



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
__a      :       str				  =   ""
__a      :       List[Any]				  =   ""
__a      :       Union[str, Any]				  =   ""
__a      :       Optional[Any]				  =   1  # (0 is vertical, 1 is horizontal)



def 			_SCREAMING_SNAKE_CASE						( )						->      None:





    """simple docstring"""

    __A    ,	__A         =	get_dataset(__lowercase			,				__lowercase    )
    print("""Processing..."""    )
    __A    ,	__A    ,	__A         =	update_image_and_anno(__lowercase			,				__lowercase			,				__lowercase    )

    for index, image in enumerate(__lowercase    ):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __A         =	random_chars(3_2    )
        __A         =	paths[index].split(os.sep    )[-1].rsplit("""."""			,				1    )[0]
        __A         =	f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
        cva.imwrite(f"/{file_root}.jpg"			,				__lowercase			,				[cva.IMWRITE_JPEG_QUALITY, 8_5]    )
        print(f"Success {index+1}/{len(__lowercase    )} with {file_name}"    )
        __A         =	[]
        for anno in new_annos[index]:
            __A         =	f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
            annos_list.append(__lowercase    )
        with open(f"/{file_root}.txt"			,				"""w"""    ) as outfile:
            outfile.write("""\n""".join(line for line in annos_list    )    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			str    )						->      tuple[list, list]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    for label_file in glob.glob(os.path.join(__lowercase			,				"""*.txt"""    )    ):
        __A         =	label_file.split(os.sep    )[-1].rsplit("""."""			,				1    )[0]
        with open(__lowercase    ) as in_file:
            __A         =	in_file.readlines()
        __A         =	os.path.join(__lowercase			,				f"{label_name}.jpg"    )

        __A         =	[]
        for obj_list in obj_lists:
            __A         =	obj_list.rstrip("""\n"""    ).split(""" """    )
            boxes.append(
                [
                    int(obj[0]    ),
                    float(obj[1]    ),
                    float(obj[2]    ),
                    float(obj[3]    ),
                    float(obj[4]    ),
                ]    )
        if not boxes:
            continue
        img_paths.append(__lowercase    )
        labels.append(__lowercase    )
    return img_paths, labels



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			list			,				__lowercase   :			list			,				__lowercase   :			int = 1    )						->      tuple[list, list, list]:





    """simple docstring"""

    __A         =	[]
    __A         =	[]
    __A         =	[]
    for idx in range(len(__lowercase    )    ):
        __A         =	[]
        __A         =	img_list[idx]
        path_list.append(__lowercase    )
        __A         =	anno_list[idx]
        __A         =	cva.imread(__lowercase    )
        if flip_type == 1:
            __A         =	cva.flip(__lowercase			,				__lowercase    )
            for bbox in img_annos:
                __A         =	1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
        elif flip_type == 0:
            __A         =	cva.flip(__lowercase			,				__lowercase    )
            for bbox in img_annos:
                __A         =	1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]    )
        new_annos_lists.append(__lowercase    )
        new_imgs_list.append(__lowercase    )
    return new_imgs_list, new_annos_lists, path_list



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int = 3_2    )						->      str:





    """simple docstring"""

    assert number_char > 1, "The number of character should greater than 1"
    __A         =	ascii_lowercase + digits
    return "".join(random.choice(__lowercase    ) for _ in range(__lowercase    )    )


if __name__ == "__main__":
  main()
  print("DONE ✅")












import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      str:





    """simple docstring"""

    if "cls_token" in name:
        __A         =	name.replace("""cls_token"""			,				"""vit.embeddings.cls_token"""    )
    if "mask_token" in name:
        __A         =	name.replace("""mask_token"""			,				"""decoder.mask_token"""    )
    if "decoder_pos_embed" in name:
        __A         =	name.replace("""decoder_pos_embed"""			,				"""decoder.decoder_pos_embed"""    )
    if "pos_embed" in name and "decoder" not in name:
        __A         =	name.replace("""pos_embed"""			,				"""vit.embeddings.position_embeddings"""    )
    if "patch_embed.proj" in name:
        __A         =	name.replace("""patch_embed.proj"""			,				"""vit.embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        __A         =	name.replace("""patch_embed.norm"""			,				"""vit.embeddings.norm"""    )
    if "decoder_blocks" in name:
        __A         =	name.replace("""decoder_blocks"""			,				"""decoder.decoder_layers"""    )
    if "blocks" in name:
        __A         =	name.replace("""blocks"""			,				"""vit.encoder.layer"""    )
    if "attn.proj" in name:
        __A         =	name.replace("""attn.proj"""			,				"""attention.output.dense"""    )
    if "attn" in name:
        __A         =	name.replace("""attn"""			,				"""attention.self"""    )
    if "norm1" in name:
        __A         =	name.replace("""norm1"""			,				"""layernorm_before"""    )
    if "norm2" in name:
        __A         =	name.replace("""norm2"""			,				"""layernorm_after"""    )
    if "mlp.fc1" in name:
        __A         =	name.replace("""mlp.fc1"""			,				"""intermediate.dense"""    )
    if "mlp.fc2" in name:
        __A         =	name.replace("""mlp.fc2"""			,				"""output.dense"""    )
    if "decoder_embed" in name:
        __A         =	name.replace("""decoder_embed"""			,				"""decoder.decoder_embed"""    )
    if "decoder_norm" in name:
        __A         =	name.replace("""decoder_norm"""			,				"""decoder.decoder_norm"""    )
    if "decoder_pred" in name:
        __A         =	name.replace("""decoder_pred"""			,				"""decoder.decoder_pred"""    )
    if "norm.weight" in name and "decoder" not in name:
        __A         =	name.replace("""norm.weight"""			,				"""vit.layernorm.weight"""    )
    if "norm.bias" in name and "decoder" not in name:
        __A         =	name.replace("""norm.bias"""			,				"""vit.layernorm.bias"""    )

    return name



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Dict    )						->      str:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A         =	orig_state_dict.pop(__lowercase    )

        if "qkv" in key:
            __A         =	key.split("""."""    )
            __A         =	int(key_split[1]    )
            if "decoder_blocks" in key:
                __A         =	config.decoder_hidden_size
                __A         =	"""decoder.decoder_layers."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]
            else:
                __A         =	config.hidden_size
                __A         =	"""vit.encoder.layer."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]

        else:
            __A         =	val

    return orig_state_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			str    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	ViTMAEConfig()
    if "large" in checkpoint_url:
        __A         =	1_0_2_4
        __A         =	4_0_9_6
        __A         =	2_4
        __A         =	1_6
    elif "huge" in checkpoint_url:
        __A         =	1_4
        __A         =	1_2_8_0
        __A         =	5_1_2_0
        __A         =	3_2
        __A         =	1_6

    __A         =	ViTMAEForPreTraining(__lowercase    )

    __A         =	torch.hub.load_state_dict_from_url(__lowercase			,				map_location="""cpu"""    )["""model"""]

    __A         =	ViTMAEImageProcessor(size=config.image_size    )

    __A         =	convert_state_dict(__lowercase			,				__lowercase    )

    model.load_state_dict(__lowercase    )
    model.eval()

    __A         =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

    __A         =	Image.open(requests.get(__lowercase			,				stream=__lowercase    ).raw    )
    __A         =	ViTMAEImageProcessor(size=config.image_size    )
    __A         =	image_processor(images=__lowercase			,				return_tensors="""pt"""    )

    # forward pass
    torch.manual_seed(2    )
    __A         =	model(**__lowercase    )
    __A         =	outputs.logits

    if "large" in checkpoint_url:
        __A         =	torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
    elif "huge" in checkpoint_url:
        __A         =	torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
    else:
        __A         =	torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

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

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

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


if __name__ == "__main__":
  __a      :       List[str]				  =   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."
  )

  __a      :       List[str]				  =   parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




