
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()
__lowerCAmelCase					:			List[Any]			      =		logging.get_logger(__name__)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    __lowercase		:			List[Any]	    =       SwinConfig(
        embed_dim=192	,					depths=(2, 2, 18, 2)	,					num_heads=(6, 12, 24, 48)	,					window_size=12	,					out_features=['''stage2''', '''stage3''', '''stage4''']	,					)

    __lowercase		:			List[Any]	    =       DetaConfig(
        backbone_config=__lowerCAmelCase	,					num_queries=900	,					encoder_ffn_dim=2_048	,					decoder_ffn_dim=2_048	,					num_feature_levels=5	,					assign_first_stage=__lowerCAmelCase	,					with_box_refine=__lowerCAmelCase	,					two_stage=__lowerCAmelCase	,					)

    # set labels
    __lowercase		:			Tuple	    =       '''huggingface/label-files'''
    if "o365" in model_name:
        __lowercase		:			Optional[Any]	    =       366
        __lowercase		:			Optional[int]	    =       '''object365-id2label.json'''
    else:
        __lowercase		:			Any	    =       91
        __lowercase		:			List[Any]	    =       '''coco-detection-id2label.json'''

    __lowercase		:			Optional[int]	    =       num_labels
    __lowercase		:			int	    =       json.load(open(cached_download(hf_hub_url(__lowerCAmelCase	,					__lowerCAmelCase	,					repo_type='''dataset'''	)	)	,					'''r'''	)	)
    __lowercase		:			Union[str, Any]	    =       {int(__lowerCAmelCase	): v for k, v in idalabel.items()}
    __lowercase		:			Dict	    =       idalabel
    __lowercase		:			Dict	    =       {v: k for k, v in idalabel.items()}

    return config


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						str:
    __lowercase		:			List[str]	    =       []

    # 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     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Optional[Any]:
    __lowercase		:			Optional[int]	    =       dct.pop(__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       val


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						str:
    __lowercase		:			List[Any]	    =       [int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
    for i in range(len(backbone_config.depths	)	):
        __lowercase		:			Optional[Any]	    =       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)
            __lowercase		:			Any	    =       state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'	)
            __lowercase		:			Tuple	    =       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
            __lowercase		:			Tuple	    =       in_proj_weight[:dim, :]
            __lowercase		:			Optional[int]	    =       in_proj_bias[: dim]
            __lowercase		:			Dict	    =       in_proj_weight[
                dim : dim * 2, :
            ]
            __lowercase		:			List[str]	    =       in_proj_bias[
                dim : dim * 2
            ]
            __lowercase		:			str	    =       in_proj_weight[
                -dim :, :
            ]
            __lowercase		:			Union[str, Any]	    =       in_proj_bias[-dim :]
            # fmt: on


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    # transformer decoder self-attention layers
    __lowercase		:			Union[str, Any]	    =       config.d_model
    for i in range(config.decoder_layers	):
        # read in weights + bias of input projection layer of self-attention
        __lowercase		:			Optional[int]	    =       state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'	)
        __lowercase		:			List[str]	    =       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		:			int	    =       in_proj_weight[:hidden_size, :]
        __lowercase		:			str	    =       in_proj_bias[:hidden_size]
        __lowercase		:			Optional[Any]	    =       in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        __lowercase		:			Union[str, Any]	    =       in_proj_bias[hidden_size : hidden_size * 2]
        __lowercase		:			Tuple	    =       in_proj_weight[-hidden_size:, :]
        __lowercase		:			int	    =       in_proj_bias[-hidden_size:]


def     UpperCAmelCase_  (						) ->						List[str]:
    __lowercase		:			Optional[int]	    =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __lowercase		:			Union[str, Any]	    =       Image.open(requests.get(__lowerCAmelCase	,					stream=__lowerCAmelCase	).raw	)

    return im


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						Dict:
    __lowercase		:			Dict	    =       get_deta_config(__lowerCAmelCase	)

    # load original state dict
    if model_name == "deta-swin-large":
        __lowercase		:			str	    =       hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,					filename='''adet_swin_ft.pth'''	)
    elif model_name == "deta-swin-large-o365":
        __lowercase		:			int	    =       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'	)

    __lowercase		:			Dict	    =       torch.load(__lowerCAmelCase	,					map_location='''cpu'''	)['''model''']

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

    # rename keys
    __lowercase		:			Any	    =       create_rename_keys(__lowerCAmelCase	)
    for src, dest in rename_keys:
        rename_key(__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)
    read_in_swin_q_k_v(__lowerCAmelCase	,					config.backbone_config	)
    read_in_decoder_q_k_v(__lowerCAmelCase	,					__lowerCAmelCase	)

    # 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		:			str	    =       state_dict.pop(__lowerCAmelCase	)
            __lowercase		:			Dict	    =       val
        if "input_proj" in key:
            __lowercase		:			Union[str, Any]	    =       state_dict.pop(__lowerCAmelCase	)
            __lowercase		:			int	    =       val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            __lowercase		:			int	    =       state_dict.pop(__lowerCAmelCase	)
            __lowercase		:			Optional[Any]	    =       val

    # finally, create HuggingFace model and load state dict
    __lowercase		:			Optional[int]	    =       DetaForObjectDetection(__lowerCAmelCase	)
    model.load_state_dict(__lowerCAmelCase	)
    model.eval()

    __lowercase		:			Any	    =       '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(__lowerCAmelCase	)

    # load image processor
    __lowercase		:			List[Any]	    =       DetaImageProcessor(format='''coco_detection'''	)

    # verify our conversion on image
    __lowercase		:			int	    =       prepare_img()
    __lowercase		:			int	    =       processor(images=__lowerCAmelCase	,					return_tensors='''pt'''	)
    __lowercase		:			List[str]	    =       encoding['''pixel_values''']
    __lowercase		:			List[Any]	    =       model(pixel_values.to(__lowerCAmelCase	)	)

    # verify logits
    print('''Logits:'''	,					outputs.logits[0, :3, :3]	)
    print('''Boxes:'''	,					outputs.pred_boxes[0, :3, :3]	)
    if model_name == "deta-swin-large":
        __lowercase		:			int	    =       torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]	)
        __lowercase		:			Optional[Any]	    =       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":
        __lowercase		:			Optional[Any]	    =       torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]	)
        __lowercase		:			Any	    =       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(__lowerCAmelCase	)	,					atol=1E-4	)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,					expected_boxes.to(__lowerCAmelCase	)	,					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(__lowerCAmelCase	).mkdir(exist_ok=__lowerCAmelCase	)
        model.save_pretrained(__lowerCAmelCase	)
        processor.save_pretrained(__lowerCAmelCase	)

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




from math import factorial

__lowerCAmelCase					:			Dict			      =		{str(d): factorial(d) for d in range(10)}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						) ->						int:
    __lowercase		:			int	    =       7 * factorial(9	) + 1
    return sum(i for i in range(3	,					__lowerCAmelCase	) if sum_of_digit_factorial(__lowerCAmelCase	) == i	)


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


