





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


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

		return orig_key


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


def 						__UpperCamelCase						(			):
		__UpperCAmelCase	:							Any     =     "http://images.cocodataset.org/val2017/000000039769.jpg"
		__UpperCAmelCase	:							List[str]     =     Image.open(requests.get(_UpperCAmelCase,       stream=_UpperCAmelCase  ).raw  )

		return im


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

		# load image processor
		__UpperCAmelCase	:							int     =     DeformableDetrImageProcessor(format="coco_detection"  )

		# prepare image
		__UpperCAmelCase	:							Dict     =     prepare_img()
		__UpperCAmelCase	:							List[Any]     =     image_processor(images=_UpperCAmelCase,       return_tensors="pt"  )
		__UpperCAmelCase	:							str     =     encoding["pixel_values"]

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

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

		__UpperCAmelCase	:							List[Any]     =     "cuda" if torch.cuda.is_available() else "cpu"
		model.to(_UpperCAmelCase  )
		# verify our conversion
		__UpperCAmelCase	:							str     =     model(pixel_values.to(_UpperCAmelCase  )  )

		__UpperCAmelCase	:							List[str]     =     torch.tensor(
		    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]  )
		__UpperCAmelCase	:							Optional[int]     =     torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]  )

		if single_scale:
				__UpperCAmelCase	:							List[str]     =     torch.tensor(
				    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]  )
				__UpperCAmelCase	:							Optional[Any]     =     torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]  )

		if single_scale and dilation:
				__UpperCAmelCase	:							int     =     torch.tensor(
				    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]  )
				__UpperCAmelCase	:							Union[str, Any]     =     torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]  )

		if with_box_refine:
				__UpperCAmelCase	:							Dict     =     torch.tensor(
				    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]  )
				__UpperCAmelCase	:							Tuple     =     torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]  )

		if with_box_refine and two_stage:
				__UpperCAmelCase	:							Dict     =     torch.tensor(
				    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]  )
				__UpperCAmelCase	:							Dict     =     torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]  )

		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:
				__UpperCAmelCase	:							Optional[Any]     =     "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__      :   Optional[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."
			)
			lowerCAmelCase__      :   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'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




