




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

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase      : Tuple					      =   []
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	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Tuple:
					a		       =							state_dict.pop(__lowerCamelCase					)
					a		       =							val





def 	__A	(			__lowerCamelCase					)   -> Optional[int]:
					a		       =							OrderedDict()
					for key, value in state_dict.items():
										if "backbone.0.body" in key:
															a		       =							key.replace("""backbone.0.body"""							,    """backbone.conv_encoder.model"""					)
															a		       =							value
										else:
															a		       =							value

					return new_state_dict





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=False					)   -> Dict:
					a		       =							""""""
					if is_panoptic:
										a		       =							"""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		       =							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'					)
										a		       =							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		       =							in_proj_weight[:256, :]
										a		       =							in_proj_bias[:256]
										a		       =							in_proj_weight[256:512, :]
										a		       =							in_proj_bias[256:512]
										a		       =							in_proj_weight[-256:, :]
										a		       =							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		       =							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'					)
										a		       =							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		       =							in_proj_weight[:256, :]
										a		       =							in_proj_bias[:256]
										a		       =							in_proj_weight[256:512, :]
										a		       =							in_proj_bias[256:512]
										a		       =							in_proj_weight[-256:, :]
										a		       =							in_proj_bias[-256:]
										# read in weights + bias of input projection layer of cross-attention
										a		       =							state_dict.pop(
										    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'					)
										a		       =							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		       =							in_proj_weight_cross_attn[:256, :]
										a		       =							in_proj_bias_cross_attn[:256]
										a		       =							in_proj_weight_cross_attn[256:512, :]
										a		       =							in_proj_bias_cross_attn[256:512]
										a		       =							in_proj_weight_cross_attn[-256:, :]
										a		       =							in_proj_bias_cross_attn[-256:]





def 	__A	(			)   -> int:
					a		       =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					a		       =							Image.open(requests.get(__lowerCamelCase							,    stream=__lowerCamelCase					).raw					)

					return im





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> Any:
					a		       =							DetrConfig()
					# set backbone and dilation attributes
					if "resnet101" in model_name:
										a		       =							"""resnet101"""
					if "dc5" in model_name:
										a		       =							True
					a		       =							"""panoptic""" in model_name
					if is_panoptic:
										a		       =							250
					else:
										a		       =							91
										a		       =							"""huggingface/label-files"""
										a		       =							"""coco-detection-id2label.json"""
										a		       =							json.load(open(hf_hub_download(__lowerCamelCase							,    __lowerCamelCase							,    repo_type="""dataset"""					)							,    """r"""					)					)
										a		       =							{int(__lowerCamelCase					): v for k, v in idalabel.items()}
										a		       =							idalabel
										a		       =							{v: k for k, v in idalabel.items()}

					# load image processor
					a		       =							"""coco_panoptic""" if is_panoptic else """coco_detection"""
					a		       =							DetrImageProcessor(format=__lowerCamelCase					)

					# prepare image
					a		       =							prepare_img()
					a		       =							image_processor(images=__lowerCamelCase							,    return_tensors="""pt"""					)
					a		       =							encoding["""pixel_values"""]

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

					# load original model from torch hub
					a		       =							torch.hub.load("""facebookresearch/detr"""							,    __lowerCamelCase							,    pretrained=__lowerCamelCase					).eval()
					a		       =							detr.state_dict()
					# rename keys
					for src, dest in rename_keys:
										if is_panoptic:
															a		       =							"""detr.""" + src
										rename_key(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
					a		       =							rename_backbone_keys(__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		       =							"""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		       =							state_dict.pop(__lowerCamelCase					)
																				a		       =							val
															elif "class_labels_classifier" in key or "bbox_predictor" in key:
																				a		       =							state_dict.pop(__lowerCamelCase					)
																				a		       =							val
															elif key.startswith("""bbox_attention"""					) or key.startswith("""mask_head"""					):
																				continue
															else:
																				a		       =							state_dict.pop(__lowerCamelCase					)
																				a		       =							val
										else:
															if not key.startswith("""class_labels_classifier"""					) and not key.startswith("""bbox_predictor"""					):
																				a		       =							state_dict.pop(__lowerCamelCase					)
																				a		       =							val
    # finally, create HuggingFace model and load state dict
					a		       =							DetrForSegmentation(__lowerCamelCase					) if is_panoptic else DetrForObjectDetection(__lowerCamelCase					)
					model.load_state_dict(__lowerCamelCase					)
					model.eval()
					# verify our conversion
					a		       =							detr(__lowerCamelCase					)
					a		       =							model(__lowerCamelCase					)
					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(__lowerCamelCase					).mkdir(exist_ok=__lowerCamelCase					)
					model.save_pretrained(__lowerCamelCase					)
					image_processor.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
		__UpperCamelCase      : int					      =   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."
		)
		__UpperCamelCase      : Tuple					      =   parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)





def 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


if __name__ == "__main__":
		print(F'{solution() = }')

