





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()
lowerCAmelCase_    =				logging.get_logger(__name__)






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if "resnet-50" in model_name:
								snake_case_						:    List[str]				=  ResNetConfig.from_pretrained('''microsoft/resnet-50''' )
				elif "resnet-101" in model_name:
								snake_case_						:    List[Any]				=  ResNetConfig.from_pretrained('''microsoft/resnet-101''' )
				else:
								raise ValueError('''Model name should include either resnet50 or resnet101''' )

				snake_case_						:    Union[str, Any]				=  DetrConfig(use_timm_backbone=_UpperCamelCase ,				backbone_config=_UpperCamelCase )

				# set label attributes
				snake_case_						:    List[Any]				=  '''panoptic''' in model_name
				if is_panoptic:
								snake_case_						:    Any				=  250
				else:
								snake_case_						:    Any				=  91
								snake_case_						:    Dict				=  '''huggingface/label-files'''
								snake_case_						:    Any				=  '''coco-detection-id2label.json'''
								snake_case_						:    Optional[Any]				=  json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset''' ) ,				'''r''' ) )
								snake_case_						:    List[str]				=  {int(_UpperCamelCase ): v for k, v in idalabel.items()}
								snake_case_						:    Tuple				=  idalabel
								snake_case_						:    Optional[int]				=  {v: k for k, v in idalabel.items()}

				return config, is_panoptic






def      lowerCamelCase_						(				_UpperCamelCase )		->						Tuple:






				"""simple docstring"""


				snake_case_						:    Any				=  []

				# 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_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:    Optional[int]				=  state_dict.pop(_UpperCamelCase )
				snake_case_						:    List[str]				=  val






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=False )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    List[Any]				=  ''''''
				if is_panoptic:
								snake_case_						:    Optional[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)
								snake_case_						:    Optional[Any]				=  state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
								snake_case_						:    Tuple				=  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
								snake_case_						:    List[str]				=  in_proj_weight[:256, :]
								snake_case_						:    Dict				=  in_proj_bias[:256]
								snake_case_						:    Dict				=  in_proj_weight[256:512, :]
								snake_case_						:    Union[str, Any]				=  in_proj_bias[256:512]
								snake_case_						:    int				=  in_proj_weight[-256:, :]
								snake_case_						:    Tuple				=  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
								snake_case_						:    List[str]				=  state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
								snake_case_						:    Optional[Any]				=  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
								snake_case_						:    List[str]				=  in_proj_weight[:256, :]
								snake_case_						:    str				=  in_proj_bias[:256]
								snake_case_						:    int				=  in_proj_weight[256:512, :]
								snake_case_						:    str				=  in_proj_bias[256:512]
								snake_case_						:    List[str]				=  in_proj_weight[-256:, :]
								snake_case_						:    List[str]				=  in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								snake_case_						:    str				=  state_dict.pop(
								    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
								snake_case_						:    Optional[int]				=  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
								snake_case_						:    Optional[int]				=  in_proj_weight_cross_attn[:256, :]
								snake_case_						:    List[str]				=  in_proj_bias_cross_attn[:256]
								snake_case_						:    List[Any]				=  in_proj_weight_cross_attn[256:512, :]
								snake_case_						:    Union[str, Any]				=  in_proj_bias_cross_attn[256:512]
								snake_case_						:    Optional[int]				=  in_proj_weight_cross_attn[-256:, :]
								snake_case_						:    Optional[Any]				=  in_proj_bias_cross_attn[-256:]






def      lowerCamelCase_						(				)		->						str:






				"""simple docstring"""


				snake_case_						:    str				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:    Union[str, Any]				=  Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase ).raw )

				return im






@torch.no_grad()
def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase=None ,				_UpperCamelCase=False )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    int				=  get_detr_config(_UpperCamelCase )

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

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

				# verify our conversion on an image
				snake_case_						:    Optional[Any]				=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				snake_case_						:    Tuple				=  DetrImageProcessor(format=_UpperCamelCase )

				snake_case_						:    int				=  processor(images=prepare_img() ,				return_tensors='''pt''' )
				snake_case_						:    Dict				=  encoding['''pixel_values''']

				snake_case_						:    Optional[Any]				=  detr(_UpperCamelCase )
				snake_case_						:    List[str]				=  model(_UpperCamelCase )

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

				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__":
	lowerCAmelCase_    =				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.''')
	lowerCAmelCase_    =				parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
