


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Tuple:
    if "cls_token" in name:
        _lowercase			:				int 						=						name.replace('cls_token'				,					'vit.embeddings.cls_token'					)
    if "mask_token" in name:
        _lowercase			:				List[str] 						=						name.replace('mask_token'				,					'decoder.mask_token'					)
    if "decoder_pos_embed" in name:
        _lowercase			:				int 						=						name.replace('decoder_pos_embed'				,					'decoder.decoder_pos_embed'					)
    if "pos_embed" in name and "decoder" not in name:
        _lowercase			:				int 						=						name.replace('pos_embed'				,					'vit.embeddings.position_embeddings'					)
    if "patch_embed.proj" in name:
        _lowercase			:				List[Any] 						=						name.replace('patch_embed.proj'				,					'vit.embeddings.patch_embeddings.projection'					)
    if "patch_embed.norm" in name:
        _lowercase			:				Optional[int] 						=						name.replace('patch_embed.norm'				,					'vit.embeddings.norm'					)
    if "decoder_blocks" in name:
        _lowercase			:				Optional[Any] 						=						name.replace('decoder_blocks'				,					'decoder.decoder_layers'					)
    if "blocks" in name:
        _lowercase			:				Optional[int] 						=						name.replace('blocks'				,					'vit.encoder.layer'					)
    if "attn.proj" in name:
        _lowercase			:				Dict 						=						name.replace('attn.proj'				,					'attention.output.dense'					)
    if "attn" in name:
        _lowercase			:				Any 						=						name.replace('attn'				,					'attention.self'					)
    if "norm1" in name:
        _lowercase			:				str 						=						name.replace('norm1'				,					'layernorm_before'					)
    if "norm2" in name:
        _lowercase			:				int 						=						name.replace('norm2'				,					'layernorm_after'					)
    if "mlp.fc1" in name:
        _lowercase			:				List[Any] 						=						name.replace('mlp.fc1'				,					'intermediate.dense'					)
    if "mlp.fc2" in name:
        _lowercase			:				List[str] 						=						name.replace('mlp.fc2'				,					'output.dense'					)
    if "decoder_embed" in name:
        _lowercase			:				Any 						=						name.replace('decoder_embed'				,					'decoder.decoder_embed'					)
    if "decoder_norm" in name:
        _lowercase			:				Dict 						=						name.replace('decoder_norm'				,					'decoder.decoder_norm'					)
    if "decoder_pred" in name:
        _lowercase			:				List[Any] 						=						name.replace('decoder_pred'				,					'decoder.decoder_pred'					)
    if "norm.weight" in name and "decoder" not in name:
        _lowercase			:				str 						=						name.replace('norm.weight'				,					'vit.layernorm.weight'					)
    if "norm.bias" in name and "decoder" not in name:
        _lowercase			:				Tuple 						=						name.replace('norm.bias'				,					'vit.layernorm.bias'					)

    return name



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        _lowercase			:				List[Any] 						=						orig_state_dict.pop(lowerCamelCase_					)

        if "qkv" in key:
            _lowercase			:				Dict 						=						key.split('.'					)
            _lowercase			:				Any 						=						int(key_split[1]					)
            if "decoder_blocks" in key:
                _lowercase			:				int 						=						config.decoder_hidden_size
                _lowercase			:				Any 						=						'decoder.decoder_layers.'
                if "weight" in key:
                    _lowercase			:				str 						=						val[:dim, :]
                    _lowercase			:				int 						=						val[dim : dim * 2, :]
                    _lowercase			:				int 						=						val[-dim:, :]
                elif "bias" in key:
                    _lowercase			:				Optional[int] 						=						val[:dim]
                    _lowercase			:				Union[str, Any] 						=						val[dim : dim * 2]
                    _lowercase			:				str 						=						val[-dim:]
            else:
                _lowercase			:				Any 						=						config.hidden_size
                _lowercase			:				Union[str, Any] 						=						'vit.encoder.layer.'
                if "weight" in key:
                    _lowercase			:				Tuple 						=						val[:dim, :]
                    _lowercase			:				Union[str, Any] 						=						val[dim : dim * 2, :]
                    _lowercase			:				Dict 						=						val[-dim:, :]
                elif "bias" in key:
                    _lowercase			:				int 						=						val[:dim]
                    _lowercase			:				Tuple 						=						val[dim : dim * 2]
                    _lowercase			:				Tuple 						=						val[-dim:]

        else:
            _lowercase			:				str 						=						val

    return orig_state_dict



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_					)       ->		str:
    _lowercase			:				str 						=						ViTMAEConfig()
    if "large" in checkpoint_url:
        _lowercase			:				Dict 						=						1024
        _lowercase			:				List[str] 						=						4096
        _lowercase			:				str 						=						24
        _lowercase			:				Optional[Any] 						=						16
    elif "huge" in checkpoint_url:
        _lowercase			:				int 						=						14
        _lowercase			:				Union[str, Any] 						=						1280
        _lowercase			:				Union[str, Any] 						=						5120
        _lowercase			:				Tuple 						=						32
        _lowercase			:				Optional[Any] 						=						16

    _lowercase			:				Union[str, Any] 						=						ViTMAEForPreTraining(lowerCamelCase_					)

    _lowercase			:				Dict 						=						torch.hub.load_state_dict_from_url(lowerCamelCase_				,					map_location='cpu'					)['model']

    _lowercase			:				Union[str, Any] 						=						ViTMAEImageProcessor(size=config.image_size					)

    _lowercase			:				Optional[Any] 						=						convert_state_dict(lowerCamelCase_				,					lowerCamelCase_					)

    model.load_state_dict(lowerCamelCase_					)
    model.eval()

    _lowercase			:				Optional[Any] 						=						'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

    _lowercase			:				Dict 						=						Image.open(requests.get(lowerCamelCase_				,					stream=lowerCamelCase_					).raw					)
    _lowercase			:				List[str] 						=						ViTMAEImageProcessor(size=config.image_size					)
    _lowercase			:				str 						=						image_processor(images=lowerCamelCase_				,					return_tensors='pt'					)

    # forward pass
    torch.manual_seed(2					)
    _lowercase			:				str 						=						model(**lowerCamelCase_					)
    _lowercase			:				Optional[int] 						=						outputs.logits

    if "large" in checkpoint_url:
        _lowercase			:				Union[str, Any] 						=						torch.tensor(
            [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]					)
    elif "huge" in checkpoint_url:
        _lowercase			:				Any 						=						torch.tensor(
            [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]					)
    else:
        _lowercase			:				Optional[int] 						=						torch.tensor(
            [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]					)

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

    print(F'''Saving model 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__":
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
     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			: Dict				   =					parser.parse_args()
 convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return math.sqrt(sum(pow(a - b  ,     2						) for a, b in zip(__snake_case  ,     __snake_case						)						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):

					if dataset.ndim != value_array.ndim:
										_UpperCamelCase			     =     (
										    '''Wrong input data\'s dimensions... '''
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(__snake_case						)

					try:
										if dataset.shape[1] != value_array.shape[1]:
															_UpperCamelCase			     =     (
															    '''Wrong input data\'s shape... '''
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(__snake_case						)
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError('''Wrong shape'''						)

					if dataset.dtype != value_array.dtype:
										_UpperCamelCase			     =     (
										    '''Input data have different datatype... '''
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(__snake_case						)

					_UpperCamelCase			     =     []

					for value in value_array:
										_UpperCamelCase			     =     euclidean(__snake_case  ,     dataset[0]						)
										_UpperCamelCase			     =     dataset[0].tolist()

										for dataset_value in dataset[1:]:
															_UpperCamelCase			     =     euclidean(__snake_case  ,     __snake_case						)

															if dist > temp_dist:
																				_UpperCamelCase			     =     temp_dist
																				_UpperCamelCase			     =     dataset_value.tolist()

										answer.append([vector, dist]						)

					return answer
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return np.dot(__snake_case  ,     __snake_case						) / (norm(__snake_case						) * norm(__snake_case						))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
