





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()
_A :   Union[str, Any]  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """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"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"
      lowerCamelCase__        =							[(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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    Dict:







   """simple docstring"""
   for i in range(config.num_hidden_layers					):
      if base_model:
         lowerCamelCase__        =							""
      else:
         lowerCamelCase__        =							"vit."
      # 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 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__        =							["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(UpperCAmelCase      ,  UpperCAmelCase					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    str:







   """simple docstring"""
   lowerCamelCase__        =							dct.pop(UpperCAmelCase					)
   lowerCamelCase__        =							val



def 	_a						(   )    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__        =							"http://images.cocodataset.org/val2017/000000039769.jpg"
   lowerCamelCase__        =							Image.open(requests.get(UpperCAmelCase      ,  stream=UpperCAmelCase					).raw					)
   return im



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=True					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__        =							ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      lowerCamelCase__        =							8
   # set labels if required
   if not base_model:
      lowerCamelCase__        =							1000
      lowerCamelCase__        =							"huggingface/label-files"
      lowerCamelCase__        =							"imagenet-1k-id2label.json"
      lowerCamelCase__        =							json.load(open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					)					)
      lowerCamelCase__        =							{int(UpperCAmelCase					): v for k, v in idalabel.items()}
      lowerCamelCase__        =							idalabel
      lowerCamelCase__        =							{v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      lowerCamelCase__        =							384
      lowerCamelCase__        =							1536
      lowerCamelCase__        =							12
      lowerCamelCase__        =							6

   # load original model from torch hub
   lowerCamelCase__        =							torch.hub.load('''facebookresearch/dino:main'''      ,  UpperCAmelCase					)
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   lowerCamelCase__        =							original_model.state_dict()
   if base_model:
      remove_classification_head_(UpperCAmelCase					)
   lowerCamelCase__        =							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:
      lowerCamelCase__        =							ViTModel(UpperCAmelCase      ,  add_pooling_layer=UpperCAmelCase					).eval()
   else:
      lowerCamelCase__        =							ViTForImageClassification(UpperCAmelCase					).eval()
   model.load_state_dict(UpperCAmelCase					)

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

   if base_model:
      lowerCamelCase__        =							original_model(UpperCAmelCase					)
      assert torch.allclose(UpperCAmelCase      ,  outputs.last_hidden_state[:, 0, :]      ,  atol=1E-1					)
   else:
      lowerCamelCase__        =							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__":
       _A :   Tuple  			=  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)
       _A :   Optional[Any]  			=  parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if b == 0:
      return 1
   if (b % 2) == 0:
      return actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)
   else:
      return a * actual_power(UpperCAmelCase      ,  int(b / 2					)					) * actual_power(UpperCAmelCase      ,  int(b / 2					)					)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    float:







   """simple docstring"""
   if b < 0:
      return 1 / actual_power(UpperCAmelCase      ,  UpperCAmelCase					)
   return actual_power(UpperCAmelCase      ,  UpperCAmelCase					)


if __name__ == "__main__":
       print(power(-2, -3))



