






"""simple docstring"""



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		:       List[Any]  				=		logging.get_logger(__name__)



def 						lowercase    (				__snake_case				: List[str]	):
		# initialize config
		if "resnet-50" in model_name:
				lowercase_ : Optional[Any]							       =   ResNetConfig.from_pretrained('''microsoft/resnet-50'''	)
		elif "resnet-101" in model_name:
				lowercase_ : Tuple							       =   ResNetConfig.from_pretrained('''microsoft/resnet-101'''	)
		else:
				raise ValueError('''Model name should include either resnet50 or resnet101'''	)

		lowercase_ : str							       =   DetrConfig(use_timm_backbone=__snake_case		,					backbone_config=__snake_case	)

		# set label attributes
		lowercase_ : Union[str, Any]							       =   '''panoptic''' in model_name
		if is_panoptic:
				lowercase_ : List[str]							       =   2_5_0
		else:
				lowercase_ : Optional[Any]							       =   9_1
				lowercase_ : int							       =   '''huggingface/label-files'''
				lowercase_ : str							       =   '''coco-detection-id2label.json'''
				lowercase_ : List[Any]							       =   json.load(open(hf_hub_download(__snake_case		,					__snake_case		,					repo_type='''dataset'''	)		,					'''r'''	)	)
				lowercase_ : Union[str, Any]							       =   {int(__snake_case	): v for k, v in idalabel.items()}
				lowercase_ : Optional[int]							       =   idalabel
				lowercase_ : Optional[int]							       =   {v: k for k, v in idalabel.items()}

		return config, is_panoptic



def 						lowercase    (				__snake_case				: int	):
		# here we list all keys to be renamed (original name on the left, our name on the right)
		lowercase_ : Tuple							       =   []

		# 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 						lowercase    (				__snake_case				: Union[str, Any]		,					__snake_case				: Optional[int]		,					__snake_case				: Any	):
		lowercase_ : Optional[int]							       =   state_dict.pop(__snake_case	)
		lowercase_ : str							       =   val



def 						lowercase    (				__snake_case				: int		,					__snake_case				: Optional[Any]=False	):
		lowercase_ : Any							       =   ''''''
		if is_panoptic:
				lowercase_ : int							       =   '''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)
				lowercase_ : Tuple							       =   state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
				lowercase_ : str							       =   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
				lowercase_ : str							       =   in_proj_weight[:2_5_6, :]
				lowercase_ : Union[str, Any]							       =   in_proj_bias[:2_5_6]
				lowercase_ : Tuple							       =   in_proj_weight[2_5_6:5_1_2, :]
				lowercase_ : List[str]							       =   in_proj_bias[2_5_6:5_1_2]
				lowercase_ : Tuple							       =   in_proj_weight[-2_5_6:, :]
				lowercase_ : Union[str, Any]							       =   in_proj_bias[-2_5_6:]
		# 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
				lowercase_ : Dict							       =   state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
				lowercase_ : Dict							       =   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
				lowercase_ : Union[str, Any]							       =   in_proj_weight[:2_5_6, :]
				lowercase_ : Optional[int]							       =   in_proj_bias[:2_5_6]
				lowercase_ : Any							       =   in_proj_weight[2_5_6:5_1_2, :]
				lowercase_ : List[Any]							       =   in_proj_bias[2_5_6:5_1_2]
				lowercase_ : Any							       =   in_proj_weight[-2_5_6:, :]
				lowercase_ : Tuple							       =   in_proj_bias[-2_5_6:]
				# read in weights + bias of input projection layer of cross-attention
				lowercase_ : Union[str, Any]							       =   state_dict.pop(
				    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
				lowercase_ : Union[str, Any]							       =   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
				lowercase_ : Union[str, Any]							       =   in_proj_weight_cross_attn[:2_5_6, :]
				lowercase_ : Tuple							       =   in_proj_bias_cross_attn[:2_5_6]
				lowercase_ : Any							       =   in_proj_weight_cross_attn[2_5_6:5_1_2, :]
				lowercase_ : str							       =   in_proj_bias_cross_attn[2_5_6:5_1_2]
				lowercase_ : Optional[int]							       =   in_proj_weight_cross_attn[-2_5_6:, :]
				lowercase_ : Optional[Any]							       =   in_proj_bias_cross_attn[-2_5_6:]



def 						lowercase    (				):
		lowercase_ : Optional[int]							       =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowercase_ : Tuple							       =   Image.open(requests.get(__snake_case		,					stream=__snake_case	).raw	)

		return im



@torch.no_grad()
def 						lowercase    (				__snake_case				: Optional[int]		,					__snake_case				: Optional[int]=None		,					__snake_case				: Dict=False	):
		lowercase_       ,					lowercase_ : Tuple							       =   get_detr_config(__snake_case	)

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

    # finally, create HuggingFace model and load state dict
		lowercase_ : List[str]							       =   DetrForSegmentation(__snake_case	) if is_panoptic else DetrForObjectDetection(__snake_case	)
		model.load_state_dict(__snake_case	)
		model.eval()

		# verify our conversion on an image
		lowercase_ : List[str]							       =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
		lowercase_ : Union[str, Any]							       =   DetrImageProcessor(format=__snake_case	)

		lowercase_ : List[Any]							       =   processor(images=prepare_img()		,					return_tensors='''pt'''	)
		lowercase_ : Any							       =   encoding['''pixel_values''']

		lowercase_ : Tuple							       =   detr(__snake_case	)
		lowercase_ : int							       =   model(__snake_case	)

		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(__snake_case	).mkdir(exist_ok=__snake_case	)
				model.save_pretrained(__snake_case	)
				processor.save_pretrained(__snake_case	)

		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		:       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		:       Any  				=		parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











"""simple docstring"""



def 						lowercase    (				__snake_case				: int	):
		if not isinstance(__snake_case		,					__snake_case	):
				raise ValueError('''Input must be an integer'''	)
		if input_num <= 0:
				raise ValueError('''Input must be positive'''	)
		return sum(
		    divisor for divisor in range(1		,					input_num // 2 + 1	) if input_num % divisor == 0	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



