


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()
snake_case_						:			Optional[Any]													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__):
				# 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=a__	,backbone_config=a__)

				# set label attributes
				_SCREAMING_SNAKE_CASE			  ='''panoptic''' in model_name
				if is_panoptic:
								_SCREAMING_SNAKE_CASE			  =250
				else:
								_SCREAMING_SNAKE_CASE			  =91
								_SCREAMING_SNAKE_CASE			  ='''huggingface/label-files'''
								_SCREAMING_SNAKE_CASE			  ='''coco-detection-id2label.json'''
								_SCREAMING_SNAKE_CASE			  =json.load(open(hf_hub_download(a__	,a__	,repo_type='''dataset''')	,'''r'''))
								_SCREAMING_SNAKE_CASE			  ={int(a__): 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 						lowerCamelCase(   a__):
				# 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 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =state_dict.pop(a__)
				_SCREAMING_SNAKE_CASE			  =val







def 						lowerCamelCase(   a__	,a__=False):
				_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[:256, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[-256:, :]
								_SCREAMING_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
								_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[:256, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[:256]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[256:512, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[256:512]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight[-256:, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias[-256:]
								# 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[:256, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias_cross_attn[:256]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight_cross_attn[256:512, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias_cross_attn[256:512]
								_SCREAMING_SNAKE_CASE			  =in_proj_weight_cross_attn[-256:, :]
								_SCREAMING_SNAKE_CASE			  =in_proj_bias_cross_attn[-256:]







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)

				return im







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__=None	,a__=False):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =get_detr_config(a__)

				# 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=a__).eval()
				_SCREAMING_SNAKE_CASE			  =detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(a__):
								if is_panoptic:
												_SCREAMING_SNAKE_CASE			  ='''detr.''' + src
								rename_key(a__	,a__	,a__)
				# query, key and value matrices need special treatment
				read_in_q_k_v(a__	,is_panoptic=a__)
				# 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(a__)
																_SCREAMING_SNAKE_CASE			  =val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																_SCREAMING_SNAKE_CASE			  =state_dict.pop(a__)
																_SCREAMING_SNAKE_CASE			  =val
												elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																continue
												else:
																_SCREAMING_SNAKE_CASE			  =state_dict.pop(a__)
																_SCREAMING_SNAKE_CASE			  =val
								else:
												if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																_SCREAMING_SNAKE_CASE			  =state_dict.pop(a__)
																_SCREAMING_SNAKE_CASE			  =val

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

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

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

				_SCREAMING_SNAKE_CASE			  =detr(a__)
				_SCREAMING_SNAKE_CASE			  =model(a__)

				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(a__).mkdir(exist_ok=a__)
								model.save_pretrained(a__)
								processor.save_pretrained(a__)

				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__":
							snake_case_						:			Dict													=							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.''')
							snake_case_						:			Dict													=							parser.parse_args()
							convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



from collections.abc import Generator







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =0, 1
				while True:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =b, a + b
								yield b







def 						lowerCamelCase(   a__ = 1000):
				_SCREAMING_SNAKE_CASE			  =1
				_SCREAMING_SNAKE_CASE			  =fibonacci_generator()
				while len(str(next(a__))) < n:
								answer += 1
				return answer + 1


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