




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   :			Union[str, Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				SwinConfig(
	    embed_dim=192				,     depths=(2, 2, 18, 2)				,     num_heads=(6, 12, 24, 48)				,     window_size=12				,     out_features=["""stage2""", """stage3""", """stage4"""]				,     )

	a									=				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
	a									=				"""huggingface/label-files"""
	if "o365" in model_name:
		a									=				366
		a									=				"""object365-id2label.json"""
	else:
		a									=				91
		a									=				"""coco-detection-id2label.json"""

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

	return config





def 						__A						(      __lowerCamelCase       ) ->			Optional[Any]:
	a									=				[]

	# 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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Tuple:
	a									=				dct.pop(__lowerCamelCase       )
	a									=				val





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





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





def 						__A						(      ) ->			Tuple:
	a									=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a									=				Image.open(requests.get(__lowerCamelCase				,     stream=__lowerCamelCase       ).raw       )

	return im





@torch.no_grad()
def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				get_deta_config(__lowerCamelCase       )

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

	a									=				torch.load(__lowerCamelCase				,     map_location="""cpu"""       )["""model"""]

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

	# rename keys
	a									=				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:
			a									=				state_dict.pop(__lowerCamelCase       )
			a									=				val
		if "input_proj" in key:
			a									=				state_dict.pop(__lowerCamelCase       )
			a									=				val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			a									=				state_dict.pop(__lowerCamelCase       )
			a									=				val

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

	a									=				"""cuda""" if torch.cuda.is_available() else """cpu"""
	model.to(__lowerCamelCase       )

	# load image processor
	a									=				DetaImageProcessor(format="""coco_detection"""       )

	# verify our conversion on image
	a									=				prepare_img()
	a									=				processor(images=__lowerCamelCase				,     return_tensors="""pt"""       )
	a									=				encoding["""pixel_values"""]
	a									=				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":
		a									=				torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]       )
		a									=				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":
		a									=				torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]       )
		a									=				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   :			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 typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







								super().__init__()
								self.register_modules(unet=__magic_name__						, scheduler=__magic_name__							)





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

