





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_A					:				List[str]				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    # initialize config
    if "resnet-50" in model_name:
        SCREAMING_SNAKE_CASE__											=			ResNetConfig.from_pretrained('''microsoft/resnet-50'''	)
    elif "resnet-101" in model_name:
        SCREAMING_SNAKE_CASE__											=			ResNetConfig.from_pretrained('''microsoft/resnet-101'''	)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''	)

    SCREAMING_SNAKE_CASE__											=			DetrConfig(use_timm_backbone=lowerCAmelCase_ ,							backbone_config=lowerCAmelCase_	)

    # set label attributes
    SCREAMING_SNAKE_CASE__											=			'''panoptic''' in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE__											=			2_5_0
    else:
        SCREAMING_SNAKE_CASE__											=			9_1
        SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
        SCREAMING_SNAKE_CASE__											=			'''coco-detection-id2label.json'''
        SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
        SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    return config, is_panoptic


def 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    SCREAMING_SNAKE_CASE__											=			[]

    # stem
    # fmt: off
    rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')	)
    rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')	)
    rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')	)
    rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')	)
    rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.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'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
                    )	)
    # fmt: on

    for i in range(config.encoder_layers	):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
                f'''encoder.layers.{i}.self_attn.out_proj.weight''',
            )	)
        rename_keys.append(
            (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')	)
        rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight''')	)
        rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias''')	)
        rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight''')	)
        rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias''')	)
        rename_keys.append(
            (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')	)
        rename_keys.append(
            (f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias''')	)
        rename_keys.append(
            (f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight''')	)
        rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias''')	)
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
                f'''decoder.layers.{i}.self_attn.out_proj.weight''',
            )	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')	)
        rename_keys.append(
            (
                f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
                f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
            )	)
        rename_keys.append(
            (
                f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
                f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
            )	)
        rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight''')	)
        rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias''')	)
        rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight''')	)
        rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias''')	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias''')	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')	)
        rename_keys.append(
            (f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight''')	)
        rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias''')	)

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('''input_proj.weight''', '''input_projection.weight'''),
            ('''input_proj.bias''', '''input_projection.bias'''),
            ('''query_embed.weight''', '''query_position_embeddings.weight'''),
            ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
            ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
            ('''class_embed.weight''', '''class_labels_classifier.weight'''),
            ('''class_embed.bias''', '''class_labels_classifier.bias'''),
            ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
            ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
            ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
            ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
            ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
            ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ]	)

    return rename_keys


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			''''''
    if is_panoptic:
        SCREAMING_SNAKE_CASE__											=			'''detr.'''

    # first: transformer encoder
    for i in range(6	):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''{prefix}transformer.encoder.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[:2_5_6, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:2_5_6]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[2_5_6:5_1_2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[2_5_6:5_1_2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-2_5_6:, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6	):
        # read in weights + bias of input projection layer of self-attention
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''{prefix}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[:2_5_6, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:2_5_6]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[2_5_6:5_1_2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[2_5_6:5_1_2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-2_5_6:, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(
            f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'''	)
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        SCREAMING_SNAKE_CASE__											=			in_proj_weight_cross_attn[:2_5_6, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias_cross_attn[:2_5_6]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias_cross_attn[2_5_6:5_1_2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight_cross_attn[-2_5_6:, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias_cross_attn[-2_5_6:]


def 							__snake_case							(    )				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)

    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=False	)				->						Any:
    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			get_detr_config(lowerCAmelCase_	)

    # load original model from torch hub
    SCREAMING_SNAKE_CASE__											=			{
        '''detr-resnet-50''': '''detr_resnet50''',
        '''detr-resnet-101''': '''detr_resnet101''',
    }
    logger.info(f'''Converting model {model_name}...'''	)
    SCREAMING_SNAKE_CASE__											=			torch.hub.load('''facebookresearch/detr''' ,							model_name_to_original_name[model_name] ,							pretrained=lowerCAmelCase_	).eval()
    SCREAMING_SNAKE_CASE__											=			detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(lowerCAmelCase_	):
        if is_panoptic:
            SCREAMING_SNAKE_CASE__											=			'''detr.''' + src
        rename_key(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
    # query, key and value matrices need special treatment
    read_in_q_k_v(lowerCAmelCase_ ,							is_panoptic=lowerCAmelCase_	)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    SCREAMING_SNAKE_CASE__											=			'''detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''detr'''	)
                and not key.startswith('''class_labels_classifier'''	)
                and not key.startswith('''bbox_predictor'''	)
            ):
                SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
                SCREAMING_SNAKE_CASE__											=			val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
                SCREAMING_SNAKE_CASE__											=			val
            elif key.startswith('''bbox_attention'''	) or key.startswith('''mask_head'''	):
                continue
            else:
                SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
                SCREAMING_SNAKE_CASE__											=			val
        else:
            if not key.startswith('''class_labels_classifier'''	) and not key.startswith('''bbox_predictor'''	):
                SCREAMING_SNAKE_CASE__											=			state_dict.pop(lowerCAmelCase_	)
                SCREAMING_SNAKE_CASE__											=			val

    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE__											=			DetrForSegmentation(lowerCAmelCase_	) if is_panoptic else DetrForObjectDetection(lowerCAmelCase_	)
    model.load_state_dict(lowerCAmelCase_	)
    model.eval()

    # verify our conversion on an image
    SCREAMING_SNAKE_CASE__											=			'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    SCREAMING_SNAKE_CASE__											=			DetrImageProcessor(format=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			encoding['''pixel_values''']

    SCREAMING_SNAKE_CASE__											=			detr(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			model(lowerCAmelCase_	)

    assert torch.allclose(outputs.logits ,							original_outputs['''pred_logits'''] ,							atol=1e-3	)
    assert torch.allclose(outputs.pred_boxes ,							original_outputs['''pred_boxes'''] ,							atol=1e-3	)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks ,							original_outputs['''pred_masks'''] ,							atol=1e-4	)
    print('''Looks ok!'''	)

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
        Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
        model.save_pretrained(lowerCAmelCase_	)
        processor.save_pretrained(lowerCAmelCase_	)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('''Uploading PyTorch model and image processor to the hub...'''	)
        model.push_to_hub(f'''nielsr/{model_name}'''	)
        processor.push_to_hub(f'''nielsr/{model_name}'''	)


if __name__ == "__main__":
  _A					:				List[Any]				=			argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      default="""detr-resnet-50""",
      type=str,
      choices=["""detr-resnet-50""", """detr-resnet-101"""],
      help="""Name of the DETR 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 to push the model to the hub or not.""")
  _A					:				Any				=			parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







def 							__snake_case							(    )				->						int:
    return 1


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else two_pence(x - 2	) + one_pence()


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else five_pence(x - 5	) + two_pence(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else ten_pence(x - 1_0	) + five_pence(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else twenty_pence(x - 2_0	) + ten_pence(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else fifty_pence(x - 5_0	) + twenty_pence(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else one_pound(x - 1_0_0	) + fifty_pence(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    return 0 if x < 0 else two_pound(x - 2_0_0	) + one_pound(lowerCAmelCase_	)


def 							__snake_case							(    lowerCAmelCase_ = 2_0_0	)				->						int:
    return two_pound(lowerCAmelCase_	)


if __name__ == "__main__":
  print(solution(int(input().strip())))
