

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


_SCREAMING_SNAKE_CASE					:  Tuple             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "lxmert"
  a									= {}






  def __init__(							self						:		Dict     ,       __lowerCamelCase						:		Any=3_0522     ,       __lowerCamelCase						:		int=768     ,       __lowerCamelCase						:		Optional[int]=12     ,       __lowerCamelCase						:		Dict=9500     ,       __lowerCamelCase						:		List[str]=1600     ,       __lowerCamelCase						:		Tuple=400     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		str="gelu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		Optional[int]=0.1     ,       __lowerCamelCase						:		List[str]=512     ,       __lowerCamelCase						:		Any=2     ,       __lowerCamelCase						:		Union[str, Any]=0.02     ,       __lowerCamelCase						:		str=1e-12     ,       __lowerCamelCase						:		Optional[Any]=9     ,       __lowerCamelCase						:		List[Any]=5     ,       __lowerCamelCase						:		List[str]=5     ,       __lowerCamelCase						:		List[Any]=2048     ,       __lowerCamelCase						:		int=4     ,       __lowerCamelCase						:		Tuple=6.67     ,       __lowerCamelCase						:		Optional[int]=True     ,       __lowerCamelCase						:		Optional[int]=True     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		List[str]=True     ,       __lowerCamelCase						:		str=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		str=True     ,       **__lowerCamelCase						:		List[Any]     ,       )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   max_position_embeddings
       SCREAMING_SNAKE_CASE__   =   type_vocab_size
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   num_qa_labels
       SCREAMING_SNAKE_CASE__   =   num_object_labels
       SCREAMING_SNAKE_CASE__   =   num_attr_labels
       SCREAMING_SNAKE_CASE__   =   l_layers
       SCREAMING_SNAKE_CASE__   =   x_layers
       SCREAMING_SNAKE_CASE__   =   r_layers
       SCREAMING_SNAKE_CASE__   =   visual_feat_dim
       SCREAMING_SNAKE_CASE__   =   visual_pos_dim
       SCREAMING_SNAKE_CASE__   =   visual_loss_normalizer
       SCREAMING_SNAKE_CASE__   =   task_matched
       SCREAMING_SNAKE_CASE__   =   task_mask_lm
       SCREAMING_SNAKE_CASE__   =   task_obj_predict
       SCREAMING_SNAKE_CASE__   =   task_qa
       SCREAMING_SNAKE_CASE__   =   visual_obj_loss
       SCREAMING_SNAKE_CASE__   =   visual_attr_loss
       SCREAMING_SNAKE_CASE__   =   visual_feat_loss
       SCREAMING_SNAKE_CASE__   =   {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
       super().__init__(**__lowerCamelCase )





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


