




'''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()
a_   :	Optional[Any]							   = logging.get_logger(__name__)





def   a_							(   __snake_case							:    Dict     )							->		Tuple:



       """simple docstring"""
       if "backbone.0.body" in orig_key:
              lowerCamelCase_							=orig_key.replace('''backbone.0.body'''     , '''backbone.conv_encoder.model'''     )
       if "transformer" in orig_key:
              lowerCamelCase_							=orig_key.replace('''transformer.'''     , ''''''     )
       if "norm1" in orig_key:
              if "encoder" in orig_key:
                     lowerCamelCase_							=orig_key.replace('''norm1'''     , '''self_attn_layer_norm'''     )
              else:
                     lowerCamelCase_							=orig_key.replace('''norm1'''     , '''encoder_attn_layer_norm'''     )
       if "norm2" in orig_key:
              if "encoder" in orig_key:
                     lowerCamelCase_							=orig_key.replace('''norm2'''     , '''final_layer_norm'''     )
              else:
                     lowerCamelCase_							=orig_key.replace('''norm2'''     , '''self_attn_layer_norm'''     )
       if "norm3" in orig_key:
              lowerCamelCase_							=orig_key.replace('''norm3'''     , '''final_layer_norm'''     )
       if "linear1" in orig_key:
              lowerCamelCase_							=orig_key.replace('''linear1'''     , '''fc1'''     )
       if "linear2" in orig_key:
              lowerCamelCase_							=orig_key.replace('''linear2'''     , '''fc2'''     )
       if "query_embed" in orig_key:
              lowerCamelCase_							=orig_key.replace('''query_embed'''     , '''query_position_embeddings'''     )
       if "cross_attn" in orig_key:
              lowerCamelCase_							=orig_key.replace('''cross_attn'''     , '''encoder_attn'''     )

       return orig_key





def   a_							(   __snake_case							:    List[str]     )							->		Tuple:



       """simple docstring"""
       # transformer decoder self-attention layers
       for i in range(6     ):
              # read in weights + bias of input projection layer of self-attention
              lowerCamelCase_							=state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''     )
              lowerCamelCase_							=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
              lowerCamelCase_							=in_proj_weight[:256, :]
              lowerCamelCase_							=in_proj_bias[:256]
              lowerCamelCase_							=in_proj_weight[256:512, :]
              lowerCamelCase_							=in_proj_bias[256:512]
              lowerCamelCase_							=in_proj_weight[-256:, :]
              lowerCamelCase_							=in_proj_bias[-256:]





def   a_							(   )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							='''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowerCamelCase_							=Image.open(requests.get(__snake_case     , stream=__snake_case     ).raw     )

       return im





@torch.no_grad()
def   a_							(   __snake_case							:    Optional[Any]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    List[Any]     , __snake_case							:    List[Any]     , __snake_case							:    Dict     , __snake_case							:    str     , )							->		str:



       """simple docstring"""
       lowerCamelCase_							=DeformableDetrConfig()
       # set config attributes
       if single_scale:
              lowerCamelCase_							=1
       lowerCamelCase_							=dilation
       lowerCamelCase_							=with_box_refine
       lowerCamelCase_							=two_stage
       # set labels
       lowerCamelCase_							=91
       lowerCamelCase_							='''huggingface/label-files'''
       lowerCamelCase_							='''coco-detection-id2label.json'''
       lowerCamelCase_							=json.load(open(cached_download(hf_hub_url(__snake_case     , __snake_case     , repo_type='''dataset'''     )     )     , '''r'''     )     )
       lowerCamelCase_							={int(__snake_case     ): v for k, v in idalabel.items()}
       lowerCamelCase_							=idalabel
       lowerCamelCase_							={v: k for k, v in idalabel.items()}

       # load image processor
       lowerCamelCase_							=DeformableDetrImageProcessor(format='''coco_detection'''     )

       # prepare image
       lowerCamelCase_							=prepare_img()
       lowerCamelCase_							=image_processor(images=__snake_case     , return_tensors='''pt'''     )
       lowerCamelCase_							=encoding['''pixel_values''']

       logger.info('''Converting model...'''     )

       # load original state dict
       lowerCamelCase_							=torch.load(__snake_case     , map_location='''cpu'''     )['''model''']
       # rename keys
       for key in state_dict.copy().keys():
              lowerCamelCase_							=state_dict.pop(__snake_case     )
              lowerCamelCase_							=val
       # query, key and value matrices need special treatment
       read_in_q_k_v(__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
       lowerCamelCase_							='''model.'''
       for key in state_dict.copy().keys():
              if not key.startswith('''class_embed'''     ) and not key.startswith('''bbox_embed'''     ):
                     lowerCamelCase_							=state_dict.pop(__snake_case     )
                     lowerCamelCase_							=val
    # finally, create HuggingFace model and load state dict
       lowerCamelCase_							=DeformableDetrForObjectDetection(__snake_case     )
       model.load_state_dict(__snake_case     )
       model.eval()

       lowerCamelCase_							='''cuda''' if torch.cuda.is_available() else '''cpu'''
       model.to(__snake_case     )
       # verify our conversion
       lowerCamelCase_							=model(pixel_values.to(__snake_case     )     )

       lowerCamelCase_							=torch.tensor(
           [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]     )
       lowerCamelCase_							=torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]     )

       if single_scale:
              lowerCamelCase_							=torch.tensor(
                  [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]     )
              lowerCamelCase_							=torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]     )

       if single_scale and dilation:
              lowerCamelCase_							=torch.tensor(
                  [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]     )
              lowerCamelCase_							=torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]     )

       if with_box_refine:
              lowerCamelCase_							=torch.tensor(
                  [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]     )
              lowerCamelCase_							=torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]     )

       if with_box_refine and two_stage:
              lowerCamelCase_							=torch.tensor(
                  [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]     )
              lowerCamelCase_							=torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]     )

       print('''Logits:'''     , outputs.logits[0, :3, :3]     )

       assert torch.allclose(outputs.logits[0, :3, :3]     , expected_logits.to(__snake_case     )     , atol=1e-4     )
       assert torch.allclose(outputs.pred_boxes[0, :3, :3]     , expected_boxes.to(__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(__snake_case     ).mkdir(exist_ok=__snake_case     )
       model.save_pretrained(__snake_case     )
       image_processor.save_pretrained(__snake_case     )

       # Push to hub
       if push_to_hub:
              lowerCamelCase_							='''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=__snake_case     , organization='''nielsr'''     , commit_message='''Add model'''     )


if __name__ == "__main__":
     a_   :	List[Any]							   = 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."""
     )
     a_   :	int							   = 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 HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Union[str, Any]   		=(HeunDiscreteScheduler,)
       lowercase			:					int   		=10






       def   lowercase__  (							self,	**lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_0_0_1,
                  '''beta_end''': 0.0_2,
                  '''beta_schedule''': '''linear''',
              }

              config.update(**lowerCAmelCase						)
              return config






       def   lowercase__  (							self						):



              """simple docstring"""







              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],	[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
                     self.check_over_configs(beta_start=lowerCAmelCase,	beta_end=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              for clip_sample_range in [1.0, 2.0, 3.0]:
                     self.check_over_configs(clip_sample_range=lowerCAmelCase,	clip_sample=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config(prediction_type='''v_prediction'''						)
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for i, t in enumerate(scheduler.timesteps						):
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if torch_device in ["cpu", "mps"]:
                     assert abs(result_sum.item() - 4.6_934e-07						) < 1e-2
                     assert abs(result_mean.item() - 6.1_112e-10						) < 1e-3
              else:
                     # CUDA
                     assert abs(result_sum.item() - 4.693_428_650_170_972e-07						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              if str(lowerCAmelCase						).startswith('''cpu'''						):
                     # The following sum varies between 148 and 156 on mps. Why?
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3
              elif str(lowerCAmelCase						).startswith('''mps'''						):
                     # Larger tolerance on mps
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-2
              else:
                     # CUDA
                     assert abs(result_sum.item() - 0.1_2_3_3						) < 1e-2
                     assert abs(result_mean.item() - 0.0_0_0_2						) < 1e-3






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.scheduler_classes[0]
              lowerCamelCase_							=self.get_scheduler_config()
              lowerCamelCase_							=scheduler_class(**lowerCAmelCase,	use_karras_sigmas=lowerCAmelCase						)

              scheduler.set_timesteps(self.num_inference_steps,	device=lowerCAmelCase						)

              lowerCamelCase_							=self.dummy_model()
              lowerCamelCase_							=self.dummy_sample_deter.to(lowerCAmelCase						) * scheduler.init_noise_sigma
              lowerCamelCase_							=sample.to(lowerCAmelCase						)

              for t in scheduler.timesteps:
                     lowerCamelCase_							=scheduler.scale_model_input(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=model(lowerCAmelCase,	lowerCAmelCase						)

                     lowerCamelCase_							=scheduler.step(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)
                     lowerCamelCase_							=output.prev_sample

              lowerCamelCase_							=torch.sum(torch.abs(lowerCAmelCase						)						)
              lowerCamelCase_							=torch.mean(torch.abs(lowerCAmelCase						)						)

              assert abs(result_sum.item() - 0.0_0_0_1_5						) < 1e-2
              assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07						) < 1e-2




