




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)







import math





def 	__A	(			__lowerCamelCase					)   -> bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,    int(math.sqrt(__lowerCamelCase					) + 1					)							,    6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 	__A	(			__lowerCamelCase = 6008_5147_5143					)   -> int:

	try:
		a		       =							int(__lowerCamelCase					)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""					)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""					)
	a		       =							0
	if is_prime(__lowerCamelCase					):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__lowerCamelCase					):
		return n
	for i in range(3							,    int(math.sqrt(__lowerCamelCase					)					) + 1							,    2					):
		if n % i == 0:
			if is_prime(n // i					):
				a		       =							n // i
				break
			elif is_prime(__lowerCamelCase					):
				a		       =							i
	return max_number


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

