





"""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()
_snake_case	:						Optional[Any]					=					logging.get_logger(__name__)






def    A__							(     UpperCamelCase			):
							if "backbone.0.body" in orig_key:
														A						=    orig_key.replace("backbone.0.body"   ,    "backbone.conv_encoder.model"			)
							if "transformer" in orig_key:
														A						=    orig_key.replace("transformer."   ,    ""			)
							if "norm1" in orig_key:
														if "encoder" in orig_key:
																					A						=    orig_key.replace("norm1"   ,    "self_attn_layer_norm"			)
														else:
																					A						=    orig_key.replace("norm1"   ,    "encoder_attn_layer_norm"			)
							if "norm2" in orig_key:
														if "encoder" in orig_key:
																					A						=    orig_key.replace("norm2"   ,    "final_layer_norm"			)
														else:
																					A						=    orig_key.replace("norm2"   ,    "self_attn_layer_norm"			)
							if "norm3" in orig_key:
														A						=    orig_key.replace("norm3"   ,    "final_layer_norm"			)
							if "linear1" in orig_key:
														A						=    orig_key.replace("linear1"   ,    "fc1"			)
							if "linear2" in orig_key:
														A						=    orig_key.replace("linear2"   ,    "fc2"			)
							if "query_embed" in orig_key:
														A						=    orig_key.replace("query_embed"   ,    "query_position_embeddings"			)
							if "cross_attn" in orig_key:
														A						=    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
														A						=    state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"			)
														A						=    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
														A						=    in_proj_weight[:256, :]
														A						=    in_proj_bias[:256]
														A						=    in_proj_weight[256:512, :]
														A						=    in_proj_bias[256:512]
														A						=    in_proj_weight[-256:, :]
														A						=    in_proj_bias[-256:]






def    A__							(     ):
							A						=    "http://images.cocodataset.org/val2017/000000039769.jpg"
							A						=    Image.open(requests.get(a_   ,    stream=a_			).raw			)

							return im






@torch.no_grad()
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    ):
							A						=    DeformableDetrConfig()
							# set config attributes
							if single_scale:
														A						=    1
							A						=    dilation
							A						=    with_box_refine
							A						=    two_stage
							# set labels
							A						=    91
							A						=    "huggingface/label-files"
							A						=    "coco-detection-id2label.json"
							A						=    json.load(open(cached_download(hf_hub_url(a_   ,    a_   ,    repo_type="dataset"			)			)   ,    "r"			)			)
							A						=    {int(a_			): v for k, v in idalabel.items()}
							A						=    idalabel
							A						=    {v: k for k, v in idalabel.items()}

							# load image processor
							A						=    DeformableDetrImageProcessor(format="coco_detection"			)

							# prepare image
							A						=    prepare_img()
							A						=    image_processor(images=a_   ,    return_tensors="pt"			)
							A						=    encoding["pixel_values"]

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

							# load original state dict
							A						=    torch.load(a_   ,    map_location="cpu"			)["model"]
							# rename keys
							for key in state_dict.copy().keys():
														A						=    state_dict.pop(a_			)
														A						=    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
							A						=    "model."
							for key in state_dict.copy().keys():
														if not key.startswith("class_embed"			) and not key.startswith("bbox_embed"			):
																					A						=    state_dict.pop(a_			)
																					A						=    val
    # finally, create HuggingFace model and load state dict
							A						=    DeformableDetrForObjectDetection(a_			)
							model.load_state_dict(a_			)
							model.eval()

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

							A						=    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]]			)
							A						=    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:
														A						=    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]]			)
														A						=    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:
														A						=    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]]			)
														A						=    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:
														A						=    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]]			)
														A						=    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:
														A						=    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]]			)
														A						=    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(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:
														A						=    "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__":
		_snake_case	:						List[str]					=					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.'
		)
		_snake_case	:						Union[str, 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,
		)




from __future__ import annotations

from typing import Any

class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			Any							,			snake_case__				:			Any ):  # noqa: A002
			"""simple docstring"""





			A				=item
			A				=next


class        UpperCamelCase__:

	"""simple docstring"""

	def __init__(      self				:			Optional[Any] ):
			"""simple docstring"""





			A				=None
			A				=0







	def        _a					(      self				:			str							,			snake_case__				:			Any ):
			"""simple docstring"""





			A				=Node(snake_case__							,			self.head )
			self.size += 1







	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			if self.head is None:
					return None
			else:
					A				=self.head.item
					A				=self.head.next
					self.size -= 1
					return item







	def        _a					(      self				:			Any ):
			"""simple docstring"""





			return self.head is None







	def __str__(      self				:			Dict ):
			"""simple docstring"""





			if self.is_empty():
					return ""
			else:
					A				=self.head
					A				=""
					A				=[]
					while iterate:
							item_list.append(str(iterate.item ) )
							A				=iterate.next

					A				=" --> ".join(snake_case__ )

					return item_str







	def __len__(      self				:			Union[str, Any] ):
			"""simple docstring"""





			return self.size




