




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
from transformers.utils import logging


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


def 						lowerCamelCase__   (		):







	'''simple docstring'''





	snake_case_		   =					DetaConfig(
	    num_queries=900					,     encoder_ffn_dim=2048					,     decoder_ffn_dim=2048					,     num_feature_levels=5					,     assign_first_stage=_A					,     with_box_refine=_A					,     two_stage=_A					,     )

	# set labels
	snake_case_		   =					91
	snake_case_		   =					"huggingface/label-files"
	snake_case_		   =					"coco-detection-id2label.json"
	snake_case_		   =					json.load(open(cached_download(hf_hub_url(_A					,     _A					,     repo_type="dataset"							)							)					,     "r"							)							)
	snake_case_		   =					{int(_A							): v for k, v in idalabel.items()}
	snake_case_		   =					idalabel
	snake_case_		   =					{v: k for k, v in idalabel.items()}

	return config


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					[]

	# stem
	# fmt: off
	rename_keys.append(("backbone.0.body.conv1.weight", "model.backbone.model.embedder.embedder.convolution.weight")							)
	rename_keys.append(("backbone.0.body.bn1.weight", "model.backbone.model.embedder.embedder.normalization.weight")							)
	rename_keys.append(("backbone.0.body.bn1.bias", "model.backbone.model.embedder.embedder.normalization.bias")							)
	rename_keys.append(("backbone.0.body.bn1.running_mean", "model.backbone.model.embedder.embedder.normalization.running_mean")							)
	rename_keys.append(("backbone.0.body.bn1.running_var", "model.backbone.model.embedder.embedder.normalization.running_var")							)
	# stages
	for stage_idx in range(len(config.backbone_config.depths							)							):
		for layer_idx in range(config.backbone_config.depths[stage_idx]							):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
				    )							)
			# 3 convs
			for i in range(3							):
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
				    )							)
				rename_keys.append(
				    (
				        f"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
				        f"model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )							)
    # 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					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					dct.pop(_A							)
	snake_case_		   =					val


def 						lowerCamelCase__   (		_A					,     _A							):







	'''simple docstring'''





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


def 						lowerCamelCase__   (		):







	'''simple docstring'''





	snake_case_		   =					"http://images.cocodataset.org/val2017/000000039769.jpg"
	snake_case_		   =					Image.open(requests.get(_A					,     stream=_A							).raw							)

	return im


@torch.no_grad()
def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					get_deta_config()

	# load original state dict
	if model_name == "deta-resnet-50":
		snake_case_		   =					"adet_checkpoint0011.pth"
	elif model_name == "deta-resnet-50-24-epochs":
		snake_case_		   =					"adet_2x_checkpoint0023.pth"
	else:
		raise ValueError(f"Model name {model_name} not supported"							)
	snake_case_		   =					hf_hub_download(repo_id="nielsr/deta-checkpoints"					,     filename=_A							)
	snake_case_		   =					torch.load(_A					,     map_location="cpu"							)["model"]

	# rename keys
	snake_case_		   =					create_rename_keys(_A							)
	for src, dest in rename_keys:
		rename_key(_A					,     _A					,     _A							)
	read_in_decoder_q_k_v(_A					,     _A							)

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

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

	snake_case_		   =					"cuda" if torch.cuda.is_available() else "cpu"
	model.to(_A							)

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

	# verify our conversion on image
	snake_case_		   =					prepare_img()
	snake_case_		   =					processor(images=_A					,     return_tensors="pt"							)
	snake_case_		   =					encoding["pixel_values"]
	snake_case_		   =					model(pixel_values.to(_A							)							)

	# verify logits
	if model_name == "deta-resnet-50":
		snake_case_		   =					torch.tensor(
		    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]							)
		snake_case_		   =					torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]							)
	elif model_name == "deta-resnet-50-24-epochs":
		snake_case_		   =					torch.tensor(
		    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]							)
		snake_case_		   =					torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]							)

	assert torch.allclose(outputs.logits[0, :3, :3]					,     expected_logits.to(_A							)					,     atol=1E-4							)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,     expected_boxes.to(_A							)					,     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(_A							).mkdir(exist_ok=_A							)
		model.save_pretrained(_A							)
		processor.save_pretrained(_A							)

	# 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__    :					str					=						argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    type=str,
		    default="deta-resnet-50",
		    choices=["deta-resnet-50", "deta-resnet-50-24-epochs"],
		    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__    :					Dict					=						parser.parse_args()
		convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       (EulerAncestralDiscreteScheduler,)
						lowerCAmelCase_							=       10






						def 	snake_case__						(						self							:					Tuple	,     **__lowercase							:					int						):






							"""simple docstring"""






							snake_case_		   =					{
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**__lowercase						)
							return config






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for timesteps in [10, 50, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]						):
								self.check_over_configs(beta_start=__lowercase	,     beta_end=__lowercase						)






						def 	snake_case__						(						self							:					List[str]						):






							"""simple docstring"""






							for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=__lowercase						)






						def 	snake_case__						(						self							:					Union[str, Any]						):






							"""simple docstring"""






							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=__lowercase						)






						def 	snake_case__						(						self							:					Optional[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3






						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config(prediction_type="v_prediction"						)
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps						)

							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_		   =					sample.to(__lowercase						)

							for i, t in enumerate(scheduler.timesteps						):
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 108.4439						) < 1E-2
							assert abs(result_mean.item() - 0.1412						) < 1E-3






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






							snake_case_		   =					self.scheduler_classes[0]
							snake_case_		   =					self.get_scheduler_config()
							snake_case_		   =					scheduler_class(**__lowercase						)

							scheduler.set_timesteps(self.num_inference_steps	,     device=__lowercase						)
							snake_case_		   =					torch.manual_seed(0						)

							snake_case_		   =					self.dummy_model()
							snake_case_		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_		   =					sample.to(__lowercase						)

							for t in scheduler.timesteps:
								snake_case_		   =					scheduler.scale_model_input(__lowercase	,     __lowercase						)

								snake_case_		   =					model(__lowercase	,     __lowercase						)

								snake_case_		   =					scheduler.step(__lowercase	,     __lowercase	,     __lowercase	,     generator=__lowercase						)
								snake_case_		   =					output.prev_sample

							snake_case_		   =					torch.sum(torch.abs(__lowercase						)						)
							snake_case_		   =					torch.mean(torch.abs(__lowercase						)						)

							assert abs(result_sum.item() - 152.3192						) < 1E-2
							assert abs(result_mean.item() - 0.1983						) < 1E-3
