




'''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()
_SCREAMING_SNAKE_CASE							:     Dict   	=				logging.get_logger(__name__)


def   UpperCamelCase_(			snake_case							: str  ):

							'''simple docstring'''






							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   UpperCamelCase_(			snake_case							: Union[str, Any]  ):

							'''simple docstring'''






							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_5_6, :]
														snake_case_			      =   in_proj_bias[:2_5_6]
														snake_case_			      =   in_proj_weight[2_5_6:5_1_2, :]
														snake_case_			      =   in_proj_bias[2_5_6:5_1_2]
														snake_case_			      =   in_proj_weight[-2_5_6:, :]
														snake_case_			      =   in_proj_bias[-2_5_6:]


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   "http://images.cocodataset.org/val2017/000000039769.jpg"
							snake_case_			      =   Image.open(requests.get(snake_case							,       stream=snake_case  ).raw  )

							return im


@torch.no_grad()
def   UpperCamelCase_(			snake_case							: List[str]							,       snake_case							: List[Any]							,       snake_case							: Any							,       snake_case							: Dict							,       snake_case							: List[str]							,       snake_case							: Optional[Any]							,       snake_case							: str							,       ):

							'''simple docstring'''






							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_			      =   9_1
							snake_case_			      =   "huggingface/label-files"
							snake_case_			      =   "coco-detection-id2label.json"
							snake_case_			      =   json.load(open(cached_download(hf_hub_url(snake_case							,       snake_case							,       repo_type="dataset"  )  )							,       "r"  )  )
							snake_case_			      =   {int(snake_case  ): 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=snake_case							,       return_tensors="pt"  )
							snake_case_			      =   encoding["pixel_values"]

							logger.info("Converting model..."  )

							# load original state dict
							snake_case_			      =   torch.load(snake_case							,       map_location="cpu"  )["model"]
							# rename keys
							for key in state_dict.copy().keys():
														snake_case_			      =   state_dict.pop(snake_case  )
														snake_case_			      =   val
							# query, key and value matrices need special treatment
							read_in_q_k_v(snake_case  )
							# 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(snake_case  )
																					snake_case_			      =   val
    # finally, create HuggingFace model and load state dict
							snake_case_			      =   DeformableDetrForObjectDetection(snake_case  )
							model.load_state_dict(snake_case  )
							model.eval()

							snake_case_			      =   "cuda" if torch.cuda.is_available() else "cpu"
							model.to(snake_case  )
							# verify our conversion
							snake_case_			      =   model(pixel_values.to(snake_case  )  )

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

							# 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=snake_case							,       organization="nielsr"							,       commit_message="Add model"  )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     int   	=				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."
		)
		_SCREAMING_SNAKE_CASE							:     Dict   	=				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'''





import heapq


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: List[str]							,       snake_case							: Dict  ):

							'''simple docstring'''






							snake_case_			      =   [(0, start)]  # cost from start node,end node
							snake_case_			      =   set()
							while heap:
														((snake_case_)      ,   (snake_case_))			      =   heapq.heappop(snake_case  )
														if u in visited:
																					continue
														visited.add(snake_case  )
														if u == end:
																					return cost
														for v, c in graph[u]:
																					if v in visited:
																												continue
																					snake_case_			      =   cost + c
																					heapq.heappush(snake_case							,       (next_item, v)  )
							return -1


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_SCREAMING_SNAKE_CASE							:     Any   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_SCREAMING_SNAKE_CASE							:     Tuple   	=				dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_SCREAMING_SNAKE_CASE							:     str   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
		import doctest

		doctest.testmod()




