


'''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()
lowercase__					  =		logging.get_logger(__name__)






def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  List[Any]:







     '''simple docstring'''






     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 __UpperCamelCase (      __lowerCamelCase		:			Any					)   ->  Union[str, Any]:







     '''simple docstring'''






     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 __UpperCamelCase (      )   ->  Optional[Any]:







     '''simple docstring'''






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

     return im






@torch.no_grad()
def __UpperCamelCase (      __lowerCamelCase		:			str   ,	__lowerCamelCase		:			str   ,	__lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Optional[Any]   ,	__lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			Union[str, Any]   ,	__lowerCamelCase		:			Dict   ,	)   ->  int:







     '''simple docstring'''






     _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(__lowerCamelCase   ,	__lowerCamelCase   ,	repo_type="dataset"					)					)   ,	"r"					)					)
     _a            =		{int(__lowerCamelCase					): 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=__lowerCamelCase   ,	return_tensors="pt"					)
     _a            =		encoding["pixel_values"]

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

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

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

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

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


if __name__ == "__main__":
   lowercase__					  =		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."
   )
   lowercase__					  =		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 gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__					  =		False





@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def 			a_       (      self		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		"cyberpunk 2077"
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

