


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




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




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

       # load original model from timm
       lowercase__:       Tuple        =       timm.create_model(snake_case      ,							pretrained=snake_case					)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowercase__:       List[str]        =       timm_model.state_dict()
       lowercase__:       Optional[Any]        =       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__:       Optional[Any]        =       DeiTForImageClassificationWithTeacher(snake_case					).eval()
       model.load_state_dict(snake_case					)

       # Check outputs on an image, prepared by DeiTImageProcessor
       lowercase__:       Union[str, 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__:       str        =       DeiTImageProcessor(size=snake_case      ,							crop_size=config.image_size					)
       lowercase__:       int        =       image_processor(images=prepare_img()      ,							return_tensors='pt'					)
       lowercase__:       str        =       encoding['pixel_values']
       lowercase__:       Optional[int]        =       model(snake_case					)

       lowercase__:       List[str]        =       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)



from math import pow




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							)  ->    tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       lowercase__:       int        =       int(pow(snake_case      ,							snake_case					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              lowercase__      ,						lowercase__:       Any        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              lowercase__      ,						lowercase__:       Optional[int]        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
       return current_sum, solutions_count




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  'Invalid input\n'
                  'needed_sum must be between 1 and 1000, power between 2 and 10.'					)

       return backtrack(snake_case      ,							snake_case      ,							1      ,							0      ,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
 import doctest

 doctest.testmod()


