import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase		:       str						    =	logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_UpperCAmelCase		:       Any						    =	[]
for i in range(6):
					# 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 encoder + 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.encoder.norm.weight""", """encoder.layernorm.weight"""),
        ("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
        ("""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"""),
    ]
)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   state_dict.pop(UpperCamelCase__				)
							snake_case_          =   val





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					snake_case_          =   key.replace('backbone.0.body'       ,							'backbone.conv_encoder.model'				)
																					snake_case_          =   value
														else:
																					snake_case_          =   value

							return new_state_dict





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   ""

							# 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)
														snake_case_          =   state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
														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
														snake_case_          =   in_proj_weight[:256, :]
														snake_case_          =   in_proj_bias[:256]
														snake_case_          =   in_proj_weight[256:512, :]
														snake_case_          =   in_proj_bias[256:512]
														snake_case_          =   in_proj_weight[-256:, :]
														snake_case_          =   in_proj_bias[-256:]
							# 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
														snake_case_          =   state_dict.pop(F'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''				)
														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
														snake_case_          =   in_proj_weight[:256, :]
														snake_case_          =   in_proj_bias[:256]
														snake_case_          =   in_proj_weight[256:512, :]
														snake_case_          =   in_proj_bias[256:512]
														snake_case_          =   in_proj_weight[-256:, :]
														snake_case_          =   in_proj_bias[-256:]
														# read in weights + bias of input projection layer of cross-attention
														snake_case_          =   state_dict.pop(
														    F'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''				)
														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
														snake_case_          =   in_proj_weight_cross_attn[:256, :]
														snake_case_          =   in_proj_bias_cross_attn[:256]
														snake_case_          =   in_proj_weight_cross_attn[256:512, :]
														snake_case_          =   in_proj_bias_cross_attn[256:512]
														snake_case_          =   in_proj_weight_cross_attn[-256:, :]
														snake_case_          =   in_proj_bias_cross_attn[-256:]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   image.size
							snake_case_          =   max(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   800 if "detection" in checkpoint_url else 1000
							snake_case_          =   target_max_size / current_max_size
							snake_case_          =   image.resize((int(round(scale * width				)				), int(round(scale * height				)				))				)

							return resized_image





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   F.to_tensor(UpperCamelCase__				)
							snake_case_          =   F.normalize(UpperCamelCase__       ,							mean=[0.4_85, 0.4_56, 0.4_06]       ,							std=[0.2_29, 0.2_24, 0.2_25]				)
							return image





@torch.no_grad()
def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							logger.info('Converting model...'				)

							# load original state dict
							snake_case_          =   torch.hub.load_state_dict_from_url(UpperCamelCase__       ,							map_location='cpu'				)
							# rename keys
							for src, dest in rename_keys:
														rename_key(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   rename_backbone_keys(UpperCamelCase__				)
							# query, key and value matrices need special treatment
							read_in_q_k_v(UpperCamelCase__				)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							snake_case_          =   "model."
							for key in state_dict.copy().keys():
														if not key.startswith('class_labels_classifier'				) and not key.startswith('bbox_predictor'				):
																					snake_case_          =   state_dict.pop(UpperCamelCase__				)
																					snake_case_          =   val
    # create HuggingFace model and load state dict
							snake_case_          =   TableTransformerConfig(
							    backbone='resnet18'       ,							mask_loss_coefficient=1       ,							dice_loss_coefficient=1       ,							ce_loss_coefficient=1       ,							bbox_loss_coefficient=5       ,							giou_loss_coefficient=2       ,							eos_coefficient=0.4       ,							class_cost=1       ,							bbox_cost=5       ,							giou_cost=2       ,							)

							if "detection" in checkpoint_url:
														snake_case_          =   15
														snake_case_          =   2
														snake_case_          =   {0: "table", 1: "table rotated"}
														snake_case_          =   idalabel
														snake_case_          =   {v: k for k, v in idalabel.items()}
							else:
														snake_case_          =   125
														snake_case_          =   6
														snake_case_          =   {
														    0: "table",
														    1: "table column",
														    2: "table row",
														    3: "table column header",
														    4: "table projected row header",
														    5: "table spanning cell",
														}
														snake_case_          =   idalabel
														snake_case_          =   {v: k for k, v in idalabel.items()}

							snake_case_          =   DetrImageProcessor(
							    format='coco_detection'       ,							max_size=800 if 'detection' in checkpoint_url else 1000				)
							snake_case_          =   TableTransformerForObjectDetection(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)
							model.eval()

							# verify our conversion
							snake_case_          =   "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
							snake_case_          =   hf_hub_download(repo_id='nielsr/example-pdf'       ,							repo_type='dataset'       ,							filename=UpperCamelCase__				)
							snake_case_          =   Image.open(UpperCamelCase__				).convert('RGB'				)
							snake_case_          =   normalize(resize(UpperCamelCase__       ,							UpperCamelCase__				)				).unsqueeze(0				)

							snake_case_          =   model(UpperCamelCase__				)

							if "detection" in checkpoint_url:
														snake_case_          =   (1, 15, 3)
														snake_case_          =   torch.tensor(
														    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]				)
														snake_case_          =   torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]				)

							else:
														snake_case_          =   (1, 125, 7)
														snake_case_          =   torch.tensor(
														    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]				)
														snake_case_          =   torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]				)

							assert outputs.logits.shape == expected_shape
							assert torch.allclose(outputs.logits[0, :3, :3]       ,							UpperCamelCase__       ,							atol=1E-4				)
							assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,							UpperCamelCase__       ,							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(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
														model.save_pretrained(UpperCamelCase__				)
														image_processor.save_pretrained(UpperCamelCase__				)

							if push_to_hub:
														# Push model to HF hub
														logger.info('Pushing model to the hub...'				)
														snake_case_          =   (
														    "microsoft/table-transformer-detection"
														    if "detection" in checkpoint_url
														    else "microsoft/table-transformer-structure-recognition"
														)
														model.push_to_hub(UpperCamelCase__				)
														image_processor.push_to_hub(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
					    type=str,
					    choices=[
					        """https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
					        """https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
					    ],
					    help="""URL of the Table Transformer checkpoint 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."""
					)
					_UpperCAmelCase		:       int						    =	parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







from math import sqrt





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   0
							for i in range(1       ,							int(sqrt(UpperCamelCase__				) + 1				)				):
														if n % i == 0 and i != sqrt(UpperCamelCase__				):
																					total += i + n // i
														elif i == sqrt(UpperCamelCase__				):
																					total += i
							return total - n





def  __lowerCamelCase     (			UpperCamelCase__ = 10000				):







							'''simple docstring'''






							snake_case_          =   sum(
							    i
							    for i in range(1       ,							UpperCamelCase__				)
							    if sum_of_divisors(sum_of_divisors(UpperCamelCase__				)				) == i and sum_of_divisors(UpperCamelCase__				) != i				)
							return total


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




