





"""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_				=    logging.get_logger(__name__)







def 	__lowercase  (     snake_case_						:						Dict		)     ->List[Any]:



    '''simple docstring'''







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

    return orig_key







def 	__lowercase  (     snake_case_						:						List[Any]		)     ->Union[str, Any]:



    '''simple docstring'''







    for i in range(6		):
        # read in weights + bias of input projection layer of self-attention
        __A					:		Optional[Any]						=  state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""		)
        __A					:		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
        __A					:		Union[str, Any]						=  in_proj_weight[:256, :]
        __A					:		Union[str, Any]						=  in_proj_bias[:256]
        __A					:		Optional[int]						=  in_proj_weight[256:512, :]
        __A					:		Optional[Any]						=  in_proj_bias[256:512]
        __A					:		Tuple						=  in_proj_weight[-256:, :]
        __A					:		Union[str, Any]						=  in_proj_bias[-256:]







def 	__lowercase  (     )     ->List[str]:



    '''simple docstring'''







    __A					:		Tuple						=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A					:		List[Any]						=  Image.open(requests.get(snake_case_       ,stream=snake_case_		).raw		)

    return im







@torch.no_grad()
def 	__lowercase  (     snake_case_						:						Tuple       ,snake_case_						:						Any       ,snake_case_						:						Any       ,snake_case_						:						Tuple       ,snake_case_						:						List[Any]       ,snake_case_						:						List[Any]       ,snake_case_						:						str       ,)     ->Optional[int]:



    '''simple docstring'''







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

    # load image processor
    __A					:		Tuple						=  DeformableDetrImageProcessor(format='''coco_detection'''		)

    # prepare image
    __A					:		Tuple						=  prepare_img()
    __A					:		Any						=  image_processor(images=snake_case_       ,return_tensors='''pt'''		)
    __A					:		Union[str, Any]						=  encoding['''pixel_values''']

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

    # load original state dict
    __A					:		Optional[Any]						=  torch.load(snake_case_       ,map_location='''cpu'''		)['''model''']
    # rename keys
    for key in state_dict.copy().keys():
        __A					:		int						=  state_dict.pop(snake_case_		)
        __A					:		Tuple						=  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
    __A					:		List[str]						=  '''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith('''class_embed'''		) and not key.startswith('''bbox_embed'''		):
            __A					:		int						=  state_dict.pop(snake_case_		)
            __A					:		Tuple						=  val
    # finally, create HuggingFace model and load state dict
    __A					:		int						=  DeformableDetrForObjectDetection(snake_case_		)
    model.load_state_dict(snake_case_		)
    model.eval()

    __A					:		Optional[Any]						=  '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(snake_case_		)
    # verify our conversion
    __A					:		Optional[Any]						=  model(pixel_values.to(snake_case_		)		)

    __A					:		Tuple						=  torch.tensor(
        [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]		)
    __A					:		Dict						=  torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]		)

    if single_scale:
        __A					:		List[Any]						=  torch.tensor(
            [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]		)
        __A					:		List[Any]						=  torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]		)

    if single_scale and dilation:
        __A					:		Union[str, Any]						=  torch.tensor(
            [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]		)
        __A					:		Dict						=  torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]		)

    if with_box_refine:
        __A					:		Union[str, Any]						=  torch.tensor(
            [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]		)
        __A					:		int						=  torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]		)

    if with_box_refine and two_stage:
        __A					:		Dict						=  torch.tensor(
            [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]		)
        __A					:		Dict						=  torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]		)

    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:
        __A					:		str						=  '''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_				=    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_				=    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"""



from __future__ import annotations

import sys







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						int		)     ->tuple[int, int]:



    '''simple docstring'''







    if abs(snake_case_		) == 1:
        return a, 0
    elif abs(snake_case_		) == 1:
        return 0, b

    __A				, __A					:		int						=  a, b
    __A				, __A					:		Any						=  1, 0
    __A				, __A					:		int						=  0, 1

    while remainder != 0:
        __A					:		List[Any]						=  old_remainder // remainder
        __A				, __A					:		int						=  remainder, old_remainder - quotient * remainder
        __A				, __A					:		List[Any]						=  coeff_a, old_coeff_a - quotient * coeff_a
        __A				, __A					:		Union[str, Any]						=  coeff_b, old_coeff_b - quotient * coeff_b

    # sign correction for negative numbers
    if a < 0:
        __A					:		Optional[Any]						=  -old_coeff_a
    if b < 0:
        __A					:		List[Any]						=  -old_coeff_b

    return old_coeff_a, old_coeff_b







def 	__lowercase  (     )     ->List[str]:



    '''simple docstring'''







    if len(sys.argv		) < 3:
        print('''2 integer arguments required'''		)
        return 1
    __A					:		Optional[int]						=  int(sys.argv[1]		)
    __A					:		Tuple						=  int(sys.argv[2]		)
    print(extended_euclidean_algorithm(snake_case_       ,snake_case_		)		)
    return 0


if __name__ == "__main__":
       raise SystemExit(main())




