
"""simple docstring"""







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 UpperCamelCase_						:     Any				   =    (DDIMScheduler,)
 UpperCamelCase_						:     Dict				   =    (("""eta""", 0.0), ("""num_inference_steps""", 50))




 def   a      ( self       : List[Any]     ,   **a_       : int )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  {
          """num_train_timesteps""": 10_00,
          """beta_start""": 0.0001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
          """clip_sample""": True,
      }

      config.update(**a_ )
      return config




 def   a      ( self       : int     ,   **a_       : Union[str, Any] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    str    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    Union[str, Any]    					=  self.get_scheduler_config(**a_ )
      UpperCAmelCase_						:    Optional[Any]    					=  scheduler_class(**a_ )

      UpperCAmelCase_					,UpperCAmelCase_						:    Any    					=  10, 0.0

      UpperCAmelCase_						:    Any    					=  self.dummy_model()
      UpperCAmelCase_						:    Tuple    					=  self.dummy_sample_deter

      scheduler.set_timesteps(a_ )

      for t in scheduler.timesteps:
           UpperCAmelCase_						:    List[str]    					=  model(a_     ,   a_ )
           UpperCAmelCase_						:    Dict    					=  scheduler.step(a_     ,   a_     ,   a_     ,   a_ ).prev_sample

      return sample




 def   a      ( self       : Union[str, Any] )->					Dict:



      """simple docstring"""

      for timesteps in [1_00, 5_00, 10_00]:
           self.check_over_configs(num_train_timesteps=a_ )




 def   a      ( self       : List[Any] )->					Union[str, Any]:



      """simple docstring"""

      for steps_offset in [0, 1]:
           self.check_over_configs(steps_offset=a_ )

      UpperCAmelCase_						:    int    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    List[Any]    					=  self.get_scheduler_config(steps_offset=1 )
      UpperCAmelCase_						:    Union[str, Any]    					=  scheduler_class(**a_ )
      scheduler.set_timesteps(5 )
      assert torch.equal(scheduler.timesteps     ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )




 def   a      ( self       : List[Any] )->					List[str]:



      """simple docstring"""

      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,   [0.002, 0.02, 0.2, 2] ):
           self.check_over_configs(beta_start=a_     ,   beta_end=a_ )




 def   a      ( self       : Optional[int] )->					List[str]:



      """simple docstring"""

      for schedule in ["linear", "squaredcos_cap_v2"]:
           self.check_over_configs(beta_schedule=a_ )




 def   a      ( self       : List[Any] )->					Optional[Any]:



      """simple docstring"""

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




 def   a      ( self       : Optional[int] )->					List[Any]:



      """simple docstring"""

      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=a_ )




 def   a      ( self       : Dict )->					Any:



      """simple docstring"""

      for timestep_spacing in ["trailing", "leading"]:
           self.check_over_configs(timestep_spacing=a_ )




 def   a      ( self       : Union[str, Any] )->					int:



      """simple docstring"""

      for rescale_betas_zero_snr in [True, False]:
           self.check_over_configs(rescale_betas_zero_snr=a_ )




 def   a      ( self       : List[Any] )->					Any:



      """simple docstring"""

      self.check_over_configs(thresholding=a_ )
      for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=a_     ,   prediction_type=a_     ,   sample_max_value=a_     ,   )




 def   a      ( self       : Dict )->					List[str]:



      """simple docstring"""

      for t in [1, 10, 49]:
           self.check_over_forward(time_step=a_ )




 def   a      ( self       : List[Any] )->					List[str]:



      """simple docstring"""

      for t, num_inference_steps in zip([1, 10, 50]     ,   [10, 50, 5_00] ):
           self.check_over_forward(time_step=a_     ,   num_inference_steps=a_ )




 def   a      ( self       : Any )->					Dict:



      """simple docstring"""

      for t, eta in zip([1, 10, 49]     ,   [0.0, 0.5, 1.0] ):
           self.check_over_forward(time_step=a_     ,   eta=a_ )




 def   a      ( self       : int )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Dict    					=  self.scheduler_classes[0]
      UpperCAmelCase_						:    List[str]    					=  self.get_scheduler_config()
      UpperCAmelCase_						:    Any    					=  scheduler_class(**a_ )

      assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0 ) - 0.0 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_20     ,   4_00 ) - 0.14771 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_80     ,   9_60 ) - 0.32460 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(0     ,   0 ) - 0.0 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87     ,   4_86 ) - 0.00979 ) ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99     ,   9_98 ) - 0.02 ) ) < 1E-5




 def   a      ( self       : Tuple )->					Optional[int]:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.full_loop()

      UpperCAmelCase_						:    Union[str, Any]    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 172.0067 ) < 1E-2
      assert abs(result_mean.item() - 0.223967 ) < 1E-3




 def   a      ( self       : Any )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    int    					=  self.full_loop(prediction_type="""v_prediction""" )

      UpperCAmelCase_						:    Dict    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    List[Any]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 52.5302 ) < 1E-2
      assert abs(result_mean.item() - 0.0684 ) < 1E-3




 def   a      ( self       : Tuple )->					Dict:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  self.full_loop(set_alpha_to_one=a_     ,   beta_start=0.01 )
      UpperCAmelCase_						:    Dict    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    int    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 149.8295 ) < 1E-2
      assert abs(result_mean.item() - 0.1951 ) < 1E-3






 def   a      ( self       : Dict )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Union[str, Any]    					=  self.full_loop(set_alpha_to_one=a_     ,   beta_start=0.01 )
      UpperCAmelCase_						:    Union[str, Any]    					=  torch.sum(torch.abs(a_ ) )
      UpperCAmelCase_						:    Optional[int]    					=  torch.mean(torch.abs(a_ ) )

      assert abs(result_sum.item() - 149.0784 ) < 1E-2
      assert abs(result_mean.item() - 0.1941 ) < 1E-3



"""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 			A_			(					lowercase      )     ->							List[Any]:


     """simple docstring"""







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

     return orig_key




def 			A_			(					lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     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[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
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[:256, :]
          UpperCAmelCase_						:    Union[str, Any]    					=  in_proj_bias[:256]
          UpperCAmelCase_						:    int    					=  in_proj_weight[256:512, :]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_bias[256:512]
          UpperCAmelCase_						:    List[Any]    					=  in_proj_weight[-256:, :]
          UpperCAmelCase_						:    List[str]    					=  in_proj_bias[-256:]




def 			A_			(					)     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    List[str]    					=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     UpperCAmelCase_						:    Union[str, Any]    					=  Image.open(requests.get(lowercase						, stream=lowercase      ).raw      )

     return im




@torch.no_grad()
def 			A_			(					lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, lowercase						, )     ->							List[Any]:


     """simple docstring"""







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

     # load image processor
     UpperCAmelCase_						:    Union[str, Any]    					=  DeformableDetrImageProcessor(format="""coco_detection"""      )

     # prepare image
     UpperCAmelCase_						:    Tuple    					=  prepare_img()
     UpperCAmelCase_						:    Optional[int]    					=  image_processor(images=lowercase						, return_tensors="""pt"""      )
     UpperCAmelCase_						:    List[str]    					=  encoding["""pixel_values"""]

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

     # load original state dict
     UpperCAmelCase_						:    Union[str, Any]    					=  torch.load(lowercase						, map_location="""cpu"""      )["""model"""]
     # rename keys
     for key in state_dict.copy().keys():
          UpperCAmelCase_						:    List[str]    					=  state_dict.pop(lowercase      )
          UpperCAmelCase_						:    Optional[int]    					=  val
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase      )
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     UpperCAmelCase_						:    Optional[int]    					=  """model."""
     for key in state_dict.copy().keys():
          if not key.startswith("""class_embed"""      ) and not key.startswith("""bbox_embed"""      ):
               UpperCAmelCase_						:    Dict    					=  state_dict.pop(lowercase      )
               UpperCAmelCase_						:    Tuple    					=  val
    # finally, create HuggingFace model and load state dict
     UpperCAmelCase_						:    List[Any]    					=  DeformableDetrForObjectDetection(lowercase      )
     model.load_state_dict(lowercase      )
     model.eval()

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

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

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

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

     # Push to hub
     if push_to_hub:
          UpperCAmelCase_						:    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=lowercase						, 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,
       )

