




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							=      logging.get_logger(__name__)





def 							lowerCamelCase    (							UpperCAmelCase_	:      Union[str, Any]      )->			Union[str, 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 							lowerCamelCase    (							UpperCAmelCase_	:      str      )->			Optional[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 							lowerCamelCase    (							)->			str:

       """simple docstring"""




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

       return im





@torch.no_grad()
def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      Union[str, Any]   ,       UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      Dict   ,       )->			Optional[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(UpperCAmelCase_   ,       UpperCAmelCase_   ,       repo_type="""dataset"""      )      )   ,       """r"""      )      )
       a			   ={int(UpperCAmelCase_      ): 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=UpperCAmelCase_   ,       return_tensors="""pt"""      )
       a			   =encoding["""pixel_values"""]

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

       # load original state dict
       a			   =torch.load(UpperCAmelCase_   ,       map_location="""cpu"""      )["""model"""]
       # rename keys
       for key in state_dict.copy().keys():
              a			   =state_dict.pop(UpperCAmelCase_      )
              a			   =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
       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(UpperCAmelCase_      )
                     a			   =val
    # finally, create HuggingFace model and load state dict
       a			   =DeformableDetrForObjectDetection(UpperCAmelCase_      )
       model.load_state_dict(UpperCAmelCase_      )
       model.eval()

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

       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(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:
              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=UpperCAmelCase_   ,       organization="""nielsr"""   ,       commit_message="""Add model"""      )


if __name__ == "__main__":
    _lowerCamelCase							=      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							=      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,
    )






import math
from timeit import timeit





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       a			   =0
       a			   =abs(UpperCAmelCase_      )
       while True:
              a			   =n // 10
              digits += 1
              if n == 0:
                     break
       return digits





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       return 1 if n == 0 else math.floor(math.log(abs(UpperCAmelCase_      )   ,       10      ) + 1      )





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       return len(str(abs(UpperCAmelCase_      )      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       from collections.abc import Callable

       def benchmark_a_function(UpperCAmelCase_	:      Callable   ,       UpperCAmelCase_	:      int      ) -> None:
              a			   =F'''{func.__name__}({value})'''
              a			   =timeit(F'''__main__.{call}'''   ,       setup="""import __main__"""      )
              print(F'''{call}: {func(UpperCAmelCase_      )} -- {timing} seconds'''      )

       for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
              for func in (num_digits, num_digits_fast, num_digits_faster):
                     benchmark_a_function(UpperCAmelCase_   ,       UpperCAmelCase_      )
              print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
