



"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:    Dict				   =     logging.get_logger(__name__)

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    str=False						)						->       Any:

  """simple docstring"""




  lowerCAmelCase_       :    List[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"vit.encoder.layer.{i}.layernorm_before.weight")						)
    rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")						)
    rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")						)
    rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")						)
    rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")						)
    rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")						)
    rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")						)
    rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")						)
    rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")						)
    rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")						)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'vit.embeddings.cls_token'),
          ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'vit.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 "vit" from all keys that start with "vit"
    lowerCAmelCase_       :    Tuple								=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'						) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]						)

  return rename_keys

def     UpperCamelCase_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    Optional[Any]   ,       lowerCAmelCase__				:    Any=False						)						->       Optional[Any]:

  """simple docstring"""




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

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]						)						->       Optional[int]:

  """simple docstring"""




  lowerCAmelCase_       :    List[Any]								=			['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase__   ,       lowerCAmelCase__						)

def     UpperCamelCase_   (   lowerCAmelCase__				:    List[Any]   ,       lowerCAmelCase__				:    Dict   ,       lowerCAmelCase__				:    Optional[int]						)						->       List[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    Optional[int]								=			dct.pop(lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[int]								=			val

def     UpperCamelCase_   (   )						->       Tuple:

  """simple docstring"""




  lowerCAmelCase_       :    str								=			'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_       :    Dict								=			Image.open(requests.get(lowerCAmelCase__   ,       stream=lowerCAmelCase__						).raw						)
  return im

@torch.no_grad()
def     UpperCamelCase_   (   lowerCAmelCase__				:    Tuple   ,       lowerCAmelCase__				:    List[Any]						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Tuple								=			ViTConfig()
  lowerCAmelCase_       :    Union[str, Any]								=			False
  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_       :    str								=			True
    lowerCAmelCase_       :    Optional[Any]								=			int(vit_name[-12:-10]						)
    lowerCAmelCase_       :    Dict								=			int(vit_name[-9:-6]						)
  else:
    lowerCAmelCase_       :    Dict								=			1000
    lowerCAmelCase_       :    Union[str, Any]								=			'huggingface/label-files'
    lowerCAmelCase_       :    Optional[int]								=			'imagenet-1k-id2label.json'
    lowerCAmelCase_       :    Union[str, Any]								=			json.load(open(hf_hub_download(lowerCAmelCase__   ,       lowerCAmelCase__   ,       repo_type='dataset'						)   ,       'r'						)						)
    lowerCAmelCase_       :    str								=			{int(lowerCAmelCase__						): v for k, v in idalabel.items()}
    lowerCAmelCase_       :    Optional[Any]								=			idalabel
    lowerCAmelCase_       :    Optional[int]								=			{v: k for k, v in idalabel.items()}
    lowerCAmelCase_       :    str								=			int(vit_name[-6:-4]						)
    lowerCAmelCase_       :    List[Any]								=			int(vit_name[-3:]						)
  # size of the architecture
  if "deit" in vit_name:
    if vit_name[9:].startswith('tiny'						):
      lowerCAmelCase_       :    Dict								=			192
      lowerCAmelCase_       :    List[str]								=			768
      lowerCAmelCase_       :    Optional[Any]								=			12
      lowerCAmelCase_       :    Tuple								=			3
    elif vit_name[9:].startswith('small'						):
      lowerCAmelCase_       :    Optional[Any]								=			384
      lowerCAmelCase_       :    Dict								=			1536
      lowerCAmelCase_       :    List[str]								=			12
      lowerCAmelCase_       :    str								=			6
    else:
      pass
  else:
    if vit_name[4:].startswith('small'						):
      lowerCAmelCase_       :    List[Any]								=			768
      lowerCAmelCase_       :    int								=			2304
      lowerCAmelCase_       :    Any								=			8
      lowerCAmelCase_       :    List[str]								=			8
    elif vit_name[4:].startswith('base'						):
      pass
    elif vit_name[4:].startswith('large'						):
      lowerCAmelCase_       :    List[Any]								=			1024
      lowerCAmelCase_       :    Union[str, Any]								=			4096
      lowerCAmelCase_       :    Dict								=			24
      lowerCAmelCase_       :    Any								=			16
    elif vit_name[4:].startswith('huge'						):
      lowerCAmelCase_       :    List[Any]								=			1280
      lowerCAmelCase_       :    str								=			5120
      lowerCAmelCase_       :    Any								=			32
      lowerCAmelCase_       :    Any								=			16

    # load original model from timm
  lowerCAmelCase_       :    Optional[int]								=			timm.create_model(lowerCAmelCase__   ,       pretrained=lowerCAmelCase__						)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_       :    Dict								=			timm_model.state_dict()
  if base_model:
    remove_classification_head_(lowerCAmelCase__						)
  lowerCAmelCase_       :    Optional[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
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_       :    int								=			ViTModel(lowerCAmelCase__						).eval()
  else:
    lowerCAmelCase_       :    str								=			ViTForImageClassification(lowerCAmelCase__						).eval()
  model.load_state_dict(lowerCAmelCase__						)

  # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
  if "deit" in vit_name:
    lowerCAmelCase_       :    Optional[Any]								=			DeiTImageProcessor(size=config.image_size						)
  else:
    lowerCAmelCase_       :    int								=			ViTImageProcessor(size=config.image_size						)
  lowerCAmelCase_       :    List[str]								=			image_processor(images=prepare_img()   ,       return_tensors='pt'						)
  lowerCAmelCase_       :    List[Any]								=			encoding['pixel_values']
  lowerCAmelCase_       :    int								=			model(lowerCAmelCase__						)

  if base_model:
    lowerCAmelCase_       :    Optional[Any]								=			timm_model.forward_features(lowerCAmelCase__						)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(lowerCAmelCase__   ,       outputs.pooler_output   ,       atol=1e-3						)
  else:
    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 {vit_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__":
     lowercase__		:    List[str]				   =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--vit_name""",
         default="""vit_base_patch16_224""",
         type=str,
         help="""Name of the ViT 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."""
     )

     lowercase__		:    Union[str, Any]				   =     parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










"""simple docstring"""







def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str						)						->       int:

  """simple docstring"""




  if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
    raise ValueError('String lengths must match!'						)

  lowerCAmelCase_       :    List[Any]								=			0

  for chara, chara in zip(lowerCAmelCase__   ,       lowerCAmelCase__						):
    if chara != chara:
      count += 1

  return count


if __name__ == "__main__":
     import doctest

     doctest.testmod()





