

"""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()
a					:Dict    = logging.get_logger(__name__)




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[]
      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__   :      List[Any]      		=	[(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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				str:
      for i in range(config.num_hidden_layers							):
            if base_model:
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	""""""
            else:
                  SCREAMING_SNAKE_CASE__   :      str      		=	"""deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE__   :      str      		=	state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''							)
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	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__   :      int      		=	in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE__   :      Any      		=	in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	in_proj_bias[-config.hidden_size :]




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	dct.pop(SCREAMING_SNAKE_CASE_							)
      SCREAMING_SNAKE_CASE__   :      int      		=	val




def  _lowercase					(    )       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      Dict      		=	Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,					stream=SCREAMING_SNAKE_CASE_							).raw							)
      return im




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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__   :      str      		=	1000
      SCREAMING_SNAKE_CASE__   :      Dict      		=	"""huggingface/label-files"""
      SCREAMING_SNAKE_CASE__   :      str      		=	"""imagenet-1k-id2label.json"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					repo_type="""dataset"""							)  ,					"""r"""							)							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	idalabel
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	{v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE__   :      str      		=	int(deit_name[-6:-4]							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	int(deit_name[-3:]							)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""							):
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	192
            SCREAMING_SNAKE_CASE__   :      int      		=	768
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	12
            SCREAMING_SNAKE_CASE__   :      int      		=	3
      elif deit_name[9:].startswith("""small"""							):
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	384
            SCREAMING_SNAKE_CASE__   :      Any      		=	1536
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	12
            SCREAMING_SNAKE_CASE__   :      Any      		=	6
      if deit_name[9:].startswith("""base"""							):
            pass
      elif deit_name[4:].startswith("""large"""							):
            SCREAMING_SNAKE_CASE__   :      int      		=	1024
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	4096
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	24
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	16

      # load original model from timm
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	timm.create_model(SCREAMING_SNAKE_CASE_  ,					pretrained=SCREAMING_SNAKE_CASE_							)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      SCREAMING_SNAKE_CASE__   :      Dict      		=	timm_model.state_dict()
      SCREAMING_SNAKE_CASE__   :      Dict      		=	create_rename_keys(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_							)
      for src, dest in rename_keys:
            rename_key(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_							)
      read_in_q_k_v(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_							).eval()
      model.load_state_dict(SCREAMING_SNAKE_CASE_							)

      # Check outputs on an image, prepared by DeiTImageProcessor
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	int(
          (256 / 224) * 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__   :      int      		=	DeiTImageProcessor(size=SCREAMING_SNAKE_CASE_  ,					crop_size=config.image_size							)
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_processor(images=prepare_img()  ,					return_tensors="""pt"""							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	encoding["""pixel_values"""]
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(SCREAMING_SNAKE_CASE_							)

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	timm_model(SCREAMING_SNAKE_CASE_							)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(SCREAMING_SNAKE_CASE_  ,					outputs.logits  ,					atol=1E-3							)

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


if __name__ == "__main__":
  a					:str    = 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."
  )

  a					:List[str]    = parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




