


import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case   = logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case   = []
for i in range(6):
						# 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"),
    ]
)







def   A   (		A__		,		A__		,		A__ ):
					UpperCAmelCase						:				Optional[int]		  =			state_dict.pop(A__ )
					UpperCAmelCase						:				int		  =			val







def   A   (		A__ ):
					UpperCAmelCase						:				List[str]		  =			OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															UpperCAmelCase						:				Optional[int]		  =			key.replace("backbone.0.body"		,		"backbone.conv_encoder.model" )
															UpperCAmelCase						:				Any		  =			value
										else:
															UpperCAmelCase						:				List[Any]		  =			value

					return new_state_dict







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







def   A   (		):
					UpperCAmelCase						:				Dict		  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
					UpperCAmelCase						:				Union[str, Any]		  =			Image.open(requests.get(A__		,		stream=A__ ).raw )

					return im







@torch.no_grad()
def   A   (		A__		,		A__ ):
					UpperCAmelCase						:				Dict		  =			DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										UpperCAmelCase						:				Dict		  =			"resnet101"
					if "dc5" in model_name:
										UpperCAmelCase						:				str		  =			True
					UpperCAmelCase						:				Optional[Any]		  =			"panoptic" in model_name
					if is_panoptic:
										UpperCAmelCase						:				int		  =			2_5_0
					else:
										UpperCAmelCase						:				int		  =			9_1
										UpperCAmelCase						:				Union[str, Any]		  =			"huggingface/label-files"
										UpperCAmelCase						:				Tuple		  =			"coco-detection-id2label.json"
										UpperCAmelCase						:				Tuple		  =			json.load(open(hf_hub_download(A__		,		A__		,		repo_type="dataset" )		,		"r" ) )
										UpperCAmelCase						:				List[str]		  =			{int(A__ ): v for k, v in idalabel.items()}
										UpperCAmelCase						:				Union[str, Any]		  =			idalabel
										UpperCAmelCase						:				Tuple		  =			{v: k for k, v in idalabel.items()}

					# load image processor
					UpperCAmelCase						:				List[Any]		  =			"coco_panoptic" if is_panoptic else "coco_detection"
					UpperCAmelCase						:				Union[str, Any]		  =			DetrImageProcessor(format=A__ )

					# prepare image
					UpperCAmelCase						:				Dict		  =			prepare_img()
					UpperCAmelCase						:				Optional[Any]		  =			image_processor(images=A__		,		return_tensors="pt" )
					UpperCAmelCase						:				Any		  =			encoding["pixel_values"]

					logger.info(f'Converting model {model_name}...' )

					# load original model from torch hub
					UpperCAmelCase						:				Optional[Any]		  =			torch.hub.load("facebookresearch/detr"		,		A__		,		pretrained=A__ ).eval()
					UpperCAmelCase						:				Union[str, Any]		  =			detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															UpperCAmelCase						:				Dict		  =			"detr." + src
										rename_key(A__		,		A__		,		A__ )
					UpperCAmelCase						:				Tuple		  =			rename_backbone_keys(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						:				str		  =			"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						:				Optional[Any]		  =			state_dict.pop(A__ )
																				UpperCAmelCase						:				Dict		  =			val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				UpperCAmelCase						:				Optional[Any]		  =			state_dict.pop(A__ )
																				UpperCAmelCase						:				Any		  =			val
															elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
																				continue
															else:
																				UpperCAmelCase						:				Tuple		  =			state_dict.pop(A__ )
																				UpperCAmelCase						:				Any		  =			val
										else:
															if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
																				UpperCAmelCase						:				List[str]		  =			state_dict.pop(A__ )
																				UpperCAmelCase						:				Union[str, Any]		  =			val
    # finally, create HuggingFace model and load state dict
					UpperCAmelCase						:				Any		  =			DetrForSegmentation(A__ ) if is_panoptic else DetrForObjectDetection(A__ )
					model.load_state_dict(A__ )
					model.eval()
					# verify our conversion
					UpperCAmelCase						:				List[str]		  =			detr(A__ )
					UpperCAmelCase						:				Optional[int]		  =			model(A__ )
					assert torch.allclose(outputs.logits		,		original_outputs["pred_logits"]		,		atol=1E-4 )
					assert torch.allclose(outputs.pred_boxes		,		original_outputs["pred_boxes"]		,		atol=1E-4 )
					if is_panoptic:
										assert torch.allclose(outputs.pred_masks		,		original_outputs["pred_masks"]		,		atol=1E-4 )

					# 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__ )
					image_processor.save_pretrained(A__ )


if __name__ == "__main__":
						_snake_case   = argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name", default="detr_resnet50", type=str, 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."
						)
						_snake_case   = parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from cva import destroyAllWindows, imread, imshow, waitKey







def   A   (		A__		,		A__ ):
     UpperCAmelCase  , UpperCAmelCase						:				Optional[Any]		  =			img.shape[0], img.shape[1]

     def to_grayscale(A__		,		A__		,		A__ ):
          return 0.2126 * red + 0.587 * green + 0.114 * blue

     def normalize(A__ ):
          return min(A__		,		2_5_5 )

     for i in range(A__ ):
          for j in range(A__ ):
               UpperCAmelCase						:				Optional[Any]		  =			int(to_grayscale(*img[i][j] ) )
               UpperCAmelCase						:				List[str]		  =			[
                   normalize(A__ ),
                   normalize(greyscale + factor ),
                   normalize(greyscale + 2 * factor ),
               ]

     return img


if __name__ == "__main__":
      # read original image
      _snake_case   = {
          percentage: imread("image_data/lena.jpg", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f'''Original image with sepia (factor: {percentage})''', img)

      waitKey(0)
      destroyAllWindows()



