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


a_    					=       logging.get_logger(__name__)

a_    					=       {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}





class 		_lowercase						(					_a       ):
 lowercase				     =   """trajectory_transformer"""
 lowercase				     =   ["""past_key_values"""]
 lowercase				     =   {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }

 def __init__( self	:							Union[str, Any]   ,      snake_case	:							Any=1_0_0   ,      snake_case	:							str=5   ,      snake_case	:							str=1   ,      snake_case	:							Optional[int]=1   ,      snake_case	:							int=2_4_9   ,      snake_case	:							str=6   ,      snake_case	:							Dict=1_7   ,      snake_case	:							Optional[Any]=2_5   ,      snake_case	:							List[str]=4   ,      snake_case	:							str=4   ,      snake_case	:							Tuple=1_2_8   ,      snake_case	:							Dict=0.1   ,      snake_case	:							str=0.1   ,      snake_case	:							Any=0.1   ,      snake_case	:							int=0.0006   ,      snake_case	:							List[str]=5_1_2   ,      snake_case	:							str=0.02   ,      snake_case	:							Any=1e-12   ,      snake_case	:							int=1   ,      snake_case	:							Optional[Any]=True   ,      snake_case	:							Tuple=1   ,      snake_case	:							int=5_0_2_5_6   ,      snake_case	:							Union[str, Any]=5_0_2_5_6   ,      **snake_case	:							Dict   ,      )   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					vocab_size
  UpperCamelCase_					:						int =					action_weight
  UpperCamelCase_					:						Tuple =					reward_weight
  UpperCamelCase_					:						str =					value_weight
  UpperCamelCase_					:						Tuple =					max_position_embeddings
  UpperCamelCase_					:						Tuple =					block_size
  UpperCamelCase_					:						Optional[int] =					action_dim
  UpperCamelCase_					:						int =					observation_dim
  UpperCamelCase_					:						List[str] =					transition_dim
  UpperCamelCase_					:						List[Any] =					learning_rate
  UpperCamelCase_					:						Optional[Any] =					n_layer
  UpperCamelCase_					:						Any =					n_head
  UpperCamelCase_					:						List[str] =					n_embd
  UpperCamelCase_					:						Any =					embd_pdrop
  UpperCamelCase_					:						str =					attn_pdrop
  UpperCamelCase_					:						Union[str, Any] =					resid_pdrop
  UpperCamelCase_					:						Optional[Any] =					initializer_range
  UpperCamelCase_					:						List[Any] =					layer_norm_eps
  UpperCamelCase_					:						Optional[int] =					kaiming_initializer_range
  UpperCamelCase_					:						Tuple =					use_cache
  super().__init__(pad_token_id=__lowerCamelCase   ,      bos_token_id=__lowerCamelCase   ,      eos_token_id=__lowerCamelCase   ,      **__lowerCamelCase			)











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,
	)
