



"""simple docstring"""
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()
_lowerCAmelCase    :Tuple     =					logging.get_logger(__name__)





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





def  lowerCamelCase_						(UpperCamelCase__		:		Any							):
    _UpperCAmelCase							:  Dict											=    ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase__     ,       UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		List[str]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		List[Any]							):
    _UpperCAmelCase							:  List[Any]											=    dct.pop(UpperCamelCase__							)
    _UpperCAmelCase							:  Union[str, Any]											=    val





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Tuple											=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _UpperCAmelCase							:  int											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							)
    return im





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

    # load original model from torch hub
    _UpperCAmelCase							:  Optional[Any]											=    torch.hub.load('''facebookresearch/dino:main'''     ,       UpperCamelCase__							)
    original_model.eval()

    # load state_dict of original model, remove and rename some keys
    _UpperCAmelCase							:  List[Any]											=    original_model.state_dict()
    if base_model:
        remove_classification_head_(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    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							:  int											=    ViTModel(UpperCamelCase__     ,       add_pooling_layer=UpperCamelCase__							).eval()
    else:
        _UpperCAmelCase							:  List[Any]											=    ViTForImageClassification(UpperCamelCase__							).eval()
    model.load_state_dict(UpperCamelCase__							)

    # Check outputs on an image, prepared by ViTImageProcessor
    _UpperCAmelCase							:  Tuple											=    ViTImageProcessor()
    _UpperCAmelCase							:  Dict											=    image_processor(images=prepare_img()     ,       return_tensors='''pt'''							)
    _UpperCAmelCase							:  int											=    encoding['''pixel_values''']
    _UpperCAmelCase							:  Tuple											=    model(UpperCamelCase__							)

    if base_model:
        _UpperCAmelCase							:  Optional[int]											=    original_model(UpperCamelCase__							)
        assert torch.allclose(UpperCamelCase__     ,       outputs.last_hidden_state[:, 0, :]     ,       atol=1E-1							)
    else:
        _UpperCAmelCase							:  Dict											=    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__":
  _lowerCAmelCase    :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)
  _lowerCAmelCase    :Union[str, Any]     =					parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







"""simple docstring"""
from __future__ import annotations

import math





def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		bool     ,       UpperCamelCase__		:		list[int]     ,       UpperCamelCase__		:		float							):

    if depth < 0:
        raise ValueError('''Depth cannot be less than 0'''							)

    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Scores cannot be empty'''							)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )

    return min(
        minimax(depth + 1     ,       node_index * 2     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       minimax(depth + 1     ,       node_index * 2 + 1     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)     ,       )





def  lowerCamelCase_						():
    _UpperCAmelCase							:  Any											=    [90, 23, 6, 33, 21, 65, 123, 3_4423]
    _UpperCAmelCase							:  Any											=    math.log(len(UpperCamelCase__							)     ,       2							)
    print('''Optimal value : '''     ,       end=''''''							)
    print(minimax(0     ,       0     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()


