



"""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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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






def 	snake_case_  (				A_ :		Union[str, Any],    A_ :		Dict=False						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, 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"
              _lowerCamelCase						:    int         =			[(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_  (				A_ :		List[Any],    A_ :		List[Any],    A_ :		List[str]=False						):


       '''simple docstring'''





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






def 	snake_case_  (				A_ :		Optional[int],    A_ :		Optional[Any],    A_ :		Optional[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			dct.pop(lowerCamelCase__						)
       _lowerCamelCase						:    Dict         =			val






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       _lowerCamelCase						:    List[str]         =			Image.open(requests.get(lowerCamelCase__,    stream=lowerCamelCase__						).raw						)
       return im






@torch.no_grad()
def 	snake_case_  (				A_ :		Union[str, Any],    A_ :		Dict						):


       '''simple docstring'''





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

       # load original model from timm
       _lowerCamelCase						:    Dict         =			timm.create_model(lowerCamelCase__,    pretrained=lowerCamelCase__						)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       _lowerCamelCase						:    Any         =			timm_model.state_dict()
       _lowerCamelCase						:    Union[str, Any]         =			create_rename_keys(lowerCamelCase__,    lowerCamelCase__						)
       for src, dest in rename_keys:
              rename_key(lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__						)
       read_in_q_k_v(lowerCamelCase__,    lowerCamelCase__,    lowerCamelCase__						)

       # load HuggingFace model
       _lowerCamelCase						:    Dict         =			DeiTForImageClassificationWithTeacher(lowerCamelCase__						).eval()
       model.load_state_dict(lowerCamelCase__						)

       # Check outputs on an image, prepared by DeiTImageProcessor
       _lowerCamelCase						:    Dict         =			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
       _lowerCamelCase						:    Optional[int]         =			DeiTImageProcessor(size=lowerCamelCase__,    crop_size=config.image_size						)
       _lowerCamelCase						:    Optional[int]         =			image_processor(images=prepare_img(),    return_tensors='''pt'''						)
       _lowerCamelCase						:    Optional[Any]         =			encoding['''pixel_values''']
       _lowerCamelCase						:    List[Any]         =			model(lowerCamelCase__						)

       _lowerCamelCase						:    int         =			timm_model(lowerCamelCase__						)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(lowerCamelCase__,    outputs.logits,    atol=1E-3						)

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


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)







"""simple docstring"""






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


