

def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			len(A    )
	UpperCAmelCase_       				=			len(A    )
	UpperCAmelCase_       				=			(
	    first_str_length if first_str_length > second_str_length else second_str_length
	)
	UpperCAmelCase_       				=			[]
	for char_count in range(A    ):
		if char_count < first_str_length:
			output_list.append(first_str[char_count]    )
		if char_count < second_str_length:
			output_list.append(second_str[char_count]    )
	return "".join(A    )


if __name__ == "__main__":
				print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_a:    Optional[Any] 							=			logging.get_logger(__name__)



def       __lowerCAmelCase      (						A    ):
	# initialize config
	if "resnet-50" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-50"    )
	elif "resnet-101" in model_name:
		UpperCAmelCase_       				=			ResNetConfig.from_pretrained("microsoft/resnet-101"    )
	else:
		raise ValueError("Model name should include either resnet50 or resnet101"    )

	UpperCAmelCase_       				=			DetrConfig(use_timm_backbone=A       ,     backbone_config=A    )

	# set label attributes
	UpperCAmelCase_       				=			"panoptic" in model_name
	if is_panoptic:
		UpperCAmelCase_       				=			250
	else:
		UpperCAmelCase_       				=			91
		UpperCAmelCase_       				=			"huggingface/label-files"
		UpperCAmelCase_       				=			"coco-detection-id2label.json"
		UpperCAmelCase_       				=			json.load(open(hf_hub_download(A       ,     A       ,     repo_type="dataset"    )       ,     "r"    )    )
		UpperCAmelCase_       				=			{int(A    ): v for k, v in idalabel.items()}
		UpperCAmelCase_       				=			idalabel
		UpperCAmelCase_       				=			{v: k for k, v in idalabel.items()}

	return config, is_panoptic



def       __lowerCAmelCase      (						A    ):
	# here we list all keys to be renamed (original name on the left, our name on the right)
	UpperCAmelCase_       				=			[]

	# stem
	# fmt: off
	rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight")    )
	rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight")    )
	rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias")    )
	rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean")    )
	rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.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"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
				    )    )
    # fmt: on

	for i in range(config.encoder_layers    ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
		        F"encoder.layers.{i}.self_attn.out_proj.weight",
		    )    )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")    )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight")    )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias")    )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight")    )
		rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias")    )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")    )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias")    )
		rename_keys.append(
		    (F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight")    )
		rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias")    )
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
		        F"decoder.layers.{i}.self_attn.out_proj.weight",
		    )    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")    )
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
		        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
		    )    )
		rename_keys.append(
		    (
		        F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
		        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
		    )    )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight")    )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias")    )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight")    )
		rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias")    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias")    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")    )
		rename_keys.append(
		    (F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight")    )
		rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias")    )

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ("input_proj.weight", "input_projection.weight"),
	        ("input_proj.bias", "input_projection.bias"),
	        ("query_embed.weight", "query_position_embeddings.weight"),
	        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
	        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
	        ("class_embed.weight", "class_labels_classifier.weight"),
	        ("class_embed.bias", "class_labels_classifier.bias"),
	        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
	        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
	        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
	        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
	        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
	        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
	    ]    )

	return rename_keys



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			state_dict.pop(A    )
	UpperCAmelCase_       				=			val



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			""
	if is_panoptic:
		UpperCAmelCase_       				=			"detr."

	# first: transformer encoder
	for i in range(6    ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"    )
		# next, add query, keys and values (in that order) to the state dict
		UpperCAmelCase_       				=			in_proj_weight[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6    ):
		# read in weights + bias of input projection layer of self-attention
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}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[:256, :]
		UpperCAmelCase_       				=			in_proj_bias[:256]
		UpperCAmelCase_       				=			in_proj_weight[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias[256:512]
		UpperCAmelCase_       				=			in_proj_weight[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		UpperCAmelCase_       				=			state_dict.pop(
		    F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"    )
		UpperCAmelCase_       				=			state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"    )
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[:256, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[:256]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[256:512, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[256:512]
		UpperCAmelCase_       				=			in_proj_weight_cross_attn[-256:, :]
		UpperCAmelCase_       				=			in_proj_bias_cross_attn[-256:]



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			"http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_       				=			Image.open(requests.get(A       ,     stream=A    ).raw    )

	return im



@torch.no_grad()
def       __lowerCAmelCase      (						A       ,     A=None       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			get_detr_config(A    )

	# load original model from torch hub
	UpperCAmelCase_       				=			{
	    "detr-resnet-50": "detr_resnet50",
	    "detr-resnet-101": "detr_resnet101",
	}
	logger.info(F"Converting model {model_name}..."    )
	UpperCAmelCase_       				=			torch.hub.load("facebookresearch/detr"       ,     model_name_to_original_name[model_name]       ,     pretrained=A    ).eval()
	UpperCAmelCase_       				=			detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(A    ):
		if is_panoptic:
			UpperCAmelCase_       				=			"detr." + src
		rename_key(A       ,     A       ,     A    )
	# query, key and value matrices need special treatment
	read_in_q_k_v(A       ,     is_panoptic=A    )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	UpperCAmelCase_       				=			"detr.model." if is_panoptic else "model."
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith("detr"    )
			    and not key.startswith("class_labels_classifier"    )
			    and not key.startswith("bbox_predictor"    )
			):
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
			elif key.startswith("bbox_attention"    ) or key.startswith("mask_head"    ):
				continue
			else:
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val
		else:
			if not key.startswith("class_labels_classifier"    ) and not key.startswith("bbox_predictor"    ):
				UpperCAmelCase_       				=			state_dict.pop(A    )
				UpperCAmelCase_       				=			val

    # finally, create HuggingFace model and load state dict
	UpperCAmelCase_       				=			DetrForSegmentation(A    ) if is_panoptic else DetrForObjectDetection(A    )
	model.load_state_dict(A    )
	model.eval()

	# verify our conversion on an image
	UpperCAmelCase_       				=			"coco_panoptic" if is_panoptic else "coco_detection"
	UpperCAmelCase_       				=			DetrImageProcessor(format=A    )

	UpperCAmelCase_       				=			processor(images=prepare_img()       ,     return_tensors="pt"    )
	UpperCAmelCase_       				=			encoding["pixel_values"]

	UpperCAmelCase_       				=			detr(A    )
	UpperCAmelCase_       				=			model(A    )

	assert torch.allclose(outputs.logits       ,     original_outputs["pred_logits"]       ,     atol=1E-3    )
	assert torch.allclose(outputs.pred_boxes       ,     original_outputs["pred_boxes"]       ,     atol=1E-3    )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks       ,     original_outputs["pred_masks"]       ,     atol=1E-4    )
	print("Looks ok!"    )

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."    )
		Path(A    ).mkdir(exist_ok=A    )
		model.save_pretrained(A    )
		processor.save_pretrained(A    )

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info("Uploading PyTorch model and image processor to the hub..."    )
		model.push_to_hub(F"nielsr/{model_name}"    )
		processor.push_to_hub(F"nielsr/{model_name}"    )


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

				parser.add_argument(
				    """--model_name""",
				    default="""detr-resnet-50""",
				    type=str,
				    choices=["""detr-resnet-50""", """detr-resnet-101"""],
				    help="""Name of the DETR 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 to push the model to the hub or not.""")
				_a:    str 							=			parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)