





'''simple docstring'''




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()
a__				: Optional[int]									=    logging.get_logger(__name__)




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

	return orig_key




def 	__lowerCamelCase (  UpperCAmelCase_       )      ->List[str]:
	# transformer decoder self-attention layers
	for i in range(6       ):
		# read in weights + bias of input projection layer of self-attention
		snake_case__  					=      state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''       )
		snake_case__  					=      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
		snake_case__  					=      in_proj_weight[:2_56, :]
		snake_case__  					=      in_proj_bias[:2_56]
		snake_case__  					=      in_proj_weight[2_56:5_12, :]
		snake_case__  					=      in_proj_bias[2_56:5_12]
		snake_case__  					=      in_proj_weight[-2_56:, :]
		snake_case__  					=      in_proj_bias[-2_56:]




def 	__lowerCamelCase (  )      ->Tuple:
	snake_case__  					=      'http://images.cocodataset.org/val2017/000000039769.jpg'
	snake_case__  					=      Image.open(requests.get(UpperCAmelCase_				,							stream=UpperCAmelCase_       ).raw       )

	return im




@torch.no_grad()
def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							)      ->Optional[Any]:
	snake_case__  					=      DeformableDetrConfig()
	# set config attributes
	if single_scale:
		snake_case__  					=      1
	snake_case__  					=      dilation
	snake_case__  					=      with_box_refine
	snake_case__  					=      two_stage
	# set labels
	snake_case__  					=      91
	snake_case__  					=      'huggingface/label-files'
	snake_case__  					=      'coco-detection-id2label.json'
	snake_case__  					=      json.load(open(cached_download(hf_hub_url(UpperCAmelCase_				,							UpperCAmelCase_				,							repo_type='dataset'       )       )				,							'r'       )       )
	snake_case__  					=      {int(UpperCAmelCase_       ): v for k, v in idalabel.items()}
	snake_case__  					=      idalabel
	snake_case__  					=      {v: k for k, v in idalabel.items()}

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

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

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

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

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

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

	if single_scale:
		snake_case__  					=      torch.tensor(
		    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]       )
		snake_case__  					=      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:
		snake_case__  					=      torch.tensor(
		    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]       )
		snake_case__  					=      torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]       )

	if with_box_refine:
		snake_case__  					=      torch.tensor(
		    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]       )
		snake_case__  					=      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:
		snake_case__  					=      torch.tensor(
		    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]       )
		snake_case__  					=      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(UpperCAmelCase_       )				,							atol=1E-4       )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3]				,							expected_boxes.to(UpperCAmelCase_       )				,							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(UpperCAmelCase_       ).mkdir(exist_ok=UpperCAmelCase_       )
	model.save_pretrained(UpperCAmelCase_       )
	image_processor.save_pretrained(UpperCAmelCase_       )

	# Push to hub
	if push_to_hub:
		snake_case__  					=      '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=UpperCAmelCase_				,							organization='nielsr'				,							commit_message='Add model'       )


if __name__ == "__main__":
			a__				: List[Any]									=    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.'''
			)
			a__				: List[Any]									=    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,
			)










'''simple docstring'''




a__				: Any									=    {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

a__				: Optional[Any]									=    [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

a__				: List[str]									=    [0 for x in range(len(graph))]
a__				: List[Any]									=    []




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
	snake_case__  					=      1
	while stack:
		snake_case__  					=      stack.pop()
		print(UpperCAmelCase_				,							clothes[current_clothing]       )
		order += 1




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
	snake_case__  					=      1
	for v in graph[u]:
		if not visited[v]:
			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )

	stack.append(UpperCAmelCase_       )




def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Union[str, Any]:
	for v in range(len(UpperCAmelCase_       )       ):
		if not visited[v]:
			depth_first_search(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )


if __name__ == "__main__":
			topological_sort(graph, visited)
			print(stack)
			print_stack(stack, clothes)



