

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__ :     int				    =    logging.get_logger(__name__)






def 					__lowercase      (     _A			)     ->						Optional[Any]:
						SCREAMING_SNAKE_CASE :      Dict       =   SwinConfig(
						    embed_dim=192						,    depths=(2, 2, 18, 2)						,    num_heads=(6, 12, 24, 48)						,    window_size=12						,    out_features=["""stage2""", """stage3""", """stage4"""]						,    )

						SCREAMING_SNAKE_CASE :      Any       =   DetaConfig(
						    backbone_config=_lowerCamelCase						,    num_queries=900						,    encoder_ffn_dim=2048						,    decoder_ffn_dim=2048						,    num_feature_levels=5						,    assign_first_stage=_lowerCamelCase						,    with_box_refine=_lowerCamelCase						,    two_stage=_lowerCamelCase						,    )

						# set labels
						SCREAMING_SNAKE_CASE :      Optional[int]       =   """huggingface/label-files"""
						if "o365" in model_name:
												SCREAMING_SNAKE_CASE :      int       =   366
												SCREAMING_SNAKE_CASE :      List[str]       =   """object365-id2label.json"""
						else:
												SCREAMING_SNAKE_CASE :      Union[str, Any]       =   91
												SCREAMING_SNAKE_CASE :      Dict       =   """coco-detection-id2label.json"""

						SCREAMING_SNAKE_CASE :      Tuple       =   num_labels
						SCREAMING_SNAKE_CASE :      int       =   json.load(open(cached_download(hf_hub_url(_lowerCamelCase						,    _lowerCamelCase						,    repo_type="""dataset"""			)			)						,    """r"""			)			)
						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   {int(_lowerCamelCase			): v for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE :      List[Any]       =   idalabel
						SCREAMING_SNAKE_CASE :      Optional[Any]       =   {v: k for k, v in idalabel.items()}

						return config






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE :      int       =   []

						# stem
						# fmt: off
						rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")			)
						rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")			)
						# stages
						for i in range(len(config.backbone_config.depths			)			):
												for j in range(config.backbone_config.depths[i]			):
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")			)

												if i < 3:
																		rename_keys.append((F"backbone.0.body.layers.{i}.downsample.reduction.weight", F"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.weight", F"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")			)
																		rename_keys.append((F"backbone.0.body.layers.{i}.downsample.norm.bias", F"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")			)

						rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")			)
						rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")			)
						rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")			)
						rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")			)
						rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")			)
						rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")			)

						# transformer encoder
						for i in range(config.encoder_layers			):
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight", F"model.encoder.layers.{i}.self_attn.sampling_offsets.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias", F"model.encoder.layers.{i}.self_attn.sampling_offsets.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.weight", F"model.encoder.layers.{i}.self_attn.attention_weights.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.attention_weights.bias", F"model.encoder.layers.{i}.self_attn.attention_weights.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.weight", F"model.encoder.layers.{i}.self_attn.value_proj.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.value_proj.bias", F"model.encoder.layers.{i}.self_attn.value_proj.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.weight", F"model.encoder.layers.{i}.self_attn.output_proj.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.self_attn.output_proj.bias", F"model.encoder.layers.{i}.self_attn.output_proj.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.norm1.weight", F"model.encoder.layers.{i}.self_attn_layer_norm.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"model.encoder.layers.{i}.self_attn_layer_norm.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"model.encoder.layers.{i}.fc1.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"model.encoder.layers.{i}.fc1.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"model.encoder.layers.{i}.fc2.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"model.encoder.layers.{i}.fc2.bias")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"model.encoder.layers.{i}.final_layer_norm.weight")			)
												rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"model.encoder.layers.{i}.final_layer_norm.bias")			)

						# transformer decoder
						for i in range(config.decoder_layers			):
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias", F"model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.weight", F"model.decoder.layers.{i}.encoder_attn.attention_weights.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.attention_weights.bias", F"model.decoder.layers.{i}.encoder_attn.attention_weights.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.weight", F"model.decoder.layers.{i}.encoder_attn.value_proj.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.value_proj.bias", F"model.decoder.layers.{i}.encoder_attn.value_proj.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.weight", F"model.decoder.layers.{i}.encoder_attn.output_proj.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.cross_attn.output_proj.bias", F"model.decoder.layers.{i}.encoder_attn.output_proj.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm1.weight", F"model.decoder.layers.{i}.encoder_attn_layer_norm.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"model.decoder.layers.{i}.encoder_attn_layer_norm.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"model.decoder.layers.{i}.self_attn.out_proj.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"model.decoder.layers.{i}.self_attn.out_proj.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm2.weight", F"model.decoder.layers.{i}.self_attn_layer_norm.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm2.bias", F"model.decoder.layers.{i}.self_attn_layer_norm.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"model.decoder.layers.{i}.fc1.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"model.decoder.layers.{i}.fc1.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"model.decoder.layers.{i}.fc2.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"model.decoder.layers.{i}.fc2.bias")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"model.decoder.layers.{i}.final_layer_norm.weight")			)
												rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"model.decoder.layers.{i}.final_layer_norm.bias")			)

						# fmt: on

						return rename_keys






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Optional[Any]:
						SCREAMING_SNAKE_CASE :      List[str]       =   dct.pop(_lowerCamelCase			)
						SCREAMING_SNAKE_CASE :      Any       =   val






def 					__lowercase      (     _A						,    _A			)     ->						Dict:
						SCREAMING_SNAKE_CASE :      Tuple       =   [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
						for i in range(len(backbone_config.depths			)			):
												SCREAMING_SNAKE_CASE :      int       =   num_features[i]
												for j in range(backbone_config.depths[i]			):
																		# fmt: off
																		# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
																		SCREAMING_SNAKE_CASE :      List[str]       =   state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"			)
																		SCREAMING_SNAKE_CASE :      Any       =   state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"			)
																		# next, add query, keys and values (in that order) to the state dict
																		SCREAMING_SNAKE_CASE :      Tuple       =   in_proj_weight[:dim, :]
																		SCREAMING_SNAKE_CASE :      Union[str, Any]       =   in_proj_bias[: dim]
																		SCREAMING_SNAKE_CASE :      Any       =   in_proj_weight[
																		    dim : dim * 2, :
																		]
																		SCREAMING_SNAKE_CASE :      Union[str, Any]       =   in_proj_bias[
																		    dim : dim * 2
																		]
																		SCREAMING_SNAKE_CASE :      Union[str, Any]       =   in_proj_weight[
																		    -dim :, :
																		]
																		SCREAMING_SNAKE_CASE :      Optional[int]       =   in_proj_bias[-dim :]
																		# fmt: on






def 					__lowercase      (     _A						,    _A			)     ->						Optional[Any]:
						# transformer decoder self-attention layers
						SCREAMING_SNAKE_CASE :      Union[str, Any]       =   config.d_model
						for i in range(config.decoder_layers			):
												# read in weights + bias of input projection layer of self-attention
												SCREAMING_SNAKE_CASE :      List[Any]       =   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"			)
												SCREAMING_SNAKE_CASE :      List[str]       =   state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_bias"			)
												# next, add query, keys and values (in that order) to the state dict
												SCREAMING_SNAKE_CASE :      List[str]       =   in_proj_weight[:hidden_size, :]
												SCREAMING_SNAKE_CASE :      Any       =   in_proj_bias[:hidden_size]
												SCREAMING_SNAKE_CASE :      Tuple       =   in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE :      Tuple       =   in_proj_bias[hidden_size : hidden_size * 2]
												SCREAMING_SNAKE_CASE :      List[str]       =   in_proj_weight[-hidden_size:, :]
												SCREAMING_SNAKE_CASE :      Optional[Any]       =   in_proj_bias[-hidden_size:]






def 					__lowercase      (     )     ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE :      List[str]       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
						SCREAMING_SNAKE_CASE :      Dict       =   Image.open(requests.get(_lowerCamelCase						,    stream=_lowerCamelCase			).raw			)

						return im






@torch.no_grad()
def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Any:
						SCREAMING_SNAKE_CASE :      Optional[Any]       =   get_deta_config(_lowerCamelCase			)

						# load original state dict
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE :      str       =   hf_hub_download(repo_id="""nielsr/deta-checkpoints"""						,    filename="""adet_swin_ft.pth"""			)
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE :      int       =   hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""						,    filename="""deta_swin_pt_o365.pth"""			)
						else:
												raise ValueError(F"Model name {model_name} not supported"			)

						SCREAMING_SNAKE_CASE :      Dict       =   torch.load(_lowerCamelCase						,    map_location="""cpu"""			)["""model"""]

						# original state dict
						for name, param in state_dict.items():
												print(_lowerCamelCase						,    param.shape			)

						# rename keys
						SCREAMING_SNAKE_CASE :      str       =   create_rename_keys(_lowerCamelCase			)
						for src, dest in rename_keys:
												rename_key(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase			)
						read_in_swin_q_k_v(_lowerCamelCase						,    config.backbone_config			)
						read_in_decoder_q_k_v(_lowerCamelCase						,    _lowerCamelCase			)

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		SCREAMING_SNAKE_CASE :      Optional[int]       =   state_dict.pop(_lowerCamelCase			)
																		SCREAMING_SNAKE_CASE :      Union[str, Any]       =   val
												if "input_proj" in key:
																		SCREAMING_SNAKE_CASE :      Tuple       =   state_dict.pop(_lowerCamelCase			)
																		SCREAMING_SNAKE_CASE :      Tuple       =   val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		SCREAMING_SNAKE_CASE :      Any       =   state_dict.pop(_lowerCamelCase			)
																		SCREAMING_SNAKE_CASE :      List[Any]       =   val

    # finally, create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE :      Optional[int]       =   DetaForObjectDetection(_lowerCamelCase			)
						model.load_state_dict(_lowerCamelCase			)
						model.eval()

						SCREAMING_SNAKE_CASE :      Tuple       =   """cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_lowerCamelCase			)

						# load image processor
						SCREAMING_SNAKE_CASE :      Optional[int]       =   DetaImageProcessor(format="""coco_detection"""			)

						# verify our conversion on image
						SCREAMING_SNAKE_CASE :      Any       =   prepare_img()
						SCREAMING_SNAKE_CASE :      Dict       =   processor(images=_lowerCamelCase						,    return_tensors="""pt"""			)
						SCREAMING_SNAKE_CASE :      Optional[Any]       =   encoding["""pixel_values"""]
						SCREAMING_SNAKE_CASE :      int       =   model(pixel_values.to(_lowerCamelCase			)			)

						# verify logits
						print("""Logits:"""						,    outputs.logits[0, :3, :3]			)
						print("""Boxes:"""						,    outputs.pred_boxes[0, :3, :3]			)
						if model_name == "deta-swin-large":
												SCREAMING_SNAKE_CASE :      Tuple       =   torch.tensor(
												    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
												SCREAMING_SNAKE_CASE :      Dict       =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
						elif model_name == "deta-swin-large-o365":
												SCREAMING_SNAKE_CASE :      int       =   torch.tensor(
												    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
												SCREAMING_SNAKE_CASE :      Union[str, Any]       =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
						assert torch.allclose(outputs.logits[0, :3, :3]						,    expected_logits.to(_lowerCamelCase			)						,    atol=1e-4			)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,    expected_boxes.to(_lowerCamelCase			)						,    atol=1e-4			)
						print("""Everything ok!"""			)

						if pytorch_dump_folder_path:
												# Save model and processor
												logger.info(F"Saving PyTorch model and processor to {pytorch_dump_folder_path}..."			)
												Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
												model.save_pretrained(_lowerCamelCase			)
												processor.save_pretrained(_lowerCamelCase			)

						# Push to hub
						if push_to_hub:
												print("""Pushing model and processor to hub..."""			)
												model.push_to_hub(F"jozhang97/{model_name}"			)
												processor.push_to_hub(F"jozhang97/{model_name}"			)


if __name__ == "__main__":
		UpperCAmelCase__ :     List[str]				    =    argparse.ArgumentParser()

		parser.add_argument(
		    """--model_name""",
		    type=str,
		    default="""deta-swin-large""",
		    choices=["""deta-swin-large""", """deta-swin-large-o365"""],
		    help="""Name of the model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    help="""Path to the folder to output PyTorch model.""",
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)
		UpperCAmelCase__ :     Optional[Any]				    =    parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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


UpperCAmelCase__ :     List[Any]				    =    logging.get_logger(__name__)

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







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       ="""lxmert"""
  UpperCAmelCase__      : str			       ={}


  def __init__(			self    : int				,				UpperCAmelCase__    : Dict=3_0_5_2_2				,				UpperCAmelCase__    : int=7_6_8				,				UpperCAmelCase__    : Union[str, Any]=1_2				,				UpperCAmelCase__    : int=9_5_0_0				,				UpperCAmelCase__    : Union[str, Any]=1_6_0_0				,				UpperCAmelCase__    : Optional[Any]=4_0_0				,				UpperCAmelCase__    : List[str]=3_0_7_2				,				UpperCAmelCase__    : List[Any]="gelu"				,				UpperCAmelCase__    : int=0.1				,				UpperCAmelCase__    : Optional[Any]=0.1				,				UpperCAmelCase__    : Union[str, Any]=5_1_2				,				UpperCAmelCase__    : int=2				,				UpperCAmelCase__    : List[str]=0.02				,				UpperCAmelCase__    : str=1e-12				,				UpperCAmelCase__    : int=9				,				UpperCAmelCase__    : Optional[int]=5				,				UpperCAmelCase__    : int=5				,				UpperCAmelCase__    : List[str]=2_0_4_8				,				UpperCAmelCase__    : Tuple=4				,				UpperCAmelCase__    : List[str]=6.67				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : Optional[int]=True				,				UpperCAmelCase__    : List[Any]=True				,				UpperCAmelCase__    : Dict=True				,				UpperCAmelCase__    : Optional[Any]=True				,				**UpperCAmelCase__    : Tuple				,				)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   vocab_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_size
        SCREAMING_SNAKE_CASE :      Any       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      str       =   hidden_act
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   intermediate_size
        SCREAMING_SNAKE_CASE :      List[str]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      str       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      Dict       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      Any       =   type_vocab_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Optional[int]       =   layer_norm_eps
        SCREAMING_SNAKE_CASE :      Dict       =   num_qa_labels
        SCREAMING_SNAKE_CASE :      str       =   num_object_labels
        SCREAMING_SNAKE_CASE :      str       =   num_attr_labels
        SCREAMING_SNAKE_CASE :      List[str]       =   l_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   x_layers
        SCREAMING_SNAKE_CASE :      List[str]       =   r_layers
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_dim
        SCREAMING_SNAKE_CASE :      Dict       =   visual_pos_dim
        SCREAMING_SNAKE_CASE :      List[Any]       =   visual_loss_normalizer
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   task_matched
        SCREAMING_SNAKE_CASE :      int       =   task_mask_lm
        SCREAMING_SNAKE_CASE :      int       =   task_obj_predict
        SCREAMING_SNAKE_CASE :      Optional[int]       =   task_qa
        SCREAMING_SNAKE_CASE :      Any       =   visual_obj_loss
        SCREAMING_SNAKE_CASE :      str       =   visual_attr_loss
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   visual_feat_loss
        SCREAMING_SNAKE_CASE :      List[Any]       =   {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
        super().__init__(**UpperCAmelCase__		)




