





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()
SCREAMING_SNAKE_CASE__       :			Any			      =						logging.get_logger(__name__)






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

			a__	:						Union[str, Any]      		=  DetrConfig(use_timm_backbone=lowerCamelCase							, backbone_config=lowerCamelCase   )

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

			return config, is_panoptic






def       _A  (      lowerCamelCase   ):
			# here we list all keys to be renamed (original name on the left, our name on the right)
			a__	:						Dict      		=  []

			# 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       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
			a__	:						List[str]      		=  state_dict.pop(lowerCamelCase   )
			a__	:						Tuple      		=  val






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






def       _A  (      ):
			a__	:						List[Any]      		=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

			return im






@torch.no_grad()
def       _A  (      lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=False   ):
			a__	:						Tuple      		=  get_detr_config(lowerCamelCase   )

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

    # finally, create HuggingFace model and load state dict
			a__	:						str      		=  DetrForSegmentation(lowerCamelCase   ) if is_panoptic else DetrForObjectDetection(lowerCamelCase   )
			model.load_state_dict(lowerCamelCase   )
			model.eval()

			# verify our conversion on an image
			a__	:						int      		=  '''coco_panoptic''' if is_panoptic else '''coco_detection'''
			a__	:						Any      		=  DetrImageProcessor(format=lowerCamelCase   )

			a__	:						List[Any]      		=  processor(images=prepare_img()							, return_tensors="pt"   )
			a__	:						Optional[int]      		=  encoding['''pixel_values''']

			a__	:						Optional[int]      		=  detr(lowerCamelCase   )
			a__	:						int      		=  model(lowerCamelCase   )

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

			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__":
		SCREAMING_SNAKE_CASE__       :			List[Any]			      =						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.""")
		SCREAMING_SNAKE_CASE__       :			Tuple			      =						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"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """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:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

