




'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A							:				Optional[Any]			     =	logging.get_logger(__name__)
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						DetaConfig(
        num_queries=9_00   ,encoder_ffn_dim=20_48   ,decoder_ffn_dim=20_48   ,num_feature_levels=5   ,assign_first_stage=a__   ,with_box_refine=a__   ,two_stage=a__   ,)

    # set labels
    __lowercase          =						91
    __lowercase          =						"""huggingface/label-files"""
    __lowercase          =						"""coco-detection-id2label.json"""
    __lowercase          =						json.load(open(cached_download(hf_hub_url(a__   ,a__   ,repo_type="""dataset"""      )      )   ,"""r"""      )      )
    __lowercase          =						{int(a__      ): v for k, v in idalabel.items()}
    __lowercase          =						idalabel
    __lowercase          =						{v: k for k, v in idalabel.items()}

    return config
def     snake_case_  (       a__		:     List[str]      ):



    """simple docstring"""




    __lowercase          =						[]

    # stem
    # fmt: off
    rename_keys.append(("""backbone.0.body.conv1.weight""", """model.backbone.model.embedder.embedder.convolution.weight""")      )
    rename_keys.append(("""backbone.0.body.bn1.weight""", """model.backbone.model.embedder.embedder.normalization.weight""")      )
    rename_keys.append(("""backbone.0.body.bn1.bias""", """model.backbone.model.embedder.embedder.normalization.bias""")      )
    rename_keys.append(("""backbone.0.body.bn1.running_mean""", """model.backbone.model.embedder.embedder.normalization.running_mean""")      )
    rename_keys.append(("""backbone.0.body.bn1.running_var""", """model.backbone.model.embedder.embedder.normalization.running_var""")      )
    # stages
    for stage_idx in range(len(config.backbone_config.depths      )      ):
        for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                    )      )
            # 3 convs
            for i in range(3      ):
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                    )      )
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                        f'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                    )      )
    # transformer encoder
    for i in range(config.encoder_layers      ):
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', f'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', f'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', f'model.encoder.layers.{i}.self_attn.attention_weights.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', f'model.encoder.layers.{i}.self_attn.attention_weights.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.weight', f'model.encoder.layers.{i}.self_attn.value_proj.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.value_proj.bias', f'model.encoder.layers.{i}.self_attn.value_proj.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.weight', f'model.encoder.layers.{i}.self_attn.output_proj.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.self_attn.output_proj.bias', f'model.encoder.layers.{i}.self_attn.output_proj.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.norm1.weight', f'model.encoder.layers.{i}.self_attn_layer_norm.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'model.encoder.layers.{i}.self_attn_layer_norm.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'model.encoder.layers.{i}.fc1.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'model.encoder.layers.{i}.fc1.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'model.encoder.layers.{i}.fc2.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'model.encoder.layers.{i}.fc2.bias')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'model.encoder.layers.{i}.final_layer_norm.weight')      )
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'model.encoder.layers.{i}.final_layer_norm.bias')      )

    # transformer decoder
    for i in range(config.decoder_layers      ):
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', f'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', f'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', f'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', f'model.decoder.layers.{i}.encoder_attn.value_proj.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', f'model.decoder.layers.{i}.encoder_attn.value_proj.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', f'model.decoder.layers.{i}.encoder_attn.output_proj.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', f'model.decoder.layers.{i}.encoder_attn.output_proj.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm1.weight', f'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'model.decoder.layers.{i}.self_attn.out_proj.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'model.decoder.layers.{i}.self_attn.out_proj.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm2.weight', f'model.decoder.layers.{i}.self_attn_layer_norm.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm2.bias', f'model.decoder.layers.{i}.self_attn_layer_norm.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'model.decoder.layers.{i}.fc1.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'model.decoder.layers.{i}.fc1.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'model.decoder.layers.{i}.fc2.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'model.decoder.layers.{i}.fc2.bias')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'model.decoder.layers.{i}.final_layer_norm.weight')      )
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'model.decoder.layers.{i}.final_layer_norm.bias')      )

    # fmt: on

    return rename_keys
def     snake_case_  (       a__		:     str   ,a__		:     Tuple   ,a__		:     Optional[Any]      ):



    """simple docstring"""




    __lowercase          =						dct.pop(a__      )
    __lowercase          =						val
def     snake_case_  (       a__		:     Union[str, Any]   ,a__		:     Any      ):



    """simple docstring"""




    __lowercase          =						config.d_model
    for i in range(config.decoder_layers      ):
        # read in weights + bias of input projection layer of self-attention
        __lowercase          =						state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_weight'      )
        __lowercase          =						state_dict.pop(f'transformer.decoder.layers.{i}.self_attn.in_proj_bias'      )
        # next, add query, keys and values (in that order) to the state dict
        __lowercase          =						in_proj_weight[:hidden_size, :]
        __lowercase          =						in_proj_bias[:hidden_size]
        __lowercase          =						in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        __lowercase          =						in_proj_bias[hidden_size : hidden_size * 2]
        __lowercase          =						in_proj_weight[-hidden_size:, :]
        __lowercase          =						in_proj_bias[-hidden_size:]
def     snake_case_  (       ):



    """simple docstring"""




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

    return im
@torch.no_grad()
def     snake_case_  (       a__		:     Any   ,a__		:     Tuple   ,a__		:     Tuple      ):



    """simple docstring"""




    __lowercase          =						get_deta_config()

    # load original state dict
    if model_name == "deta-resnet-50":
        __lowercase          =						"""adet_checkpoint0011.pth"""
    elif model_name == "deta-resnet-50-24-epochs":
        __lowercase          =						"""adet_2x_checkpoint0023.pth"""
    else:
        raise ValueError(f'Model name {model_name} not supported'      )
    __lowercase          =						hf_hub_download(repo_id="""nielsr/deta-checkpoints"""   ,filename=a__      )
    __lowercase          =						torch.load(a__   ,map_location="""cpu"""      )["""model"""]

    # rename keys
    __lowercase          =						create_rename_keys(a__      )
    for src, dest in rename_keys:
        rename_key(a__   ,a__   ,a__      )
    read_in_decoder_q_k_v(a__   ,a__      )

    # fix some prefixes
    for key in state_dict.copy().keys():
        if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
            __lowercase          =						state_dict.pop(a__      )
            __lowercase          =						val
        if "input_proj" in key:
            __lowercase          =						state_dict.pop(a__      )
            __lowercase          =						val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            __lowercase          =						state_dict.pop(a__      )
            __lowercase          =						val

    # finally, create HuggingFace model and load state dict
    __lowercase          =						DetaForObjectDetection(a__      )
    model.load_state_dict(a__      )
    model.eval()

    __lowercase          =						"""cuda""" if torch.cuda.is_available() else """cpu"""
    model.to(a__      )

    # load image processor
    __lowercase          =						DetaImageProcessor(format="""coco_detection"""      )

    # verify our conversion on image
    __lowercase          =						prepare_img()
    __lowercase          =						processor(images=a__   ,return_tensors="""pt"""      )
    __lowercase          =						encoding["""pixel_values"""]
    __lowercase          =						model(pixel_values.to(a__      )      )

    # verify logits
    if model_name == "deta-resnet-50":
        __lowercase          =						torch.tensor(
            [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]      )
        __lowercase          =						torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]      )
    elif model_name == "deta-resnet-50-24-epochs":
        __lowercase          =						torch.tensor(
            [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]      )
        __lowercase          =						torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]      )

    assert torch.allclose(outputs.logits[0, :3, :3]   ,expected_logits.to(a__      )   ,atol=1e-4      )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]   ,expected_boxes.to(a__      )   ,atol=1e-4      )
    print("""Everything ok!"""      )

    if pytorch_dump_folder_path:
        # Save model and processor
        logger.info(f'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'      )
        Path(a__      ).mkdir(exist_ok=a__      )
        model.save_pretrained(a__      )
        processor.save_pretrained(a__      )

    # Push to hub
    if push_to_hub:
        print("""Pushing model and processor to hub..."""      )
        model.push_to_hub(f'jozhang97/{model_name}'      )
        processor.push_to_hub(f'jozhang97/{model_name}'      )


if __name__ == "__main__":
  A							:				Optional[int]			     =	argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      type=str,
      default="""deta-resnet-50""",
      choices=["""deta-resnet-50""", """deta-resnet-50-24-epochs"""],
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      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							:				Union[str, Any]			     =	parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''
A							:				int			     =	(1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
A							:				tuple			     =	(-153, -1, 0, 1.2, 200, "A", [], {}, None)
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    __lowercase          =						n
    while temp > 0:
        __lowercase          =						temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Init a "histogram" of the digits
    __lowercase          =						[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    while temp > 0:
        __lowercase    ,__lowercase          =						divmod(a__   ,10      )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(a__   ,range(len(a__      )      )      ):
        total += cnt * i**digit_total

    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False
    __lowercase          =						len(str(a__      )      )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(a__      ) ** expo for i in str(a__      )      )
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						int(input("""Enter an integer to see if it is an Armstrong number: """      ).strip()      )
    print(f'{num} is {"" if armstrong_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if narcissistic_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if pluperfect_number(a__      ) else "not "}an Armstrong number.'      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




