





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

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


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger(__name__)




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=False  )     ->		List[Any]:
       UpperCamelCase_          =  []
       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"),
                  ]  )

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              UpperCamelCase_          =  [(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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=False  )     ->		Dict:
       for i in range(config.num_hidden_layers  ):
              if base_model:
                     UpperCamelCase_          =  ""
              else:
                     UpperCamelCase_          =  "vit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              UpperCamelCase_          =  state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''  )
              UpperCamelCase_          =  state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''  )
              # next, add query, keys and values (in that order) to the state dict
              UpperCamelCase_          =  in_proj_weight[
                  : config.hidden_size, :
              ]
              UpperCamelCase_          =  in_proj_bias[: config.hidden_size]
              UpperCamelCase_          =  in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCamelCase_          =  in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              UpperCamelCase_          =  in_proj_weight[
                  -config.hidden_size :, :
              ]
              UpperCamelCase_          =  in_proj_bias[-config.hidden_size :]




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  ["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(UpperCamelCase_					,		UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[Any]:
       UpperCamelCase_          =  dct.pop(UpperCamelCase_  )
       UpperCamelCase_          =  val




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




@torch.no_grad()
def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=True  )     ->		Optional[int]:
       UpperCamelCase_          =  ViTConfig()
       # patch_size
       if model_name[-1] == "8":
              UpperCamelCase_          =  8
       # set labels if required
       if not base_model:
              UpperCamelCase_          =  1000
              UpperCamelCase_          =  "huggingface/label-files"
              UpperCamelCase_          =  "imagenet-1k-id2label.json"
              UpperCamelCase_          =  json.load(open(hf_hub_download(UpperCamelCase_					,		UpperCamelCase_					,		repo_type="dataset"  )					,		"r"  )  )
              UpperCamelCase_          =  {int(UpperCamelCase_  ): v for k, v in idalabel.items()}
              UpperCamelCase_          =  idalabel
              UpperCamelCase_          =  {v: k for k, v in idalabel.items()}
       # size of the architecture
       if model_name in ["dino_vits8", "dino_vits16"]:
              UpperCamelCase_          =  384
              UpperCamelCase_          =  1536
              UpperCamelCase_          =  12
              UpperCamelCase_          =  6

       # load original model from torch hub
       UpperCamelCase_          =  torch.hub.load("facebookresearch/dino:main"					,		UpperCamelCase_  )
       original_model.eval()

       # load state_dict of original model, remove and rename some keys
       UpperCamelCase_          =  original_model.state_dict()
       if base_model:
              remove_classification_head_(UpperCamelCase_  )
       UpperCamelCase_          =  create_rename_keys(UpperCamelCase_					,		base_model=UpperCamelCase_  )
       for src, dest in rename_keys:
              rename_key(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )
       read_in_q_k_v(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )

       # load HuggingFace model
       if base_model:
              UpperCamelCase_          =  ViTModel(UpperCamelCase_					,		add_pooling_layer=UpperCamelCase_  ).eval()
       else:
              UpperCamelCase_          =  ViTForImageClassification(UpperCamelCase_  ).eval()
       model.load_state_dict(UpperCamelCase_  )

       # Check outputs on an image, prepared by ViTImageProcessor
       UpperCamelCase_          =  ViTImageProcessor()
       UpperCamelCase_          =  image_processor(images=prepare_img()					,		return_tensors="pt"  )
       UpperCamelCase_          =  encoding["pixel_values"]
       UpperCamelCase_          =  model(UpperCamelCase_  )

       if base_model:
              UpperCamelCase_          =  original_model(UpperCamelCase_  )
              assert torch.allclose(UpperCamelCase_					,		outputs.last_hidden_state[:, 0, :]					,		atol=1e-1  )
       else:
              UpperCamelCase_          =  original_model(UpperCamelCase_  )
              assert logits.shape == outputs.logits.shape
              assert torch.allclose(UpperCamelCase_					,		outputs.logits					,		atol=1e-3  )

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


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='dino_vitb16',
         type=str,
         help='Name of the model trained with DINO you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--base_model',
         action='store_true',
         help='Whether to only convert the base model (no projection head weights).',
     )

     parser.set_defaults(base_model=True)
     _UpperCAmelCase        =				parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)








_UpperCAmelCase        =				{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_UpperCAmelCase        =				{value: key for key, value in encode_dict.items()}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("encode() accepts only letters of the alphabet and spaces"  )
       return encoded




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       if set(UpperCamelCase_  ) - {"A", "B", " "} != set():
              raise Exception("decode() accepts only 'A', 'B' and spaces"  )
       UpperCamelCase_          =  ""
       for word in coded.split():
              while len(UpperCamelCase_  ) != 0:
                     decoded += decode_dict[word[:5]]
                     UpperCamelCase_          =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

