

import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						Optional[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						[]
    for i in range(config.num_hidden_layers			):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"module.blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")			)
        rename_keys.append((f"module.blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")			)
        rename_keys.append(
            (f"module.blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")			)
        rename_keys.append((f"module.blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")			)
        rename_keys.append((f"module.blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")			)
        rename_keys.append((f"module.blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")			)
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")			)
        rename_keys.append((f"module.blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")			)
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")			)
        rename_keys.append((f"module.blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")			)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ("module.cls_token", "vit.embeddings.cls_token"),
            ("module.patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
            ("module.patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
            ("module.pos_embed", "vit.embeddings.position_embeddings"),
        ]			)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("module.norm.weight", "layernorm.weight"),
                ("module.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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=False			)->						str:



    """simple docstring"""






    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"module.blocks.{i}.attn.qkv.weight"			)
        UpperCamelCase_			  =						state_dict.pop(f"module.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(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[
        "module.fc.fc1.weight",
        "module.fc.fc1.bias",
        "module.fc.bn1.weight",
        "module.fc.bn1.bias",
        "module.fc.bn1.running_mean",
        "module.fc.bn1.running_var",
        "module.fc.bn1.num_batches_tracked",
        "module.fc.fc2.weight",
        "module.fc.fc2.bias",
        "module.fc.bn2.weight",
        "module.fc.bn2.bias",
        "module.fc.bn2.running_mean",
        "module.fc.bn2.running_var",
        "module.fc.bn2.num_batches_tracked",
        "module.fc.fc3.weight",
        "module.fc.fc3.bias",
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						dct.pop(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						val




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						ViTMSNConfig()
    UpperCamelCase_			  =						1_0_0_0

    UpperCamelCase_			  =						"datasets/huggingface/label-files"
    UpperCamelCase_			  =						"imagenet-1k-id2label.json"
    UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						"r"			)			)
    UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
    UpperCamelCase_			  =						idalabel
    UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    if "s16" in checkpoint_url:
        UpperCamelCase_			  =						3_8_4
        UpperCamelCase_			  =						1_5_3_6
        UpperCamelCase_			  =						6
    elif "l16" in checkpoint_url:
        UpperCamelCase_			  =						1_0_2_4
        UpperCamelCase_			  =						4_0_9_6
        UpperCamelCase_			  =						2_4
        UpperCamelCase_			  =						1_6
        UpperCamelCase_			  =						0.1
    elif "b4" in checkpoint_url:
        UpperCamelCase_			  =						4
    elif "l7" in checkpoint_url:
        UpperCamelCase_			  =						7
        UpperCamelCase_			  =						1_0_2_4
        UpperCamelCase_			  =						4_0_9_6
        UpperCamelCase_			  =						2_4
        UpperCamelCase_			  =						1_6
        UpperCamelCase_			  =						0.1

    UpperCamelCase_			  =						ViTMSNModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_			,						map_location="cpu"			)["target_encoder"]

    UpperCamelCase_			  =						ViTImageProcessor(size=config.image_size			)

    remove_projection_head(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						create_rename_keys(SCREAMING_SNAKE_CASE_			,						base_model=SCREAMING_SNAKE_CASE_			)

    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    read_in_q_k_v(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						base_model=SCREAMING_SNAKE_CASE_			)

    model.load_state_dict(SCREAMING_SNAKE_CASE_			)
    model.eval()

    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"

    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    UpperCamelCase_			  =						ViTImageProcessor(
        size=config.image_size			,						image_mean=SCREAMING_SNAKE_CASE_			,						image_std=SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						image_processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    # forward pass
    torch.manual_seed(2			)
    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						outputs.last_hidden_state

    # The following Colab Notebook was used to generate these outputs:
    # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
    if "s16" in checkpoint_url:
        UpperCamelCase_			  =						torch.tensor([[-1.0915, -1.4876, -1.1809]]			)
    elif "b16" in checkpoint_url:
        UpperCamelCase_			  =						torch.tensor([[14.2889, -18.9045, 11.7281]]			)
    elif "l16" in checkpoint_url:
        UpperCamelCase_			  =						torch.tensor([[41.5028, -22.8681, 45.6475]]			)
    elif "b4" in checkpoint_url:
        UpperCamelCase_			  =						torch.tensor([[-4.3868, 5.2932, -0.4137]]			)
    else:
        UpperCamelCase_			  =						torch.tensor([[-0.1792, -0.6465, 2.4263]]			)

    # verify logits
    assert torch.allclose(last_hidden_state[:, 0, :3]			,						SCREAMING_SNAKE_CASE_			,						atol=1E-4			)

    print(f"Saving model to {pytorch_dump_folder_path}"			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    print(f"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Any						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--checkpoint_url""",
     default="""https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar""",
     type=str,
     help="""URL of the checkpoint you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     parser.parse_args()
 convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



from __future__ import annotations

import math




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






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

    if len(SCREAMING_SNAKE_CASE_			) == 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			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						minimax(depth + 1			,						node_index * 2 + 1			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						)

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




def 			lowerCAmelCase(      )->						None:



    """simple docstring"""






    UpperCamelCase_			  =						[9_0, 2_3, 6, 3_3, 2_1, 6_5, 1_2_3, 3_4_4_2_3]
    UpperCamelCase_			  =						math.log(len(SCREAMING_SNAKE_CASE_			)			,						2			)
    print("Optimal value : "			,						end=""			)
    print(minimax(0			,						0			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()
