





'''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()
_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)


def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''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(SCREAMING_SNAKE_CASE_					:   Union[str, Any]      ):



    '''simple docstring'''





    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():



    '''simple docstring'''





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

    return im


@torch.no_grad()
def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    ):



    '''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(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    repo_type="dataset"      )      )						,    "r"      )      )
    _lowerCAmelCase		     = {int(SCREAMING_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=SCREAMING_SNAKE_CASE_						,    return_tensors="pt"      )
    _lowerCAmelCase		     = encoding["pixel_values"]

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

    # load original state dict
    _lowerCAmelCase		     = torch.load(SCREAMING_SNAKE_CASE_						,    map_location="cpu"      )["model"]
    # rename keys
    for key in state_dict.copy().keys():
        _lowerCAmelCase		     = state_dict.pop(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = val
    # query, key and value matrices need special treatment
    read_in_q_k_v(SCREAMING_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(SCREAMING_SNAKE_CASE_      )
            _lowerCAmelCase		     = val
    # finally, create HuggingFace model and load state dict
    _lowerCAmelCase		     = DeformableDetrForObjectDetection(SCREAMING_SNAKE_CASE_      )
    model.load_state_dict(SCREAMING_SNAKE_CASE_      )
    model.eval()

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

    _lowerCAmelCase		     = torch.tensor(
        [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]      )
    _lowerCAmelCase		     = torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]      )

    if single_scale:
        _lowerCAmelCase		     = torch.tensor(
            [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]      )
        _lowerCAmelCase		     = torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]      )

    if single_scale and dilation:
        _lowerCAmelCase		     = torch.tensor(
            [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]      )
        _lowerCAmelCase		     = torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]      )

    if with_box_refine:
        _lowerCAmelCase		     = torch.tensor(
            [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]      )
        _lowerCAmelCase		     = torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]      )

    if with_box_refine and two_stage:
        _lowerCAmelCase		     = torch.tensor(
            [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]      )
        _lowerCAmelCase		     = torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]      )

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

    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!"      )

    # Save model and image processor
    logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''      )
    Path(SCREAMING_SNAKE_CASE_      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
    model.save_pretrained(SCREAMING_SNAKE_CASE_      )
    image_processor.save_pretrained(SCREAMING_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=SCREAMING_SNAKE_CASE_						,    organization="nielsr"						,    commit_message="Add model"      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE			=							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."
       )
       _SCREAMING_SNAKE_CASE			=							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 math


def 		__a(SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    if number == 0:
        _lowerCAmelCase		     = "Zero"
        return words
    else:
        _lowerCAmelCase		     = math.logaa(SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = digits + 1
        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "One"
        _lowerCAmelCase		     = "Two"
        _lowerCAmelCase		     = "Three"
        _lowerCAmelCase		     = "Four"
        _lowerCAmelCase		     = "Five"
        _lowerCAmelCase		     = "Six"
        _lowerCAmelCase		     = "Seven"
        _lowerCAmelCase		     = "Eight"
        _lowerCAmelCase		     = "Nine"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = "Twenty"
        _lowerCAmelCase		     = "Thirty"
        _lowerCAmelCase		     = "Forty"
        _lowerCAmelCase		     = "Fifty"
        _lowerCAmelCase		     = "Sixty"
        _lowerCAmelCase		     = "Seventy"
        _lowerCAmelCase		     = "Eighty"
        _lowerCAmelCase		     = "Ninety"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Ten"
        _lowerCAmelCase		     = "Eleven"
        _lowerCAmelCase		     = "Twelve"
        _lowerCAmelCase		     = "Thirteen"
        _lowerCAmelCase		     = "Fourteen"
        _lowerCAmelCase		     = "Fifteen"
        _lowerCAmelCase		     = "Sixteen"
        _lowerCAmelCase		     = "Seventeen"
        _lowerCAmelCase		     = "Eighteen"
        _lowerCAmelCase		     = "Nineteen"

        _lowerCAmelCase		     = {}
        _lowerCAmelCase		     = "Hundred,"
        _lowerCAmelCase		     = "Thousand,"
        _lowerCAmelCase		     = "Lakh,"
        _lowerCAmelCase		     = "Crore,"

        _lowerCAmelCase		     = number
        _lowerCAmelCase		     = ""
        _lowerCAmelCase		     = 0
        _lowerCAmelCase		     = int(SCREAMING_SNAKE_CASE_      )
        while counter < digits:
            _lowerCAmelCase		     = temp_num % 10
            if counter % 2 == 0:
                _lowerCAmelCase		     = ""
                if counter in placevalue and current != 0:
                    _lowerCAmelCase		     = placevalue[counter]
                if counter == 2:
                    _lowerCAmelCase		     = singles[current] + addition + words
                elif counter == 0:
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words

                else:
                    _lowerCAmelCase		     = doubles[current] + addition + words

            else:
                if counter == 1:
                    if current == 1:
                        _lowerCAmelCase		     = teens[number % 10] + words
                    else:
                        _lowerCAmelCase		     = ""
                        if counter in placevalue:
                            _lowerCAmelCase		     = placevalue[counter]
                        _lowerCAmelCase		     = doubles[current] + addition + words
                else:
                    _lowerCAmelCase		     = ""
                    if counter in placevalue:
                        if current == 0 and ((temp_num % 100) // 10) == 0:
                            _lowerCAmelCase		     = ""
                        else:
                            _lowerCAmelCase		     = placevalue[counter]
                    if ((temp_num % 100) // 10) == 1:
                        _lowerCAmelCase		     = teens[current] + addition + words
                        _lowerCAmelCase		     = temp_num // 10
                        counter += 1
                    else:
                        _lowerCAmelCase		     = singles[current] + addition + words
            counter += 1
            _lowerCAmelCase		     = temp_num // 10
    return words


if __name__ == "__main__":
       import doctest

       doctest.testmod()


