




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  snake_case (							A      )							->							Optional[Any]:




	'''simple docstring'''


	if "backbone.0.body" in orig_key:
		lowerCamelCase_							:		Any							       =							orig_key.replace('backbone.0.body'						,       'backbone.conv_encoder.model'      )
	if "transformer" in orig_key:
		lowerCamelCase_							:		Union[str, Any]							       =							orig_key.replace('transformer.'						,       ''      )
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			lowerCamelCase_							:		Dict							       =							orig_key.replace('norm1'						,       'self_attn_layer_norm'      )
		else:
			lowerCamelCase_							:		Any							       =							orig_key.replace('norm1'						,       'encoder_attn_layer_norm'      )
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			lowerCamelCase_							:		Optional[Any]							       =							orig_key.replace('norm2'						,       'final_layer_norm'      )
		else:
			lowerCamelCase_							:		Dict							       =							orig_key.replace('norm2'						,       'self_attn_layer_norm'      )
	if "norm3" in orig_key:
		lowerCamelCase_							:		Tuple							       =							orig_key.replace('norm3'						,       'final_layer_norm'      )
	if "linear1" in orig_key:
		lowerCamelCase_							:		Dict							       =							orig_key.replace('linear1'						,       'fc1'      )
	if "linear2" in orig_key:
		lowerCamelCase_							:		List[Any]							       =							orig_key.replace('linear2'						,       'fc2'      )
	if "query_embed" in orig_key:
		lowerCamelCase_							:		Optional[Any]							       =							orig_key.replace('query_embed'						,       'query_position_embeddings'      )
	if "cross_attn" in orig_key:
		lowerCamelCase_							:		Optional[Any]							       =							orig_key.replace('cross_attn'						,       'encoder_attn'      )

	return orig_key






def  snake_case (							A      )							->							str:




	'''simple docstring'''


	for i in range(6      ):
		# read in weights + bias of input projection layer of self-attention
		lowerCamelCase_							:		Optional[int]							       =							state_dict.pop(f"decoder.layers.{i}.self_attn.in_proj_weight"      )
		lowerCamelCase_							:		int							       =							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
		lowerCamelCase_							:		Dict							       =							in_proj_weight[:256, :]
		lowerCamelCase_							:		Optional[int]							       =							in_proj_bias[:256]
		lowerCamelCase_							:		Tuple							       =							in_proj_weight[256:512, :]
		lowerCamelCase_							:		Optional[Any]							       =							in_proj_bias[256:512]
		lowerCamelCase_							:		int							       =							in_proj_weight[-256:, :]
		lowerCamelCase_							:		Optional[Any]							       =							in_proj_bias[-256:]






def  snake_case (							)							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCamelCase_							:		Union[str, Any]							       =							Image.open(requests.get(A						,       stream=A      ).raw      )

	return im






@torch.no_grad()
def  snake_case (							A						,       A						,       A						,       A						,       A						,       A						,       A						,       )							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Dict							       =							DeformableDetrConfig()
	# set config attributes
	if single_scale:
		lowerCamelCase_							:		Union[str, Any]							       =							1
	lowerCamelCase_							:		Any							       =							dilation
	lowerCamelCase_							:		Optional[int]							       =							with_box_refine
	lowerCamelCase_							:		Union[str, Any]							       =							two_stage
	# set labels
	lowerCamelCase_							:		List[str]							       =							91
	lowerCamelCase_							:		Union[str, Any]							       =							'huggingface/label-files'
	lowerCamelCase_							:		Tuple							       =							'coco-detection-id2label.json'
	lowerCamelCase_							:		Any							       =							json.load(open(cached_download(hf_hub_url(A						,       A						,       repo_type='dataset'      )      )						,       'r'      )      )
	lowerCamelCase_							:		List[str]							       =							{int(A      ): v for k, v in idalabel.items()}
	lowerCamelCase_							:		Union[str, Any]							       =							idalabel
	lowerCamelCase_							:		Dict							       =							{v: k for k, v in idalabel.items()}

	# load image processor
	lowerCamelCase_							:		List[str]							       =							DeformableDetrImageProcessor(format='coco_detection'      )

	# prepare image
	lowerCamelCase_							:		Dict							       =							prepare_img()
	lowerCamelCase_							:		int							       =							image_processor(images=A						,       return_tensors='pt'      )
	lowerCamelCase_							:		Dict							       =							encoding['pixel_values']

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

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

	lowerCamelCase_							:		Union[str, Any]							       =							'cuda' if torch.cuda.is_available() else 'cpu'
	model.to(A      )
	# verify our conversion
	lowerCamelCase_							:		Optional[Any]							       =							model(pixel_values.to(A      )      )

	lowerCamelCase_							:		Tuple							       =							torch.tensor(
	    [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
	lowerCamelCase_							:		str							       =							torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

	if single_scale:
		lowerCamelCase_							:		Tuple							       =							torch.tensor(
		    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
		lowerCamelCase_							:		int							       =							torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )

	if single_scale and dilation:
		lowerCamelCase_							:		Any							       =							torch.tensor(
		    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
		lowerCamelCase_							:		Dict							       =							torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

	if with_box_refine:
		lowerCamelCase_							:		List[Any]							       =							torch.tensor(
		    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
		lowerCamelCase_							:		int							       =							torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )

	if with_box_refine and two_stage:
		lowerCamelCase_							:		Optional[int]							       =							torch.tensor(
		    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
		lowerCamelCase_							:		str							       =							torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )

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

	assert torch.allclose(outputs.logits[0, :3, :3]						,       expected_logits.to(A      )						,       atol=1e-4      )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,       expected_boxes.to(A      )						,       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(A      ).mkdir(exist_ok=A      )
	model.save_pretrained(A      )
	image_processor.save_pretrained(A      )

	# Push to hub
	if push_to_hub:
		lowerCamelCase_							:		int							       =							'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=A						,       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,
							)






from typing import Callable






def  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	def wrapper(A      ):
		lowerCamelCase_							:		Dict							       =							function_with_docstring.__doc__
		return target_function

	return wrapper
