



'''simple docstring'''


import argparse
import json
from collections import OrderedDict
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
from transformers.utils import logging


logging.set_verbosity_info()
lowercase											=      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase											=      []
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 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'''),
    ]
)






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=state_dict.pop(lowercase__							)
     a_									=val






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







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

     return new_state_dict






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__=False							):


     '''simple docstring'''







     a_									=""
     if is_panoptic:
          a_									="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)
          a_									=state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""							)
          a_									=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
          a_									=in_proj_weight[:2_5_6, :]
          a_									=in_proj_bias[:2_5_6]
          a_									=in_proj_weight[2_5_6:5_1_2, :]
          a_									=in_proj_bias[2_5_6:5_1_2]
          a_									=in_proj_weight[-2_5_6:, :]
          a_									=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
          a_									=state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""							)
          a_									=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
          a_									=in_proj_weight[:2_5_6, :]
          a_									=in_proj_bias[:2_5_6]
          a_									=in_proj_weight[2_5_6:5_1_2, :]
          a_									=in_proj_bias[2_5_6:5_1_2]
          a_									=in_proj_weight[-2_5_6:, :]
          a_									=in_proj_bias[-2_5_6:]
          # read in weights + bias of input projection layer of cross-attention
          a_									=state_dict.pop(
              F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""							)
          a_									=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
          a_									=in_proj_weight_cross_attn[:2_5_6, :]
          a_									=in_proj_bias_cross_attn[:2_5_6]
          a_									=in_proj_weight_cross_attn[2_5_6:5_1_2, :]
          a_									=in_proj_bias_cross_attn[2_5_6:5_1_2]
          a_									=in_proj_weight_cross_attn[-2_5_6:, :]
          a_									=in_proj_bias_cross_attn[-2_5_6:]






def 			UpperCAmelCase_							(		):


     '''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 			UpperCAmelCase_							(		lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_									=DetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          a_									="resnet101"
     if "dc5" in model_name:
          a_									=True
     a_									="panoptic" in model_name
     if is_panoptic:
          a_									=2_5_0
     else:
          a_									=9_1
          a_									="huggingface/label-files"
          a_									="coco-detection-id2label.json"
          a_									=json.load(open(hf_hub_download(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()}

     # load image processor
     a_									="coco_panoptic" if is_panoptic else "coco_detection"
     a_									=DetrImageProcessor(format=lowercase__							)

     # prepare image
     a_									=prepare_img()
     a_									=image_processor(images=lowercase__							,						return_tensors="pt"							)
     a_									=encoding["pixel_values"]

     logger.info(F"""Converting model {model_name}..."""							)

     # load original model from torch hub
     a_									=torch.hub.load("facebookresearch/detr"							,						lowercase__							,						pretrained=lowercase__							).eval()
     a_									=detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               a_									="detr." + src
          rename_key(lowercase__							,						lowercase__							,						lowercase__							)
     a_									=rename_backbone_keys(lowercase__							)
     # query, key and value matrices need special treatment
     read_in_q_k_v(lowercase__							,						is_panoptic=lowercase__							)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     a_									="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"							)
               ):
                    a_									=state_dict.pop(lowercase__							)
                    a_									=val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    a_									=state_dict.pop(lowercase__							)
                    a_									=val
               elif key.startswith("bbox_attention"							) or key.startswith("mask_head"							):
                    continue
               else:
                    a_									=state_dict.pop(lowercase__							)
                    a_									=val
          else:
               if not key.startswith("class_labels_classifier"							) and not key.startswith("bbox_predictor"							):
                    a_									=state_dict.pop(lowercase__							)
                    a_									=val
    # finally, create HuggingFace model and load state dict
     a_									=DetrForSegmentation(lowercase__							) if is_panoptic else DetrForObjectDetection(lowercase__							)
     model.load_state_dict(lowercase__							)
     model.eval()
     # verify our conversion
     a_									=detr(lowercase__							)
     a_									=model(lowercase__							)
     assert torch.allclose(outputs.logits							,						original_outputs["pred_logits"]							,						atol=1E-4							)
     assert torch.allclose(outputs.pred_boxes							,						original_outputs["pred_boxes"]							,						atol=1E-4							)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks							,						original_outputs["pred_masks"]							,						atol=1E-4							)

     # Save model and image processor
     logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""							)
     Path(lowercase__							).mkdir(exist_ok=lowercase__							)
     model.save_pretrained(lowercase__							)
     image_processor.save_pretrained(lowercase__							)


if __name__ == "__main__":
  lowercase											=      argparse.ArgumentParser()

  parser.add_argument(
      '''--model_name''', default='''detr_resnet50''', type=str, 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.'''
  )
  lowercase											=      parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from math import floor, sqrt






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=0.0
     a_									=1.0
     a_									=int(sqrt(lowercase__							)							)
     a_									=root
     a_									=0
     while integer_part != 2 * root:
          a_									=denominator * integer_part - numerator
          a_									=(n - numerator**2) / denominator
          a_									=int((root + numerator) / denominator							)
          period += 1
     return period






def 			UpperCAmelCase_							(		lowercase__ = 1_0_0_0_0							):


     '''simple docstring'''







     a_									=0
     for i in range(2							,						n + 1							):
          a_									=sqrt(lowercase__							)
          if sr - floor(lowercase__							) != 0 and continuous_fraction_period(lowercase__							) % 2 == 1:
               count_odd_periods += 1
     return count_odd_periods


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


