


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()
__lowerCamelCase       :       List[str]				   =			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase       :       str				   =			[]
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   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							List[Any]:
				A__     :							Any   				=state_dict.pop(snake_case_	)
				A__     :							Union[str, Any]   				=val



def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
				A__     :							Tuple   				=OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												A__     :							Dict   				=key.replace('''backbone.0.body''',	'''backbone.conv_encoder.model'''	)
												A__     :							Optional[Any]   				=value
								else:
												A__     :							Optional[int]   				=value

				return new_state_dict



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



def   SCREAMING_SNAKE_CASE__						(							)			->							List[str]:
				A__     :							Any   				='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				A__     :							List[str]   				=Image.open(requests.get(snake_case_,	stream=snake_case_	).raw	)

				return im



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

				# load image processor
				A__     :							Any   				='''coco_panoptic''' if is_panoptic else '''coco_detection'''
				A__     :							Optional[int]   				=DetrImageProcessor(format=snake_case_	)

				# prepare image
				A__     :							List[str]   				=prepare_img()
				A__     :							List[Any]   				=image_processor(images=snake_case_,	return_tensors='''pt'''	)
				A__     :							int   				=encoding['''pixel_values''']

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

				# load original model from torch hub
				A__     :							int   				=torch.hub.load('''facebookresearch/detr''',	snake_case_,	pretrained=snake_case_	).eval()
				A__     :							Optional[Any]   				=detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												A__     :							List[Any]   				='''detr.''' + src
								rename_key(snake_case_,	snake_case_,	snake_case_	)
				A__     :							int   				=rename_backbone_keys(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
				A__     :							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'''	)
												):
																A__     :							Tuple   				=state_dict.pop(snake_case_	)
																A__     :							List[Any]   				=val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																A__     :							Dict   				=state_dict.pop(snake_case_	)
																A__     :							List[str]   				=val
												elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
																continue
												else:
																A__     :							List[Any]   				=state_dict.pop(snake_case_	)
																A__     :							List[str]   				=val
								else:
												if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
																A__     :							Tuple   				=state_dict.pop(snake_case_	)
																A__     :							List[str]   				=val
    # finally, create HuggingFace model and load state dict
				A__     :							Union[str, Any]   				=DetrForSegmentation(snake_case_	) if is_panoptic else DetrForObjectDetection(snake_case_	)
				model.load_state_dict(snake_case_	)
				model.eval()
				# verify our conversion
				A__     :							Union[str, Any]   				=detr(snake_case_	)
				A__     :							Optional[int]   				=model(snake_case_	)
				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(snake_case_	).mkdir(exist_ok=snake_case_	)
				model.save_pretrained(snake_case_	)
				image_processor.save_pretrained(snake_case_	)


if __name__ == "__main__":
						__lowerCamelCase       :       str				   =			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."
						)
						__lowerCamelCase       :       Dict				   =			parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict:
				A__     :							Dict   				=len(snake_case_	)
				for i in range(length - 1	):
								A__     :							List[Any]   				=False
								for j in range(length - 1 - i	):
												if collection[j] > collection[j + 1]:
																A__     :							Dict   				=True
																A__				,  A__     :							Union[str, Any]   				=collection[j + 1], collection[j]
								if not swapped:
												break  # Stop iteration if the collection is sorted.
				return collection


if __name__ == "__main__":
						import doctest
						import time

						doctest.testmod()

						__lowerCamelCase       :       Union[str, Any]				   =			input("Enter numbers separated by a comma:").strip()
						__lowerCamelCase       :       Optional[int]				   =			[int(item) for item in user_input.split(",")]
						__lowerCamelCase       :       Optional[int]				   =			time.process_time()
						print(*bubble_sort(unsorted), sep=",")
						print(F"Processing time: {(time.process_time() - start)%1E9 + 7}")



