

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()
UpperCamelCase__	:   Union[str, Any]								=     logging.get_logger(__name__)


def        _UpperCAmelCase							(	):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    DetaConfig(
        num_queries=900			, encoder_ffn_dim=2_048			, decoder_ffn_dim=2_048			, num_feature_levels=5			, assign_first_stage=_SCREAMING_SNAKE_CASE			, with_box_refine=_SCREAMING_SNAKE_CASE			, two_stage=_SCREAMING_SNAKE_CASE			, )

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

    return config


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Tuple							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    []

    # 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        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Dict			, _SCREAMING_SNAKE_CASE       :		Any			, _SCREAMING_SNAKE_CASE       :		str							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    dct.pop(_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    val


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		str			, _SCREAMING_SNAKE_CASE       :		Dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    config.d_model
    for i in range(config.decoder_layers							):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE_						=    state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
        SCREAMING_SNAKE_CASE_						=    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
        SCREAMING_SNAKE_CASE_						=    in_proj_weight[:hidden_size, :]
        SCREAMING_SNAKE_CASE_						=    in_proj_bias[:hidden_size]
        SCREAMING_SNAKE_CASE_						=    in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        SCREAMING_SNAKE_CASE_						=    in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE_						=    in_proj_weight[-hidden_size:, :]
        SCREAMING_SNAKE_CASE_						=    in_proj_bias[-hidden_size:]


def        _UpperCAmelCase							(	):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    'http://images.cocodataset.org/val2017/000000039769.jpg'
    SCREAMING_SNAKE_CASE_						=    Image.open(requests.get(_SCREAMING_SNAKE_CASE			, stream=_SCREAMING_SNAKE_CASE							).raw							)

    return im


@torch.no_grad()
def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Optional[Any]			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]			, _SCREAMING_SNAKE_CASE       :		Union[str, Any]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    get_deta_config()

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

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

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

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

    SCREAMING_SNAKE_CASE_						=    'cuda' if torch.cuda.is_available() else 'cpu'
    model.to(_SCREAMING_SNAKE_CASE							)

    # load image processor
    SCREAMING_SNAKE_CASE_						=    DetaImageProcessor(format='coco_detection'							)

    # verify our conversion on image
    SCREAMING_SNAKE_CASE_						=    prepare_img()
    SCREAMING_SNAKE_CASE_						=    processor(images=_SCREAMING_SNAKE_CASE			, return_tensors='pt'							)
    SCREAMING_SNAKE_CASE_						=    encoding['pixel_values']
    SCREAMING_SNAKE_CASE_						=    model(pixel_values.to(_SCREAMING_SNAKE_CASE							)							)

    # verify logits
    if model_name == "deta-resnet-50":
        SCREAMING_SNAKE_CASE_						=    torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]							)
        SCREAMING_SNAKE_CASE_						=    torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]							)
    elif model_name == "deta-resnet-50-24-epochs":
        SCREAMING_SNAKE_CASE_						=    torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]							)
        SCREAMING_SNAKE_CASE_						=    torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]							)

    assert torch.allclose(outputs.logits[0, :3, :3]			, expected_logits.to(_SCREAMING_SNAKE_CASE							)			, atol=1E-4							)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]			, expected_boxes.to(_SCREAMING_SNAKE_CASE							)			, 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(_SCREAMING_SNAKE_CASE							).mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)
        model.save_pretrained(_SCREAMING_SNAKE_CASE							)
        processor.save_pretrained(_SCREAMING_SNAKE_CASE							)

    # 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__":
       UpperCamelCase__	:   str								=     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."
       )
       UpperCamelCase__	:   Tuple								=     parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    [0] * len(_SCREAMING_SNAKE_CASE							)
    for i in range(1			, len(_SCREAMING_SNAKE_CASE							)							):
        SCREAMING_SNAKE_CASE_						=    duration_times[i - 1] + waiting_times[i - 1]
    return waiting_times


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]			, _SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return [
        duration_time + waiting_times[i]
        for i, duration_time in enumerate(_SCREAMING_SNAKE_CASE							)
    ]


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list[int]							):





    """simple docstring"""


    return sum(_SCREAMING_SNAKE_CASE							) / len(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       # process id's
       UpperCamelCase__	:   List[str]								=     [1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print("Zero amount of processes")
              raise SystemExit(0)

       # duration time of all processes
       UpperCamelCase__	:   Any								=     [19, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print("Unable to match all id's with their duration time")
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       UpperCamelCase__	:   List[str]								=     calculate_waiting_times(duration_times)
       UpperCamelCase__	:   Union[str, Any]								=     calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       UpperCamelCase__	:   str								=     calculate_average_waiting_time(waiting_times)
       UpperCamelCase__	:   List[Any]								=     calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
       for i, process in enumerate(processes):
              print(
                  F'{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t'
                  F'{turnaround_times[i]}'
              )
       print(F'Average waiting time = {average_waiting_time}')
       print(F'Average turn around time = {average_turnaround_time}')


