


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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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




def    snake_case_     (  snake_case			,							snake_case=False     )  ->				Optional[Any]:
       lowercase__:   int  			=   []
       for i in range(config.num_hidden_layers     ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((f'blocks.{i}.norm1.weight', f'deit.encoder.layer.{i}.layernorm_before.weight')     )
              rename_keys.append((f'blocks.{i}.norm1.bias', f'deit.encoder.layer.{i}.layernorm_before.bias')     )
              rename_keys.append((f'blocks.{i}.attn.proj.weight', f'deit.encoder.layer.{i}.attention.output.dense.weight')     )
              rename_keys.append((f'blocks.{i}.attn.proj.bias', f'deit.encoder.layer.{i}.attention.output.dense.bias')     )
              rename_keys.append((f'blocks.{i}.norm2.weight', f'deit.encoder.layer.{i}.layernorm_after.weight')     )
              rename_keys.append((f'blocks.{i}.norm2.bias', f'deit.encoder.layer.{i}.layernorm_after.bias')     )
              rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'deit.encoder.layer.{i}.intermediate.dense.weight')     )
              rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'deit.encoder.layer.{i}.intermediate.dense.bias')     )
              rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'deit.encoder.layer.{i}.output.dense.weight')     )
              rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'deit.encoder.layer.{i}.output.dense.bias')     )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('cls_token', 'deit.embeddings.cls_token'),
               ('dist_token', 'deit.embeddings.distillation_token'),
               ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
               ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
               ('pos_embed', 'deit.embeddings.position_embeddings'),
           ]     )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('norm.weight', 'layernorm.weight'),
                      ('norm.bias', 'layernorm.bias'),
                      ('pre_logits.fc.weight', 'pooler.dense.weight'),
                      ('pre_logits.fc.bias', 'pooler.dense.bias'),
                  ]     )

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              lowercase__:   Tuple  			=   [(pair[0], pair[1][4:]) if pair[1].startswith('deit'     ) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ('norm.weight', 'deit.layernorm.weight'),
                      ('norm.bias', 'deit.layernorm.bias'),
                      ('head.weight', 'cls_classifier.weight'),
                      ('head.bias', 'cls_classifier.bias'),
                      ('head_dist.weight', 'distillation_classifier.weight'),
                      ('head_dist.bias', 'distillation_classifier.bias'),
                  ]     )

       return rename_keys




def    snake_case_     (  snake_case			,							snake_case			,							snake_case=False     )  ->				Optional[int]:
       for i in range(config.num_hidden_layers     ):
              if base_model:
                     lowercase__:   str  			=   ''
              else:
                     lowercase__:   Optional[Any]  			=   'deit.'
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase__:   int  			=   state_dict.pop(f'blocks.{i}.attn.qkv.weight'     )
              lowercase__:   Tuple  			=   state_dict.pop(f'blocks.{i}.attn.qkv.bias'     )
              # next, add query, keys and values (in that order) to the state dict
              lowercase__:   List[str]  			=   in_proj_weight[
                  : config.hidden_size, :
              ]
              lowercase__:   Dict  			=   in_proj_bias[: config.hidden_size]
              lowercase__:   List[Any]  			=   in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase__:   Optional[int]  			=   in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase__:   Dict  			=   in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowercase__:   Dict  			=   in_proj_bias[-config.hidden_size :]




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				Dict:
       lowercase__:   Optional[int]  			=   dct.pop(snake_case     )
       lowercase__:   List[str]  			=   val




def    snake_case_     (  )  ->				Union[str, Any]:
       lowercase__:   List[Any]  			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:   Dict  			=   Image.open(requests.get(snake_case			,							stream=snake_case     ).raw     )
       return im




@torch.no_grad()
def    snake_case_     (  snake_case			,							snake_case     )  ->				Dict:
       lowercase__:   Tuple  			=   DeiTConfig()
       # all deit models have fine-tuned heads
       lowercase__:   Optional[int]  			=   False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       lowercase__:   Optional[Any]  			=   10_00
       lowercase__:   Dict  			=   'huggingface/label-files'
       lowercase__:   Any  			=   'imagenet-1k-id2label.json'
       lowercase__:   Any  			=   json.load(open(hf_hub_download(snake_case			,							snake_case			,							repo_type='dataset'     )			,							'r'     )     )
       lowercase__:   Optional[int]  			=   {int(snake_case     ): v for k, v in idalabel.items()}
       lowercase__:   int  			=   idalabel
       lowercase__:   Tuple  			=   {v: k for k, v in idalabel.items()}
       lowercase__:   Optional[int]  			=   int(deit_name[-6:-4]     )
       lowercase__:   Optional[Any]  			=   int(deit_name[-3:]     )
       # size of the architecture
       if deit_name[9:].startswith('tiny'     ):
              lowercase__:   List[Any]  			=   1_92
              lowercase__:   int  			=   7_68
              lowercase__:   Dict  			=   12
              lowercase__:   Union[str, Any]  			=   3
       elif deit_name[9:].startswith('small'     ):
              lowercase__:   Union[str, Any]  			=   3_84
              lowercase__:   Tuple  			=   15_36
              lowercase__:   Optional[int]  			=   12
              lowercase__:   Tuple  			=   6
       if deit_name[9:].startswith('base'     ):
              pass
       elif deit_name[4:].startswith('large'     ):
              lowercase__:   Optional[int]  			=   10_24
              lowercase__:   Tuple  			=   40_96
              lowercase__:   List[str]  			=   24
              lowercase__:   Any  			=   16

       # load original model from timm
       lowercase__:   Optional[Any]  			=   timm.create_model(snake_case			,							pretrained=snake_case     )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowercase__:   str  			=   timm_model.state_dict()
       lowercase__:   Tuple  			=   create_rename_keys(snake_case			,							snake_case     )
       for src, dest in rename_keys:
              rename_key(snake_case			,							snake_case			,							snake_case     )
       read_in_q_k_v(snake_case			,							snake_case			,							snake_case     )

       # load HuggingFace model
       lowercase__:   List[Any]  			=   DeiTForImageClassificationWithTeacher(snake_case     ).eval()
       model.load_state_dict(snake_case     )

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowercase__:   Any  			=   int(
           (2_56 / 2_24) * config.image_size     )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       lowercase__:   Dict  			=   DeiTImageProcessor(size=snake_case			,							crop_size=config.image_size     )
       lowercase__:   Any  			=   image_processor(images=prepare_img()			,							return_tensors='pt'     )
       lowercase__:   Tuple  			=   encoding['pixel_values']
       lowercase__:   Union[str, Any]  			=   model(snake_case     )

       lowercase__:   Any  			=   timm_model(snake_case     )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(snake_case			,							outputs.logits			,							atol=1e-3     )

       Path(snake_case     ).mkdir(exist_ok=snake_case     )
       print(f'Saving model {deit_name} to {pytorch_dump_folder_path}'     )
       model.save_pretrained(snake_case     )
       print(f'Saving image processor to {pytorch_dump_folder_path}'     )
       image_processor.save_pretrained(snake_case     )


if __name__ == "__main__":
 __lowerCAmelCase		       =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--deit_name''',
     default='''vit_deit_base_distilled_patch16_224''',
     type=str,
     help='''Name of the DeiT 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.'''
 )

 __lowerCAmelCase		       =							parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






__lowerCAmelCase		       =							range(2, 20 + 1)
__lowerCAmelCase		       =							[10**k for k in range(ks[-1] + 1)]
__lowerCAmelCase		       =							{}




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				Optional[int]:
       lowercase__:   str  			=   sum(a_i[j] for j in range(snake_case			,							len(snake_case     )     )     )
       lowercase__:   Optional[int]  			=   sum(a_i[j] * base[j] for j in range(min(len(snake_case     )			,							snake_case     )     )     )

       lowercase__			,						lowercase__:   str  			=   0, 0
       lowercase__:   Tuple  			=   n - i

       lowercase__:   Dict  			=   memo.get(snake_case     )

       if sub_memo is not None:
              lowercase__:   Optional[Any]  			=   sub_memo.get(snake_case     )

              if jumps is not None and len(snake_case     ) > 0:
                     # find and make the largest jump without going over
                     lowercase__:   int  			=   -1
                     for _k in range(len(snake_case     ) - 1			,							-1			,							-1     ):
                            if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
                                   lowercase__:   Union[str, Any]  			=   _k
                                   break

                     if max_jump >= 0:
                            lowercase__			,						lowercase__			,						lowercase__:   Any  			=   jumps[max_jump]
                            # since the difference between jumps is cached, add c
                            lowercase__:   str  			=   diff + c
                            for j in range(min(snake_case			,							len(snake_case     )     )     ):
                                   lowercase__			,						lowercase__:   Dict  			=   divmod(snake_case			,							10     )
                            if new_c > 0:
                                   add(snake_case			,							snake_case			,							snake_case     )

              else:
                     lowercase__:   List[Any]  			=   []
       else:
              lowercase__:   Optional[Any]  			=   {c: []}
              lowercase__:   Union[str, Any]  			=   sub_memo

       if dn >= max_dn or c + diff >= base[k]:
              return diff, dn

       if k > ks[0]:
              while True:
                     # keep doing smaller jumps
                     lowercase__			,						lowercase__:   Union[str, Any]  			=   next_term(snake_case			,							k - 1			,							i + dn			,							snake_case     )
                     diff += _diff
                     dn += terms_jumped

                     if dn >= max_dn or c + diff >= base[k]:
                            break
       else:
              # would be too small a jump, just compute sequential terms instead
              lowercase__			,						lowercase__:   Dict  			=   compute(snake_case			,							snake_case			,							i + dn			,							snake_case     )
              diff += _diff
              dn += terms_jumped

       lowercase__:   Any  			=   sub_memo[c]

       # keep jumps sorted by # of terms skipped
       lowercase__:   str  			=   0
       while j < len(snake_case     ):
              if jumps[j][1] > dn:
                     break
              j += 1

       # cache the jump for this value digitsum(b) and c
       sub_memo[c].insert(snake_case			,							(diff, dn, k)     )
       return (diff, dn)




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case     )  ->				str:
       if i >= n:
              return 0, i
       if k > len(snake_case     ):
              a_i.extend([0 for _ in range(k - len(snake_case     )     )]     )

       # note: a_i -> b * 10^k + c
       # ds_b -> digitsum(b)
       # ds_c -> digitsum(c)
       lowercase__:   List[Any]  			=   i
       lowercase__			,						lowercase__			,						lowercase__:   Any  			=   0, 0, 0
       for j in range(len(snake_case     )     ):
              if j >= k:
                     ds_b += a_i[j]
              else:
                     ds_c += a_i[j]

       while i < n:
              i += 1
              lowercase__:   str  			=   ds_c + ds_b
              diff += addend
              lowercase__:   List[str]  			=   0
              for j in range(snake_case     ):
                     lowercase__:   Any  			=   a_i[j] + addend
                     lowercase__			,						lowercase__:   List[Any]  			=   divmod(snake_case			,							10     )

                     ds_c += a_i[j]

              if addend > 0:
                     break

       if addend > 0:
              add(snake_case			,							snake_case			,							snake_case     )
       return diff, i - start_i




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				int:
       for j in range(snake_case			,							len(snake_case     )     ):
              lowercase__:   str  			=   digits[j] + addend
              if s >= 10:
                     lowercase__			,						lowercase__:   Any  			=   divmod(snake_case			,							10     )
                     lowercase__:   Any  			=   addend // 10 + quotient
              else:
                     lowercase__:   Union[str, Any]  			=   s
                     lowercase__:   Union[str, Any]  			=   addend // 10

              if addend == 0:
                     break

       while addend > 0:
              lowercase__			,						lowercase__:   Union[str, Any]  			=   divmod(snake_case			,							10     )
              digits.append(snake_case     )




def    snake_case_     (  snake_case = 10**15     )  ->				int:
       lowercase__:   Optional[Any]  			=   [1]
       lowercase__:   int  			=   1
       lowercase__:   Tuple  			=   0
       while True:
              lowercase__			,						lowercase__:   str  			=   next_term(snake_case			,							20			,							i + dn			,							snake_case     )
              dn += terms_jumped
              if dn == n - i:
                     break

       lowercase__:   Dict  			=   0
       for j in range(len(snake_case     )     ):
              a_n += digits[j] * 10**j
       return a_n


if __name__ == "__main__":
 print(F'''{solution() = }''')


