

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, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:Tuple						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						SwinConfig(
        embed_dim=1_9_2			,						depths=(2, 2, 1_8, 2)			,						num_heads=(6, 1_2, 2_4, 4_8)			,						window_size=1_2			,						out_features=["stage2", "stage3", "stage4"]			,						)

    UpperCamelCase_			  =						DetaConfig(
        backbone_config=SCREAMING_SNAKE_CASE_			,						num_queries=9_0_0			,						encoder_ffn_dim=2_0_4_8			,						decoder_ffn_dim=2_0_4_8			,						num_feature_levels=5			,						assign_first_stage=SCREAMING_SNAKE_CASE_			,						with_box_refine=SCREAMING_SNAKE_CASE_			,						two_stage=SCREAMING_SNAKE_CASE_			,						)

    # set labels
    UpperCamelCase_			  =						"huggingface/label-files"
    if "o365" in model_name:
        UpperCamelCase_			  =						3_6_6
        UpperCamelCase_			  =						"object365-id2label.json"
    else:
        UpperCamelCase_			  =						9_1
        UpperCamelCase_			  =						"coco-detection-id2label.json"

    UpperCamelCase_			  =						num_labels
    UpperCamelCase_			  =						json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						idalabel
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    # stem
    # fmt: off
    rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight")			)
    rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias")			)
    rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight")			)
    rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias")			)
    # stages
    for i in range(len(config.backbone_config.depths			)			):
        for j in range(config.backbone_config.depths[i]			):
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.norm2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias", f"model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")			)

        if i < 3:
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.reduction.weight", f"model.backbone.model.encoder.layers.{i}.downsample.reduction.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.weight", f"model.backbone.model.encoder.layers.{i}.downsample.norm.weight")			)
            rename_keys.append((f"backbone.0.body.layers.{i}.downsample.norm.bias", f"model.backbone.model.encoder.layers.{i}.downsample.norm.bias")			)

    rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight")			)
    rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias")			)
    rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight")			)
    rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias")			)
    rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight")			)
    rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias")			)

    # 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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						dct.pop(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						val




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						[int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
    for i in range(len(backbone_config.depths			)			):
        UpperCamelCase_			  =						num_features[i]
        for j in range(backbone_config.depths[i]			):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            UpperCamelCase_			  =						state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"			)
            UpperCamelCase_			  =						state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"			)
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase_			  =						in_proj_weight[:dim, :]
            UpperCamelCase_			  =						in_proj_bias[: dim]
            UpperCamelCase_			  =						in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCamelCase_			  =						in_proj_bias[
                dim : dim * 2
            ]
            UpperCamelCase_			  =						in_proj_weight[
                -dim :, :
            ]
            UpperCamelCase_			  =						in_proj_bias[-dim :]
            # fmt: on




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






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




def 			lowerCAmelCase(      )->						Union[str, Any]:



    """simple docstring"""






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

    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						get_deta_config(SCREAMING_SNAKE_CASE_			)

    # load original state dict
    if model_name == "deta-swin-large":
        UpperCamelCase_			  =						hf_hub_download(repo_id="nielsr/deta-checkpoints"			,						filename="adet_swin_ft.pth"			)
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase_			  =						hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"			,						filename="deta_swin_pt_o365.pth"			)
    else:
        raise ValueError(f"Model name {model_name} not supported"			)

    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			,						map_location="cpu"			)["model"]

    # original state dict
    for name, param in state_dict.items():
        print(SCREAMING_SNAKE_CASE_			,						param.shape			)

    # rename keys
    UpperCamelCase_			  =						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_swin_q_k_v(SCREAMING_SNAKE_CASE_			,						config.backbone_config			)
    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:
            UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						val
        if "input_proj" in key:
            UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            UpperCamelCase_			  =						state_dict.pop(SCREAMING_SNAKE_CASE_			)
            UpperCamelCase_			  =						val

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

    UpperCamelCase_			  =						"cuda" if torch.cuda.is_available() else "cpu"
    model.to(SCREAMING_SNAKE_CASE_			)

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

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

    # verify logits
    print("Logits:"			,						outputs.logits[0, :3, :3]			)
    print("Boxes:"			,						outputs.pred_boxes[0, :3, :3]			)
    if model_name == "deta-swin-large":
        UpperCamelCase_			  =						torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]			)
        UpperCamelCase_			  =						torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]			)
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase_			  =						torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]			)
        UpperCamelCase_			  =						torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]			)
    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__":
 SCREAMING_SNAKE_CASE		:Any						=     argparse.ArgumentParser()

 parser.add_argument(
     """--model_name""",
     type=str,
     default="""deta-swin-large""",
     choices=["""deta-swin-large""", """deta-swin-large-o365"""],
     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."""
 )
 SCREAMING_SNAKE_CASE		:Optional[int]						=     parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if exponent == 1:
        return base
    if exponent % 2 == 0:
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						exponent // 2			,						SCREAMING_SNAKE_CASE_			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(SCREAMING_SNAKE_CASE_			,						exponent - 1			,						SCREAMING_SNAKE_CASE_			)) % modulo_value




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_7_7_7			,						SCREAMING_SNAKE_CASE_ = 1_8_5_5			,						SCREAMING_SNAKE_CASE_ = 8			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						base
    for _ in range(1			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						1_0**digits			)

    return result


if __name__ == "__main__":
 print(F'''{solution() = }''')
