

'''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_       =       logging.get_logger(__name__)


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   List[str]:
      """simple docstring"""

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

      return orig_key


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int    )							->   Optional[Any]:
      """simple docstring"""

      for i in range(6    ):
            # read in weights + bias of input projection layer of self-attention
            SCREAMING_SNAKE_CASE_      :			str     =       state_dict.pop(F"decoder.layers.{i}.self_attn.in_proj_weight"    )
            SCREAMING_SNAKE_CASE_      :			Any     =       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
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       in_proj_weight[:2_5_6, :]
            SCREAMING_SNAKE_CASE_      :			Any     =       in_proj_bias[:2_5_6]
            SCREAMING_SNAKE_CASE_      :			int     =       in_proj_weight[2_5_6:5_1_2, :]
            SCREAMING_SNAKE_CASE_      :			List[str]     =       in_proj_bias[2_5_6:5_1_2]
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       in_proj_weight[-2_5_6:, :]
            SCREAMING_SNAKE_CASE_      :			Tuple     =       in_proj_bias[-2_5_6:]


def 					__lowerCamelCase			(      )							->   str:
      """simple docstring"""

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

      return im


@torch.no_grad()
def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[Any]  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]  ,  SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	Tuple  ,  SCREAMING_SNAKE_CASE_     :	List[Any]  ,  SCREAMING_SNAKE_CASE_     :	Optional[int]  ,  SCREAMING_SNAKE_CASE_     :	Tuple  ,  )							->   Optional[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Dict     =       DeformableDetrConfig()
      # set config attributes
      if single_scale:
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       1
      SCREAMING_SNAKE_CASE_      :			List[str]     =       dilation
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       with_box_refine
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       two_stage
      # set labels
      SCREAMING_SNAKE_CASE_      :			List[str]     =       9_1
      SCREAMING_SNAKE_CASE_      :			Any     =       "huggingface/label-files"
      SCREAMING_SNAKE_CASE_      :			List[Any]     =       "coco-detection-id2label.json"
      SCREAMING_SNAKE_CASE_      :			str     =       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  repo_type="dataset"    )    )  ,  "r"    )    )
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       {int(SCREAMING_SNAKE_CASE_    ): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       idalabel
      SCREAMING_SNAKE_CASE_      :			Tuple     =       {v: k for k, v in idalabel.items()}

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

      # prepare image
      SCREAMING_SNAKE_CASE_      :			Dict     =       prepare_img()
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       image_processor(images=SCREAMING_SNAKE_CASE_  ,  return_tensors="pt"    )
      SCREAMING_SNAKE_CASE_      :			Any     =       encoding["pixel_values"]

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

      # load original state dict
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.load(SCREAMING_SNAKE_CASE_  ,  map_location="cpu"    )["model"]
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_      :			Dict     =       state_dict.pop(SCREAMING_SNAKE_CASE_    )
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       val
      # query, key and value matrices need special treatment
      read_in_q_k_v(SCREAMING_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
      SCREAMING_SNAKE_CASE_      :			Tuple     =       "model."
      for key in state_dict.copy().keys():
            if not key.startswith("class_embed"    ) and not key.startswith("bbox_embed"    ):
                  SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       state_dict.pop(SCREAMING_SNAKE_CASE_    )
                  SCREAMING_SNAKE_CASE_      :			str     =       val
    # finally, create HuggingFace model and load state dict
      SCREAMING_SNAKE_CASE_      :			str     =       DeformableDetrForObjectDetection(SCREAMING_SNAKE_CASE_    )
      model.load_state_dict(SCREAMING_SNAKE_CASE_    )
      model.eval()

      SCREAMING_SNAKE_CASE_      :			int     =       "cuda" if torch.cuda.is_available() else "cpu"
      model.to(SCREAMING_SNAKE_CASE_    )
      # verify our conversion
      SCREAMING_SNAKE_CASE_      :			str     =       model(pixel_values.to(SCREAMING_SNAKE_CASE_    )    )

      SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.tensor(
          [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]    )
      SCREAMING_SNAKE_CASE_      :			str     =       torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]    )

      if single_scale:
            SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.tensor(
                [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]    )
            SCREAMING_SNAKE_CASE_      :			List[Any]     =       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:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.tensor(
                [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]    )
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]    )

      if with_box_refine:
            SCREAMING_SNAKE_CASE_      :			str     =       torch.tensor(
                [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]    )
            SCREAMING_SNAKE_CASE_      :			Tuple     =       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:
            SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.tensor(
                [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]    )
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       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(SCREAMING_SNAKE_CASE_    )  ,  atol=1E-4    )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]  ,  expected_boxes.to(SCREAMING_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(SCREAMING_SNAKE_CASE_    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_    )
      model.save_pretrained(SCREAMING_SNAKE_CASE_    )
      image_processor.save_pretrained(SCREAMING_SNAKE_CASE_    )

      # Push to hub
      if push_to_hub:
            SCREAMING_SNAKE_CASE_      :			Dict     =       "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=SCREAMING_SNAKE_CASE_  ,  organization="nielsr"  ,  commit_message="Add model"    )


if __name__ == "__main__":
      snake_case_       =       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_       =       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 torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(EulerAncestralDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for timesteps in [10, 50, 100, 1000]:
                self.check_over_configs(num_train_timesteps=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,     [0.0002, 0.002, 0.02]			):
                self.check_over_configs(beta_start=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Any     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Tuple     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			str     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Dict     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 108.4439			) < 1e-2
          assert abs(result_mean.item() - 0.1412			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps				,     device=lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       torch.manual_seed(0			)

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       sample.to(lowercase__			)

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Dict     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     generator=lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[str]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 152.3192			) < 1e-2
          assert abs(result_mean.item() - 0.1983			) < 1e-3

