





'''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__				:   List[Any]      =logging.get_logger(__name__)
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase=False  ):





      """simple docstring"""


      _lowerCAmelCase							     =							[]
      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							     =							[(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    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase=False  ):





      """simple docstring"""


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





      """simple docstring"""


      _lowerCAmelCase							     =							dct.pop(lowerCAmelCase  )
      _lowerCAmelCase							     =							val
def    UpperCamelCase__      (		):





      """simple docstring"""


      _lowerCAmelCase							     =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase							     =							Image.open(requests.get(lowerCAmelCase							,	stream=lowerCAmelCase  ).raw  )
      return im
@torch.no_grad()
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


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

      # load original model from timm
      _lowerCAmelCase							     =							timm.create_model(lowerCAmelCase							,	pretrained=lowerCAmelCase  )
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      _lowerCAmelCase							     =							timm_model.state_dict()
      _lowerCAmelCase							     =							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							     =							DeiTForImageClassificationWithTeacher(lowerCAmelCase  ).eval()
      model.load_state_dict(lowerCAmelCase  )

      # Check outputs on an image, prepared by DeiTImageProcessor
      _lowerCAmelCase							     =							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							     =							DeiTImageProcessor(size=lowerCAmelCase							,	crop_size=config.image_size  )
      _lowerCAmelCase							     =							image_processor(images=prepare_img()							,	return_tensors="""pt"""  )
      _lowerCAmelCase							     =							encoding["""pixel_values"""]
      _lowerCAmelCase							     =							model(lowerCAmelCase  )

      _lowerCAmelCase							     =							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__":
      A__				:   Union[str, Any]      =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__				:   Tuple      =parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









"""simple docstring"""





import baseaa




def   _A  (  UpperCamelCase_     :       str)	->						bytes:
   '''simple docstring'''


   return baseaa.baaencode(string.encode("utf-8"))




def   _A  (  UpperCamelCase_     :       bytes)	->						str:
   '''simple docstring'''


   return baseaa.baadecode(UpperCamelCase_).decode("utf-8")


if __name__ == "__main__":
   _a       =							'Hello World!'
   _a       =							baseaa_encode(test)
   print(encoded)

   _a       =							baseaa_decode(encoded)
   print(decoded)




