

'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def 					__UpperCamelCase				(						lowercase__    :	Dict			):






     '''simple docstring'''





     __lowercase      		=SwinvaConfig()
     __lowercase      		=swinva_name.split('_'			)

     __lowercase      		=name_split[1]
     if "to" in name_split[3]:
          __lowercase      		=int(name_split[3][-3:]			)
     else:
          __lowercase      		=int(name_split[3]			)
     if "to" in name_split[2]:
          __lowercase      		=int(name_split[2][-2:]			)
     else:
          __lowercase      		=int(name_split[2][6:]			)

     if model_size == "tiny":
          __lowercase      		=96
          __lowercase      		=(2, 2, 6, 2)
          __lowercase      		=(3, 6, 12, 24)
     elif model_size == "small":
          __lowercase      		=96
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(3, 6, 12, 24)
     elif model_size == "base":
          __lowercase      		=1_28
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(4, 8, 16, 32)
     else:
          __lowercase      		=1_92
          __lowercase      		=(2, 2, 18, 2)
          __lowercase      		=(6, 12, 24, 48)

     if "to" in swinva_name:
          __lowercase      		=(12, 12, 12, 6)

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          __lowercase      		=2_18_41
          __lowercase      		='huggingface/label-files'
          __lowercase      		='imagenet-22k-id2label.json'
          __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}

     else:
          __lowercase      		=10_00
          __lowercase      		='huggingface/label-files'
          __lowercase      		='imagenet-1k-id2label.json'
          __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
          __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
          __lowercase      		=idalabel
          __lowercase      		={v: k for k, v in idalabel.items()}

     __lowercase      		=img_size
     __lowercase      		=num_classes
     __lowercase      		=embed_dim
     __lowercase      		=depths
     __lowercase      		=num_heads
     __lowercase      		=window_size

     return config

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "patch_embed.norm" in name:
          __lowercase      		=name.replace('patch_embed.norm',       'embeddings.norm'			)
     if "layers" in name:
          __lowercase      		='encoder.' + name
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "q_bias" in name:
          __lowercase      		=name.replace('q_bias',       'query.bias'			)
     if "k_bias" in name:
          __lowercase      		=name.replace('k_bias',       'key.bias'			)
     if "v_bias" in name:
          __lowercase      		=name.replace('v_bias',       'value.bias'			)
     if "cpb_mlp" in name:
          __lowercase      		=name.replace('cpb_mlp',       'continuous_position_bias_mlp'			)
     if name == "norm.weight":
          __lowercase      		='layernorm.weight'
     if name == "norm.bias":
          __lowercase      		='layernorm.bias'

     if "head" in name:
          __lowercase      		=name.replace('head',       'classifier'			)
     else:
          __lowercase      		='swinv2.' + name

     return name

def 					__UpperCamelCase				(						lowercase__    :	Optional[int],       lowercase__    :	Tuple			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

          if "mask" in key:
               continue
          elif "qkv" in key:
               __lowercase      		=key.split('.'			)
               __lowercase      		=int(key_split[1]			)
               __lowercase      		=int(key_split[3]			)
               __lowercase      		=model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    __lowercase      		=val[:dim, :]
                    __lowercase      		=val[dim : dim * 2, :]
                    __lowercase      		=val[-dim:, :]
               else:
                    __lowercase      		=val[:dim]
                    __lowercase      		=val[
                        dim : dim * 2
                    ]
                    __lowercase      		=val[-dim:]
          else:
               __lowercase      		=val

     return orig_state_dict

def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=timm.create_model(lowercase__,       pretrained=lowercase__			)
     timm_model.eval()

     __lowercase      		=get_swinva_config(lowercase__			)
     __lowercase      		=SwinvaForImageClassification(lowercase__			)
     model.eval()

     __lowercase      		=convert_state_dict(timm_model.state_dict(),       lowercase__			)
     model.load_state_dict(lowercase__			)

     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'

     __lowercase      		=AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_',       '-'			)			)			)
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     __lowercase      		=image_processor(images=lowercase__,       return_tensors='pt'			)

     __lowercase      		=timm_model(inputs['pixel_values']			)
     __lowercase      		=model(**lowercase__			).logits

     assert torch.allclose(lowercase__,       lowercase__,       atol=1E-3			)

     print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)

     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     model.push_to_hub(
         repo_path_or_name=Path(lowercase__,       lowercase__			),       organization='nandwalritik',       commit_message='Add model',       )


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swinv2_name''',
           default='''swinv2_tiny_patch4_window8_256''',
           type=str,
           help='''Name of the Swinv2 timm model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)





'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     if n == 1 or not isinstance(lowercase__,       lowercase__			):
          return 0
     elif n == 2:
          return 1
     else:
          __lowercase      		=[0, 1]
          for i in range(2,       n + 1			):
               sequence.append(sequence[i - 1] + sequence[i - 2]			)

          return sequence[n]

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=0
     __lowercase      		=2

     while digits < n:
          index += 1
          __lowercase      		=len(str(fibonacci(lowercase__			)			)			)

     return index

def 					__UpperCamelCase				(						lowercase__    :	int = 10_00			):






     '''simple docstring'''





     return fibonacci_digits_index(lowercase__			)


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


