


"""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()
lowerCamelCase_     :     List[str]    		=						logging.get_logger(__name__)
def _A		(		):







    """simple docstring"""
    a					=DetaConfig(
        num_queries=9_00 ,					encoder_ffn_dim=20_48 ,					decoder_ffn_dim=20_48 ,					num_feature_levels=5 ,					assign_first_stage=lowercase ,					with_box_refine=lowercase ,					two_stage=lowercase ,					)

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

    return config
def _A		(		lowercase	):







    """simple docstring"""
    a					=[]

    # 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 _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=dct.pop(lowercase	)
    a					=val
def _A		(		lowercase ,					lowercase	):







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







    """simple docstring"""
    a					='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    a					=Image.open(requests.get(lowercase ,					stream=lowercase	).raw	)

    return im
@torch.no_grad()
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    a					=get_deta_config()

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

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

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

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

    a					='''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(lowercase	)

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

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

    # verify logits
    if model_name == "deta-resnet-50":
        a					=torch.tensor(
            [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]	)
        a					=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":
        a					=torch.tensor(
            [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]	)
        a					=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(lowercase	) ,					atol=1E-4	)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,					expected_boxes.to(lowercase	) ,					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(lowercase	).mkdir(exist_ok=lowercase	)
        model.save_pretrained(lowercase	)
        processor.save_pretrained(lowercase	)

    # 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_     :     Dict    		=						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."""
       )
       lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_     :     int    		=						logging.get_logger(__name__)

lowerCamelCase_     :     Optional[Any]    		=						{
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 __lowerCAmelCase				      =						"vilt"


 def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
     super().__init__(tie_word_embeddings=__A				,					**__A		)

     a					=vocab_size
     a					=type_vocab_size
     a					=modality_type_vocab_size
     a					=max_position_embeddings

     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=initializer_range
     a					=layer_norm_eps

     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=qkv_bias
     a					=max_image_length
     a					=num_images