






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()
UpperCAmelCase__ :     Optional[Any]				    =    logging.get_logger(__name__)






def 					__lowercase      (     _A						,    _A=False			)     ->						str:
      SCREAMING_SNAKE_CASE :      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"
            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 					__lowercase      (     _A						,    _A						,    _A=False			)     ->						Any:


      for i in range(config.num_hidden_layers			):
            if base_model:
                  SCREAMING_SNAKE_CASE :      List[str]       =   ""
            else:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   "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 :      Dict       =   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 :      Any       =   in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE :      List[str]       =   in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE :      int       =   in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE :      List[Any]       =   in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   in_proj_bias[-config.hidden_size :]






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   dct.pop(__snake_case			)
      SCREAMING_SNAKE_CASE :      Dict       =   val






def 					__lowercase      (     )     ->						int:
      SCREAMING_SNAKE_CASE :      Tuple       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE :      Optional[int]       =   Image.open(requests.get(__snake_case						,    stream=__snake_case			).raw			)
      return im






@torch.no_grad()
def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   DeiTConfig()
      # all deit models have fine-tuned heads
      SCREAMING_SNAKE_CASE :      Tuple       =   False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE :      Optional[int]       =   1000
      SCREAMING_SNAKE_CASE :      Tuple       =   "huggingface/label-files"
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE :      Dict       =   json.load(open(hf_hub_download(__snake_case						,    __snake_case						,    repo_type="""dataset"""			)						,    """r"""			)			)
      SCREAMING_SNAKE_CASE :      int       =   {int(__snake_case			): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE :      List[str]       =   idalabel
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   {v: k for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE :      int       =   int(deit_name[-6:-4]			)
      SCREAMING_SNAKE_CASE :      str       =   int(deit_name[-3:]			)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   192
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   768
            SCREAMING_SNAKE_CASE :      Tuple       =   12
            SCREAMING_SNAKE_CASE :      int       =   3
      elif deit_name[9:].startswith("""small"""			):
            SCREAMING_SNAKE_CASE :      Any       =   384
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1536
            SCREAMING_SNAKE_CASE :      List[Any]       =   12
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   6
      if deit_name[9:].startswith("""base"""			):
            pass
      elif deit_name[4:].startswith("""large"""			):
            SCREAMING_SNAKE_CASE :      Tuple       =   1024
            SCREAMING_SNAKE_CASE :      int       =   4096
            SCREAMING_SNAKE_CASE :      List[Any]       =   24
            SCREAMING_SNAKE_CASE :      Any       =   16

      # load original model from timm
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   timm.create_model(__snake_case						,    pretrained=__snake_case			)
      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(__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
      SCREAMING_SNAKE_CASE :      List[Any]       =   DeiTForImageClassificationWithTeacher(__snake_case			).eval()
      model.load_state_dict(__snake_case			)

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

      SCREAMING_SNAKE_CASE :      Optional[Any]       =   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__":
  UpperCAmelCase__ :     Dict				    =    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."""
  )

  UpperCAmelCase__ :     Optional[int]				    =    parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







from typing import Any

import numpy as np






def 					__lowercase      (     _A			)     ->						bool:
      return np.array_equal(_A						,    matrix.conjugate().T			)






def 					__lowercase      (     _A						,    _A			)     ->						Any:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   v.conjugate().T
      SCREAMING_SNAKE_CASE :      List[Any]       =   v_star.dot(_A			)
      assert isinstance(_A						,    np.ndarray			)
      return (v_star_dot.dot(_A			)) / (v_star.dot(_A			))






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      Any       =   np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]]			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array([[1], [2], [3]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      print(rayleigh_quotient(_A						,    _A			)			)

      SCREAMING_SNAKE_CASE :      Any       =   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
      assert is_hermitian(_A			), F"{a} is not hermitian."
      assert rayleigh_quotient(_A						,    _A			) == float(3			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()




