'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



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

    return name







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	Tuple			):




    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        __UpperCamelCase	:	Optional[Any]		    =  orig_state_dict.pop(_a			)

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

        else:
            __UpperCamelCase	:	Tuple		    =  val

    return orig_state_dict







def  _UpperCamelCase (     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  ViTMAEConfig()
    if "large" in checkpoint_url:
        __UpperCamelCase	:	Dict		    =  1_0_2_4
        __UpperCamelCase	:	Tuple		    =  4_0_9_6
        __UpperCamelCase	:	Tuple		    =  2_4
        __UpperCamelCase	:	Any		    =  1_6
    elif "huge" in checkpoint_url:
        __UpperCamelCase	:	List[Any]		    =  1_4
        __UpperCamelCase	:	Dict		    =  1_2_8_0
        __UpperCamelCase	:	str		    =  5_1_2_0
        __UpperCamelCase	:	List[str]		    =  3_2
        __UpperCamelCase	:	str		    =  1_6

    __UpperCamelCase	:	str		    =  ViTMAEForPreTraining(_a			)

    __UpperCamelCase	:	Optional[Any]		    =  torch.hub.load_state_dict_from_url(_a				,     map_location='cpu'			)['model']

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

    __UpperCamelCase	:	List[Any]		    =  convert_state_dict(_a				,     _a			)

    model.load_state_dict(_a			)
    model.eval()

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

    __UpperCamelCase	:	Union[str, Any]		    =  Image.open(requests.get(_a				,     stream=_a			).raw			)
    __UpperCamelCase	:	Any		    =  ViTMAEImageProcessor(size=config.image_size			)
    __UpperCamelCase	:	List[str]		    =  image_processor(images=_a				,     return_tensors='pt'			)

    # forward pass
    torch.manual_seed(2			)
    __UpperCamelCase	:	Optional[int]		    =  model(**_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  outputs.logits

    if "large" in checkpoint_url:
        __UpperCamelCase	:	Union[str, Any]		    =  torch.tensor(
            [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]			)
    elif "huge" in checkpoint_url:
        __UpperCamelCase	:	Optional[int]		    =  torch.tensor(
            [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]			)
    else:
        __UpperCamelCase	:	Optional[Any]		    =  torch.tensor(
            [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]			)

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

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

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


if __name__ == "__main__":
  a=  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=  parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
a=  ''''''
a=  ''''''
a=  ''''''
a=  1  # (0 is vertical, 1 is horizontal)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

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







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	Union[str, Any]		    =  []
    for label_file in glob.glob(os.path.join(_a				,     '*.txt'			)			):
        __UpperCamelCase	:	Any		    =  label_file.split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        with open(_a			) as in_file:
            __UpperCamelCase	:	Tuple		    =  in_file.readlines()
        __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     f"""{label_name}.jpg"""			)

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[Any]		    =  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(_a			)
        labels.append(_a			)
    return img_paths, labels







def  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
        new_annos_lists.append(_a			)
        new_imgs_list.append(_a			)
    return new_imgs_list, new_annos_lists, path_list







def  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase	:	Optional[Any]		    =  ascii_lowercase + digits
    return "".join(random.choice(_a			) for _ in range(_a			)			)


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



