





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()
__lowerCAmelCase			:List[Any]	 =     logging.get_logger(__name__)







def  A					(				UpperCAmelCase					):
					if "backbone.0.body" in orig_key:
										_snake_case							:       Dict												= orig_key.replace("backbone.0.body"	,	"backbone.conv_encoder.model"					)
					if "transformer" in orig_key:
										_snake_case							:       Any												= orig_key.replace("transformer."	,	""					)
					if "norm1" in orig_key:
										if "encoder" in orig_key:
															_snake_case							:       Tuple												= orig_key.replace("norm1"	,	"self_attn_layer_norm"					)
										else:
															_snake_case							:       Dict												= orig_key.replace("norm1"	,	"encoder_attn_layer_norm"					)
					if "norm2" in orig_key:
										if "encoder" in orig_key:
															_snake_case							:       Tuple												= orig_key.replace("norm2"	,	"final_layer_norm"					)
										else:
															_snake_case							:       str												= orig_key.replace("norm2"	,	"self_attn_layer_norm"					)
					if "norm3" in orig_key:
										_snake_case							:       Dict												= orig_key.replace("norm3"	,	"final_layer_norm"					)
					if "linear1" in orig_key:
										_snake_case							:       str												= orig_key.replace("linear1"	,	"fc1"					)
					if "linear2" in orig_key:
										_snake_case							:       Any												= orig_key.replace("linear2"	,	"fc2"					)
					if "query_embed" in orig_key:
										_snake_case							:       str												= orig_key.replace("query_embed"	,	"query_position_embeddings"					)
					if "cross_attn" in orig_key:
										_snake_case							:       Tuple												= orig_key.replace("cross_attn"	,	"encoder_attn"					)

					return orig_key







def  A					(				UpperCAmelCase					):
					# transformer decoder self-attention layers
					for i in range(6					):
										# read in weights + bias of input projection layer of self-attention
										_snake_case							:       str												= state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""					)
										_snake_case							:       Optional[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
										_snake_case							:       Optional[Any]												= in_proj_weight[:256, :]
										_snake_case							:       Optional[int]												= in_proj_bias[:256]
										_snake_case							:       Dict												= in_proj_weight[256:512, :]
										_snake_case							:       List[Any]												= in_proj_bias[256:512]
										_snake_case							:       List[Any]												= in_proj_weight[-256:, :]
										_snake_case							:       List[str]												= in_proj_bias[-256:]







def  A					(				):
					_snake_case							:       Any												= "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case							:       Tuple												= Image.open(requests.get(UpperCAmelCase	,	stream=UpperCAmelCase					).raw					)

					return im







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

					# load image processor
					_snake_case							:       List[Any]												= DeformableDetrImageProcessor(format="coco_detection"					)

					# prepare image
					_snake_case							:       int												= prepare_img()
					_snake_case							:       List[Any]												= image_processor(images=UpperCAmelCase	,	return_tensors="pt"					)
					_snake_case							:       Union[str, Any]												= encoding["pixel_values"]

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

					# load original state dict
					_snake_case							:       Optional[Any]												= torch.load(UpperCAmelCase	,	map_location="cpu"					)["model"]
					# rename keys
					for key in state_dict.copy().keys():
										_snake_case							:       List[str]												= state_dict.pop(UpperCAmelCase					)
										_snake_case							:       int												= 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							:       Dict												= "model."
					for key in state_dict.copy().keys():
										if not key.startswith("class_embed"					) and not key.startswith("bbox_embed"					):
															_snake_case							:       Optional[int]												= state_dict.pop(UpperCAmelCase					)
															_snake_case							:       str												= val
    # finally, create HuggingFace model and load state dict
					_snake_case							:       List[Any]												= DeformableDetrForObjectDetection(UpperCAmelCase					)
					model.load_state_dict(UpperCAmelCase					)
					model.eval()

					_snake_case							:       Optional[Any]												= "cuda" if torch.cuda.is_available() else "cpu"
					model.to(UpperCAmelCase					)
					# verify our conversion
					_snake_case							:       Any												= model(pixel_values.to(UpperCAmelCase					)					)

					_snake_case							:       Optional[int]												= torch.tensor(
					    [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]					)
					_snake_case							:       List[str]												= torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]					)

					if single_scale:
										_snake_case							:       Tuple												= torch.tensor(
										    [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]					)
										_snake_case							:       Union[str, Any]												= torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]					)

					if single_scale and dilation:
										_snake_case							:       Optional[int]												= torch.tensor(
										    [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]					)
										_snake_case							:       Union[str, Any]												= torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]					)

					if with_box_refine:
										_snake_case							:       Any												= torch.tensor(
										    [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]					)
										_snake_case							:       List[str]												= torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]					)

					if with_box_refine and two_stage:
										_snake_case							:       str												= torch.tensor(
										    [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]					)
										_snake_case							:       List[Any]												= torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]					)

					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							:       Tuple												= "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__":
							__lowerCAmelCase			: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.'
							)
							__lowerCAmelCase			:str	 =     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,
							)






import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def  A					(				):
					_snake_case							:       Optional[Any]												= ArgumentParser(
					    description=(
					        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
					    )					)

					# Optional arguments for the launch helper
					parser.add_argument("--num_cores"	,	type=UpperCAmelCase	,	default=1	,	help="Number of TPU cores to use (1 or 8)."					)

					# positional
					parser.add_argument(
					    "training_script"	,	type=UpperCAmelCase	,	help=(
					        "The full path to the single TPU training "
					        "program/script to be launched in parallel, "
					        "followed by all the arguments for the "
					        "training script"
					    )	,	)

					# rest from the training program
					parser.add_argument("training_script_args"	,	nargs=UpperCAmelCase					)

					return parser.parse_args()







def  A					(				):
					_snake_case							:       Any												= parse_args()

					# Import training_script as a module.
					_snake_case							:       Any												= Path(args.training_script					)
					sys.path.append(str(script_fpath.parent.resolve()					)					)
					_snake_case							:       Tuple												= script_fpath.stem
					_snake_case							:       str												= importlib.import_module(UpperCAmelCase					)

					# Patch sys.argv
					_snake_case							:       str												= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

					xmp.spawn(mod._mp_fn	,	args=()	,	nprocs=args.num_cores					)


if __name__ == "__main__":
							main()