


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__     			=							logging.get_logger(__name__)
def        UpperCAmelCase_      (							__snake_case		)  ->    Dict:







	"""simple docstring"""


	if "backbone.0.body" in orig_key:
		_lowercase							   =orig_key.replace('''backbone.0.body'''  ,		'''backbone.conv_encoder.model'''		)
	if "transformer" in orig_key:
		_lowercase							   =orig_key.replace('''transformer.'''  ,		''''''		)
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			_lowercase							   =orig_key.replace('''norm1'''  ,		'''self_attn_layer_norm'''		)
		else:
			_lowercase							   =orig_key.replace('''norm1'''  ,		'''encoder_attn_layer_norm'''		)
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			_lowercase							   =orig_key.replace('''norm2'''  ,		'''final_layer_norm'''		)
		else:
			_lowercase							   =orig_key.replace('''norm2'''  ,		'''self_attn_layer_norm'''		)
	if "norm3" in orig_key:
		_lowercase							   =orig_key.replace('''norm3'''  ,		'''final_layer_norm'''		)
	if "linear1" in orig_key:
		_lowercase							   =orig_key.replace('''linear1'''  ,		'''fc1'''		)
	if "linear2" in orig_key:
		_lowercase							   =orig_key.replace('''linear2'''  ,		'''fc2'''		)
	if "query_embed" in orig_key:
		_lowercase							   =orig_key.replace('''query_embed'''  ,		'''query_position_embeddings'''		)
	if "cross_attn" in orig_key:
		_lowercase							   =orig_key.replace('''cross_attn'''  ,		'''encoder_attn'''		)

	return orig_key
def        UpperCAmelCase_      (							__snake_case		)  ->    str:







	"""simple docstring"""


	for i in range(6		):
		# read in weights + bias of input projection layer of self-attention
		_lowercase							   =state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"		)
		_lowercase							   =state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_bias"		)
		# next, add query, keys and values (in that order) to the state dict
		_lowercase							   =in_proj_weight[:256, :]
		_lowercase							   =in_proj_bias[:256]
		_lowercase							   =in_proj_weight[256:512, :]
		_lowercase							   =in_proj_bias[256:512]
		_lowercase							   =in_proj_weight[-256:, :]
		_lowercase							   =in_proj_bias[-256:]
def        UpperCAmelCase_      (							)  ->    Tuple:







	"""simple docstring"""


	_lowercase							   ='http://images.cocodataset.org/val2017/000000039769.jpg'
	_lowercase							   =Image.open(requests.get(lowercase_  ,		stream=lowercase_		).raw		)

	return im
@torch.no_grad()
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		)  ->    Dict:







	"""simple docstring"""


	_lowercase							   =DeformableDetrConfig()
	# set config attributes
	if single_scale:
		_lowercase							   =1
	_lowercase							   =dilation
	_lowercase							   =with_box_refine
	_lowercase							   =two_stage
	# set labels
	_lowercase							   =91
	_lowercase							   ='huggingface/label-files'
	_lowercase							   ='coco-detection-id2label.json'
	_lowercase							   =json.load(open(cached_download(hf_hub_url(lowercase_  ,		lowercase_  ,		repo_type='''dataset'''		)		)  ,		'''r'''		)		)
	_lowercase							   ={int(lowercase_		): v for k, v in idalabel.items()}
	_lowercase							   =idalabel
	_lowercase							   ={v: k for k, v in idalabel.items()}

	# load image processor
	_lowercase							   =DeformableDetrImageProcessor(format='''coco_detection'''		)

	# prepare image
	_lowercase							   =prepare_img()
	_lowercase							   =image_processor(images=lowercase_  ,		return_tensors='''pt'''		)
	_lowercase							   =encoding['pixel_values']

	logger.info('''Converting model...'''		)

	# load original state dict
	_lowercase							   =torch.load(lowercase_  ,		map_location='''cpu'''		)['model']
	# rename keys
	for key in state_dict.copy().keys():
		_lowercase							   =state_dict.pop(lowercase_		)
		_lowercase							   =val
	# query, key and value matrices need special treatment
	read_in_q_k_v(lowercase_		)
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	_lowercase							   ='model.'
	for key in state_dict.copy().keys():
		if not key.startswith('''class_embed'''		) and not key.startswith('''bbox_embed'''		):
			_lowercase							   =state_dict.pop(lowercase_		)
			_lowercase							   =val
    # finally, create HuggingFace model and load state dict
	_lowercase							   =DeformableDetrForObjectDetection(lowercase_		)
	model.load_state_dict(lowercase_		)
	model.eval()

	_lowercase							   ='cuda' if torch.cuda.is_available() else 'cpu'
	model.to(lowercase_		)
	# verify our conversion
	_lowercase							   =model(pixel_values.to(lowercase_		)		)

	_lowercase							   =torch.tensor(
	    [[-9.66_45, -4.34_49, -5.87_05], [-9.70_35, -3.85_04, -5.07_24], [-10.56_34, -5.33_79, -7.51_16]]		)
	_lowercase							   =torch.tensor([[0.86_93, 0.22_89, 0.24_92], [0.31_50, 0.54_89, 0.58_45], [0.55_63, 0.75_80, 0.85_18]]		)

	if single_scale:
		_lowercase							   =torch.tensor(
		    [[-9.90_51, -4.25_41, -6.48_52], [-9.69_47, -4.08_54, -6.80_33], [-10.06_65, -5.84_70, -7.70_03]]		)
		_lowercase							   =torch.tensor([[0.72_92, 0.49_91, 0.55_32], [0.79_59, 0.24_26, 0.42_36], [0.75_82, 0.35_18, 0.44_51]]		)

	if single_scale and dilation:
		_lowercase							   =torch.tensor(
		    [[-8.96_52, -4.10_74, -5.66_35], [-9.05_96, -4.94_47, -6.60_75], [-10.11_78, -4.52_75, -6.26_71]]		)
		_lowercase							   =torch.tensor([[0.76_65, 0.41_30, 0.47_69], [0.83_64, 0.18_41, 0.33_91], [0.62_61, 0.38_95, 0.79_78]]		)

	if with_box_refine:
		_lowercase							   =torch.tensor(
		    [[-8.88_95, -5.41_87, -6.81_53], [-8.47_06, -6.16_68, -7.61_84], [-9.00_42, -5.53_59, -6.91_41]]		)
		_lowercase							   =torch.tensor([[0.78_28, 0.22_08, 0.43_23], [0.08_92, 0.59_96, 0.13_19], [0.55_24, 0.63_89, 0.89_14]]		)

	if with_box_refine and two_stage:
		_lowercase							   =torch.tensor(
		    [[-6.71_08, -4.32_13, -6.37_77], [-8.90_14, -6.17_99, -6.72_40], [-6.93_15, -4.47_35, -6.22_98]]		)
		_lowercase							   =torch.tensor([[0.25_83, 0.54_99, 0.46_83], [0.76_52, 0.90_68, 0.48_82], [0.54_90, 0.27_63, 0.05_64]]		)

	print('''Logits:'''  ,		outputs.logits[0, :3, :3]		)

	assert torch.allclose(outputs.logits[0, :3, :3]  ,		expected_logits.to(lowercase_		)  ,		atol=1e-4		)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,		expected_boxes.to(lowercase_		)  ,		atol=1e-4		)

	print('''Everything ok!'''		)

	# Save model and image processor
	logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."		)
	Path(lowercase_		).mkdir(exist_ok=lowercase_		)
	model.save_pretrained(lowercase_		)
	image_processor.save_pretrained(lowercase_		)

	# Push to hub
	if push_to_hub:
		_lowercase							   ='deformable-detr'
		model_name += "-single-scale" if single_scale else ""
		model_name += "-dc5" if dilation else ""
		model_name += "-with-box-refine" if with_box_refine else ""
		model_name += "-two-stage" if two_stage else ""
		print('''Pushing model to hub...'''		)
		model.push_to_hub(repo_path_or_name=lowercase_  ,		organization='''nielsr'''  ,		commit_message='''Add model'''		)


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

		parser.add_argument(
		    '''--checkpoint_path''',
		    type=str,
		    default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
		    help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
		)
		parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
		parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
		parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
		parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path to the folder to output PyTorch model.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)
		UpperCAmelCase__     			=							parser.parse_args()
		convert_deformable_detr_checkpoint(
		    args.checkpoint_path,
		    args.single_scale,
		    args.dilation,
		    args.with_box_refine,
		    args.two_stage,
		    args.pytorch_dump_folder_path,
		    args.push_to_hub,
		)





def        UpperCAmelCase_      (							__snake_case		)  ->    bool:







 """simple docstring"""


 if not isinstance(__snake_case  ,		__snake_case		):
  _lowercase							   =F"Input value of [number={number}] must be an integer"
  raise TypeError(__snake_case		)
 if number < 0 or number % 2 == 1:
  return False
 _lowercase							   =int(number**0.5		)
 return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

