





"""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      __lowerCamelCase   (							a_     :		Any ,   a_     :		int=False							)			->      Tuple:
      __SCREAMING_SNAKE_CASE   :str						=  []
      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"
            __SCREAMING_SNAKE_CASE   :Optional[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      __lowerCamelCase   (							a_     :		str ,   a_     :		Tuple ,   a_     :		Union[str, Any]=False							)			->      Union[str, Any]:





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



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		List[Any] ,   a_     :		Any							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  dct.pop(A__							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  val



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :str						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
      __SCREAMING_SNAKE_CASE   :str						=  Image.open(requests.get(A__ ,   stream=A__							).raw							)
      return im



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

      # load original model from timm
      __SCREAMING_SNAKE_CASE   :int						=  timm.create_model(A__ ,   pretrained=A__							)
      timm_model.eval()

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

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

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

      __SCREAMING_SNAKE_CASE   :List[Any]						=  timm_model(A__							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(A__ ,   outputs.logits ,   atol=1e-3							)

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


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'''






class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  int    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int=None    ,						lowerCamelCase    :  int=None		)   ->  str:
      lowerCAmelCase_			:      str			=   data
      lowerCAmelCase_			:      Optional[Any]			=   previous
      lowerCAmelCase_			:      int			=   next_node

    def __str__(   self    :  Any		)   ->  str:
      return F'{self.data}'

    def __lowercase   (   self    :  Optional[Any]		)   ->  int:
      return self.data

    def __lowercase   (   self    :  str		)   ->  List[str]:
      return self.next





    def __lowercase   (   self    :  int		)   ->  Optional[int]:
      return self.previous







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]    ,						lowerCamelCase    :  Optional[int]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   head

    def __iter__(   self    :  str		)   ->  Optional[Any]:
      return self





    def __lowercase   (   self    :  Union[str, Any]		)   ->  Dict:
      if not self.current:
        raise StopIteration
      else:
        lowerCAmelCase_			:      Dict			=   self.current.get_data()
        lowerCAmelCase_			:      Tuple			=   self.current.get_next()
        return value







class    __snake_case       :







    """simple docstring"""



    def __init__(   self    :  Union[str, Any]		)   ->  Any:
      lowerCAmelCase_			:      Optional[Any]			=   None  # First node in list
      lowerCAmelCase_			:      Optional[Any]			=   None  # Last node in list

    def __str__(   self    :  Optional[int]		)   ->  Dict:
      lowerCAmelCase_			:      str			=   self.head
      lowerCAmelCase_			:      Tuple			=   []
      while current is not None:
        nodes.append(current.get_data()		)
        lowerCAmelCase_			:      str			=   current.get_next()
      return " ".join(str(lowerCamelCase		) for node in nodes		)

    def __contains__(   self    :  List[Any]    ,						lowerCamelCase    :  int		)   ->  List[str]:
      lowerCAmelCase_			:      List[str]			=   self.head
      while current:
        if current.get_data() == value:
          return True
        lowerCAmelCase_			:      List[Any]			=   current.get_next()
      return False

    def __iter__(   self    :  str		)   ->  Optional[Any]:
      return LinkedListIterator(self.head		)

    def __lowercase   (   self    :  Dict		)   ->  Optional[int]:
      if self.head:
        return self.head.get_data()
      return None

    def __lowercase   (   self    :  List[str]		)   ->  Optional[Any]:
      if self.tail:
        return self.tail.get_data()
      return None

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Node		)   ->  None:
      if self.head is None:
        lowerCAmelCase_			:      Union[str, Any]			=   node
        lowerCAmelCase_			:      List[str]			=   node
      else:
        self.insert_before_node(self.head    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  Node		)   ->  None:
      if self.head is None:
        self.set_head(lowerCamelCase		)
      else:
        self.insert_after_node(self.tail    ,						lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  int		)   ->  None:
      lowerCAmelCase_			:      int			=   Node(lowerCamelCase		)
      if self.head is None:
        self.set_head(lowerCamelCase		)
      else:
        self.set_tail(lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Node    ,						lowerCamelCase    :  Node		)   ->  None:
      lowerCAmelCase_			:      Optional[int]			=   node
      lowerCAmelCase_			:      List[Any]			=   node.previous

      if node.get_previous() is None:
        lowerCAmelCase_			:      Tuple			=   node_to_insert
      else:
        lowerCAmelCase_			:      Dict			=   node_to_insert

      lowerCAmelCase_			:      Optional[int]			=   node_to_insert

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Node    ,						lowerCamelCase    :  Node		)   ->  None:
      lowerCAmelCase_			:      Optional[int]			=   node
      lowerCAmelCase_			:      Tuple			=   node.next

      if node.get_next() is None:
        lowerCAmelCase_			:      Tuple			=   node_to_insert
      else:
        lowerCAmelCase_			:      Tuple			=   node_to_insert

      lowerCAmelCase_			:      Optional[Any]			=   node_to_insert

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  int		)   ->  None:
      lowerCAmelCase_			:      List[str]			=   1
      lowerCAmelCase_			:      Tuple			=   Node(lowerCamelCase		)
      lowerCAmelCase_			:      List[Any]			=   self.head
      while node:
        if current_position == position:
          self.insert_before_node(lowerCamelCase    ,						lowerCamelCase		)
          return
        current_position += 1
        lowerCAmelCase_			:      str			=   node.next
      self.insert_after_node(self.tail    ,						lowerCamelCase		)

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  int		)   ->  Node:
      lowerCAmelCase_			:      List[Any]			=   self.head
      while node:
        if node.get_data() == item:
          return node
        lowerCAmelCase_			:      List[Any]			=   node.get_next()
      raise Exception("""Node not found"""		)

    def __lowercase   (   self    :  str    ,						lowerCamelCase    :  str		)   ->  int:
      if (node := self.get_node(lowerCamelCase		)) is not None:
        if node == self.head:
          lowerCAmelCase_			:      Any			=   self.head.get_next()

        if node == self.tail:
          lowerCAmelCase_			:      Optional[int]			=   self.tail.get_previous()

        self.remove_node_pointers(lowerCamelCase		)

    @staticmethod
    def __lowercase   (   lowerCamelCase    :  Node		)   ->  None:
      if node.get_next():
        lowerCAmelCase_			:      Tuple			=   node.previous

      if node.get_previous():
        lowerCAmelCase_			:      Any			=   node.next

      lowerCAmelCase_			:      List[Any]			=   None
      lowerCAmelCase_			:      Any			=   None





    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      return self.head is None







def 				UpperCamelCase_			(    ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



