperCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''			)
         return -1





   @max_position_embeddings.setter
   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         raise NotImplementedError(
             F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''			)






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,
    )