






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


snake_case__   :						int													=   logging.get_logger(__name__)

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


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = """lxmert"""
  A_						       = {}




  def __init__(  self		,		_UpperCAmelCase=30522		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=9500		,		_UpperCAmelCase=1600		,		_UpperCAmelCase=400		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-12		,		_UpperCAmelCase=9		,		_UpperCAmelCase=5		,		_UpperCAmelCase=5		,		_UpperCAmelCase=2048		,		_UpperCAmelCase=4		,		_UpperCAmelCase=6.6_7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		**_UpperCAmelCase		,		)      ->			str:
     UpperCamelCase_						=  vocab_size
     UpperCamelCase_						=  hidden_size
     UpperCamelCase_						=  num_attention_heads
     UpperCamelCase_						=  hidden_act
     UpperCamelCase_						=  intermediate_size
     UpperCamelCase_						=  hidden_dropout_prob
     UpperCamelCase_						=  attention_probs_dropout_prob
     UpperCamelCase_						=  max_position_embeddings
     UpperCamelCase_						=  type_vocab_size
     UpperCamelCase_						=  initializer_range
     UpperCamelCase_						=  layer_norm_eps
     UpperCamelCase_						=  num_qa_labels
     UpperCamelCase_						=  num_object_labels
     UpperCamelCase_						=  num_attr_labels
     UpperCamelCase_						=  l_layers
     UpperCamelCase_						=  x_layers
     UpperCamelCase_						=  r_layers
     UpperCamelCase_						=  visual_feat_dim
     UpperCamelCase_						=  visual_pos_dim
     UpperCamelCase_						=  visual_loss_normalizer
     UpperCamelCase_						=  task_matched
     UpperCamelCase_						=  task_mask_lm
     UpperCamelCase_						=  task_obj_predict
     UpperCamelCase_						=  task_qa
     UpperCamelCase_						=  visual_obj_loss
     UpperCamelCase_						=  visual_attr_loss
     UpperCamelCase_						=  visual_feat_loss
     UpperCamelCase_						=  {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
     super().__init__(**_UpperCAmelCase			)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__   :						Optional[int]													=   {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__   :						Dict													=   [
     """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """PegasusXForConditionalGeneration""",
     """PegasusXModel""",
     """PegasusXPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

 snake_case__   :						Optional[Any]													=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





