




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       UpperCAmelCase__  (     lowerCamelCase   ):
		lowercase    :List[str]      	=						SwinConfig(
		    embed_dim=192,			depths=(2, 2, 18, 2),			num_heads=(6, 12, 24, 48),			window_size=12,			out_features=["stage2", "stage3", "stage4"],			)

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

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

		return config



def       UpperCAmelCase__  (     lowerCamelCase   ):
		lowercase    :Union[str, Any]      	=						[]

		# 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       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
		lowercase    :Any      	=						dct.pop(lowerCamelCase   )
		lowercase    :int      	=						val



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
		lowercase    :List[str]      	=						[int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
		for i in range(len(backbone_config.depths   )   ):
				lowercase    :Any      	=						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)
						lowercase    :Union[str, Any]      	=						state_dict.pop(F"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"   )
						lowercase    :List[str]      	=						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
						lowercase    :List[Any]      	=						in_proj_weight[:dim, :]
						lowercase    :List[Any]      	=						in_proj_bias[: dim]
						lowercase    :Any      	=						in_proj_weight[
						    dim : dim * 2, :
						]
						lowercase    :Dict      	=						in_proj_bias[
						    dim : dim * 2
						]
						lowercase    :List[str]      	=						in_proj_weight[
						    -dim :, :
						]
						lowercase    :Tuple      	=						in_proj_bias[-dim :]
						# fmt: on



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
		lowercase    :Any      	=						config.d_model
		for i in range(config.decoder_layers   ):
				# read in weights + bias of input projection layer of self-attention
				lowercase    :Any      	=						state_dict.pop(F"transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
				lowercase    :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
				lowercase    :Union[str, Any]      	=						in_proj_weight[:hidden_size, :]
				lowercase    :Tuple      	=						in_proj_bias[:hidden_size]
				lowercase    :List[str]      	=						in_proj_weight[
				    hidden_size : hidden_size * 2, :
				]
				lowercase    :List[Any]      	=						in_proj_bias[hidden_size : hidden_size * 2]
				lowercase    :str      	=						in_proj_weight[-hidden_size:, :]
				lowercase    :Tuple      	=						in_proj_bias[-hidden_size:]



def       UpperCAmelCase__  (     ):
		lowercase    :List[str]      	=						"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowercase    :Tuple      	=						Image.open(requests.get(lowerCamelCase,			stream=lowerCamelCase   ).raw   )

		return im



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase,			lowerCamelCase   ):
		lowercase    :Optional[Any]      	=						get_deta_config(lowerCamelCase   )

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

		lowercase    :int      	=						torch.load(lowerCamelCase,			map_location="cpu"   )["model"]

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

		# rename keys
		lowercase    :Tuple      	=						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:
						lowercase    :int      	=						state_dict.pop(lowerCamelCase   )
						lowercase    :Optional[Any]      	=						val
				if "input_proj" in key:
						lowercase    :Dict      	=						state_dict.pop(lowerCamelCase   )
						lowercase    :int      	=						val
				if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
						lowercase    :Optional[int]      	=						state_dict.pop(lowerCamelCase   )
						lowercase    :List[str]      	=						val

    # finally, create HuggingFace model and load state dict
		lowercase    :Dict      	=						DetaForObjectDetection(lowerCamelCase   )
		model.load_state_dict(lowerCamelCase   )
		model.eval()

		lowercase    :Optional[int]      	=						"cuda" if torch.cuda.is_available() else "cpu"
		model.to(lowerCamelCase   )

		# load image processor
		lowercase    :List[Any]      	=						DetaImageProcessor(format="coco_detection"   )

		# verify our conversion on image
		lowercase    :Optional[int]      	=						prepare_img()
		lowercase    :Optional[Any]      	=						processor(images=lowerCamelCase,			return_tensors="pt"   )
		lowercase    :Dict      	=						encoding["pixel_values"]
		lowercase    :List[Any]      	=						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":
				lowercase    :str      	=						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]]   )
				lowercase    :Any      	=						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":
				lowercase    :int      	=						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]]   )
				lowercase    :Dict      	=						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(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	:						int			=     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase	:						List[Any]			=     {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						Union[str, Any]			=     [
         "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Swinv2ForImageClassification",
         "Swinv2ForMaskedImageModeling",
         "Swinv2Model",
         "Swinv2PreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Tuple			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



