


'''simple docstring'''



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()
_lowercase     :     Any        =	logging.get_logger(__name__)


def  lowerCamelCase__ (			A  :     List[Any]	):




						'''simple docstring'''






						UpperCAmelCase								=      SwinConfig(
						    embed_dim=1_92 ,  depths=(2, 2, 18, 2) ,  num_heads=(6, 12, 24, 48) ,  window_size=12 ,  out_features=['''stage2''', '''stage3''', '''stage4'''] ,  )

						UpperCAmelCase								=      DetaConfig(
						    backbone_config=UpperCAmelCase__ ,  num_queries=9_00 ,  encoder_ffn_dim=20_48 ,  decoder_ffn_dim=20_48 ,  num_feature_levels=5 ,  assign_first_stage=UpperCAmelCase__ ,  with_box_refine=UpperCAmelCase__ ,  two_stage=UpperCAmelCase__ ,  )

						# set labels
						UpperCAmelCase								=      '''huggingface/label-files'''
						if "o365" in model_name:
												UpperCAmelCase								=      3_66
												UpperCAmelCase								=      '''object365-id2label.json'''
						else:
												UpperCAmelCase								=      91
												UpperCAmelCase								=      '''coco-detection-id2label.json'''

						UpperCAmelCase								=      num_labels
						UpperCAmelCase								=      json.load(open(cached_download(hf_hub_url(UpperCAmelCase__ ,  UpperCAmelCase__ ,  repo_type='''dataset'''	)	) ,  '''r'''	)	)
						UpperCAmelCase								=      {int(UpperCAmelCase__	): v for k, v in idalabel.items()}
						UpperCAmelCase								=      idalabel
						UpperCAmelCase								=      {v: k for k, v in idalabel.items()}

						return config


def  lowerCamelCase__ (			A  :     int	):




						'''simple docstring'''






						UpperCAmelCase								=      []

						# 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  lowerCamelCase__ (			A  :     Dict ,  A  :     Optional[Any] ,  A  :     int	):




						'''simple docstring'''






						UpperCAmelCase								=      dct.pop(UpperCAmelCase__	)
						UpperCAmelCase								=      val


def  lowerCamelCase__ (			A  :     List[str] ,  A  :     Optional[Any]	):




						'''simple docstring'''






						UpperCAmelCase								=      [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
						for i in range(len(backbone_config.depths	)	):
												UpperCAmelCase								=      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)
																		UpperCAmelCase								=      state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
																		UpperCAmelCase								=      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
																		UpperCAmelCase								=      in_proj_weight[:dim, :]
																		UpperCAmelCase								=      in_proj_bias[: dim]
																		UpperCAmelCase								=      in_proj_weight[
																		    dim : dim * 2, :
																		]
																		UpperCAmelCase								=      in_proj_bias[
																		    dim : dim * 2
																		]
																		UpperCAmelCase								=      in_proj_weight[
																		    -dim :, :
																		]
																		UpperCAmelCase								=      in_proj_bias[-dim :]
																		# fmt: on


def  lowerCamelCase__ (			A  :     Union[str, Any] ,  A  :     Tuple	):




						'''simple docstring'''






						UpperCAmelCase								=      config.d_model
						for i in range(config.decoder_layers	):
												# read in weights + bias of input projection layer of self-attention
												UpperCAmelCase								=      state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""	)
												UpperCAmelCase								=      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
												UpperCAmelCase								=      in_proj_weight[:hidden_size, :]
												UpperCAmelCase								=      in_proj_bias[:hidden_size]
												UpperCAmelCase								=      in_proj_weight[
												    hidden_size : hidden_size * 2, :
												]
												UpperCAmelCase								=      in_proj_bias[hidden_size : hidden_size * 2]
												UpperCAmelCase								=      in_proj_weight[-hidden_size:, :]
												UpperCAmelCase								=      in_proj_bias[-hidden_size:]


def  lowerCamelCase__ (			):




						'''simple docstring'''






						UpperCAmelCase								=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						UpperCAmelCase								=      Image.open(requests.get(UpperCAmelCase__ ,  stream=UpperCAmelCase__	).raw	)

						return im


@torch.no_grad()
def  lowerCamelCase__ (			A  :     Any ,  A  :     Union[str, Any] ,  A  :     List[Any]	):




						'''simple docstring'''






						UpperCAmelCase								=      get_deta_config(UpperCAmelCase__	)

						# load original state dict
						if model_name == "deta-swin-large":
												UpperCAmelCase								=      hf_hub_download(repo_id='''nielsr/deta-checkpoints''' ,  filename='''adet_swin_ft.pth'''	)
						elif model_name == "deta-swin-large-o365":
												UpperCAmelCase								=      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"""	)

						UpperCAmelCase								=      torch.load(UpperCAmelCase__ ,  map_location='''cpu'''	)['''model''']

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

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

						# fix some prefixes
						for key in state_dict.copy().keys():
												if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
																		UpperCAmelCase								=      state_dict.pop(UpperCAmelCase__	)
																		UpperCAmelCase								=      val
												if "input_proj" in key:
																		UpperCAmelCase								=      state_dict.pop(UpperCAmelCase__	)
																		UpperCAmelCase								=      val
												if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
																		UpperCAmelCase								=      state_dict.pop(UpperCAmelCase__	)
																		UpperCAmelCase								=      val

    # finally, create HuggingFace model and load state dict
						UpperCAmelCase								=      DetaForObjectDetection(UpperCAmelCase__	)
						model.load_state_dict(UpperCAmelCase__	)
						model.eval()

						UpperCAmelCase								=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
						model.to(UpperCAmelCase__	)

						# load image processor
						UpperCAmelCase								=      DetaImageProcessor(format='''coco_detection'''	)

						# verify our conversion on image
						UpperCAmelCase								=      prepare_img()
						UpperCAmelCase								=      processor(images=UpperCAmelCase__ ,  return_tensors='''pt'''	)
						UpperCAmelCase								=      encoding['''pixel_values''']
						UpperCAmelCase								=      model(pixel_values.to(UpperCAmelCase__	)	)

						# verify logits
						print('''Logits:''' ,  outputs.logits[0, :3, :3]	)
						print('''Boxes:''' ,  outputs.pred_boxes[0, :3, :3]	)
						if model_name == "deta-swin-large":
												UpperCAmelCase								=      torch.tensor(
												    [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]]	)
												UpperCAmelCase								=      torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]]	)
						elif model_name == "deta-swin-large-o365":
												UpperCAmelCase								=      torch.tensor(
												    [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]]	)
												UpperCAmelCase								=      torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]]	)
						assert torch.allclose(outputs.logits[0, :3, :3] ,  expected_logits.to(UpperCAmelCase__	) ,  atol=1E-4	)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,  expected_boxes.to(UpperCAmelCase__	) ,  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(UpperCAmelCase__	).mkdir(exist_ok=UpperCAmelCase__	)
												model.save_pretrained(UpperCAmelCase__	)
												processor.save_pretrained(UpperCAmelCase__	)

						# 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__":
	_lowercase     :     Tuple        =	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."""
	)
	_lowercase     :     Union[str, Any]        =	parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   hidden_act
           A_						       =   intermediate_size
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
