


"""simple docstring"""


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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}



class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				List[Any]             =    'lxmert'
    a_    :				Optional[int]             =    {}
    def __init__( self							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=3_05_22							,					SCREAMING_SNAKE_CASE__							:					Tuple=7_68							,					SCREAMING_SNAKE_CASE__							:					str=12							,					SCREAMING_SNAKE_CASE__							:					Dict=95_00							,					SCREAMING_SNAKE_CASE__							:					str=16_00							,					SCREAMING_SNAKE_CASE__							:					str=4_00							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=30_72							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]="gelu"							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.1							,					SCREAMING_SNAKE_CASE__							:					str=0.1							,					SCREAMING_SNAKE_CASE__							:					int=5_12							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=2							,					SCREAMING_SNAKE_CASE__							:					List[Any]=0.02							,					SCREAMING_SNAKE_CASE__							:					List[str]=1e-12							,					SCREAMING_SNAKE_CASE__							:					Tuple=9							,					SCREAMING_SNAKE_CASE__							:					Tuple=5							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=5							,					SCREAMING_SNAKE_CASE__							:					str=20_48							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=4							,					SCREAMING_SNAKE_CASE__							:					Dict=6.67							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=True							,					SCREAMING_SNAKE_CASE__							:					str=True							,					**SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					):
          lowerCamelCase__           =		vocab_size
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		hidden_dropout_prob
          lowerCamelCase__           =		attention_probs_dropout_prob
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		type_vocab_size
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		num_qa_labels
          lowerCamelCase__           =		num_object_labels
          lowerCamelCase__           =		num_attr_labels
          lowerCamelCase__           =		l_layers
          lowerCamelCase__           =		x_layers
          lowerCamelCase__           =		r_layers
          lowerCamelCase__           =		visual_feat_dim
          lowerCamelCase__           =		visual_pos_dim
          lowerCamelCase__           =		visual_loss_normalizer
          lowerCamelCase__           =		task_matched
          lowerCamelCase__           =		task_mask_lm
          lowerCamelCase__           =		task_obj_predict
          lowerCamelCase__           =		task_qa
          lowerCamelCase__           =		visual_obj_loss
          lowerCamelCase__           =		visual_attr_loss
          lowerCamelCase__           =		visual_feat_loss
          lowerCamelCase__           =		{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
          super().__init__(**SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


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,
   )



