





import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase				:	List[str]      						=  logging.get_logger(__name__)



def A_     (		A__		,				A__=False    )  ->		Optional[int]:
       a__						:     List[str]			=    []
       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'deit.encoder.layer.{i}.layernorm_before.weight')    )
              rename_keys.append((F'blocks.{i}.norm1.bias', F'deit.encoder.layer.{i}.layernorm_before.bias')    )
              rename_keys.append((F'blocks.{i}.attn.proj.weight', F'deit.encoder.layer.{i}.attention.output.dense.weight')    )
              rename_keys.append((F'blocks.{i}.attn.proj.bias', F'deit.encoder.layer.{i}.attention.output.dense.bias')    )
              rename_keys.append((F'blocks.{i}.norm2.weight', F'deit.encoder.layer.{i}.layernorm_after.weight')    )
              rename_keys.append((F'blocks.{i}.norm2.bias', F'deit.encoder.layer.{i}.layernorm_after.bias')    )
              rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'deit.encoder.layer.{i}.intermediate.dense.weight')    )
              rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'deit.encoder.layer.{i}.intermediate.dense.bias')    )
              rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'deit.encoder.layer.{i}.output.dense.weight')    )
              rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'deit.encoder.layer.{i}.output.dense.bias')    )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               ('cls_token', 'deit.embeddings.cls_token'),
               ('dist_token', 'deit.embeddings.distillation_token'),
               ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
               ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
               ('pos_embed', 'deit.embeddings.position_embeddings'),
           ]    )

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('norm.weight', 'layernorm.weight'),
                      ('norm.bias', 'layernorm.bias'),
                      ('pre_logits.fc.weight', 'pooler.dense.weight'),
                      ('pre_logits.fc.bias', 'pooler.dense.bias'),
                  ]    )

              # if just the base model, we should remove "deit" from all keys that start with "deit"
              a__						:     Tuple			=    [(pair[0], pair[1][4:]) if pair[1].startswith('deit'    ) else pair for pair in rename_keys]
       else:
              # layernorm + classification heads
              rename_keys.extend(
                  [
                      ('norm.weight', 'deit.layernorm.weight'),
                      ('norm.bias', 'deit.layernorm.bias'),
                      ('head.weight', 'cls_classifier.weight'),
                      ('head.bias', 'cls_classifier.bias'),
                      ('head_dist.weight', 'distillation_classifier.weight'),
                      ('head_dist.bias', 'distillation_classifier.bias'),
                  ]    )

       return rename_keys



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



def A_     (		A__		,				A__		,				A__    )  ->		Tuple:
       a__						:     int			=    dct.pop(A__    )
       a__						:     int			=    val



def A_     (		)  ->		Optional[Any]:
       a__						:     List[str]			=    'http://images.cocodataset.org/val2017/000000039769.jpg'
       a__						:     Union[str, Any]			=    Image.open(requests.get(A__		,				stream=A__    ).raw    )
       return im



@torch.no_grad()
def A_     (		A__		,				A__    )  ->		Optional[Any]:
       a__						:     Optional[int]			=    DeiTConfig()
       # all deit models have fine-tuned heads
       a__						:     Dict			=    False
       # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
       a__						:     List[Any]			=    1000
       a__						:     List[str]			=    'huggingface/label-files'
       a__						:     str			=    'imagenet-1k-id2label.json'
       a__						:     Optional[int]			=    json.load(open(hf_hub_download(A__		,				A__		,				repo_type='dataset'    )		,				'r'    )    )
       a__						:     Tuple			=    {int(A__    ): v for k, v in idalabel.items()}
       a__						:     Dict			=    idalabel
       a__						:     List[Any]			=    {v: k for k, v in idalabel.items()}
       a__						:     str			=    int(deit_name[-6:-4]    )
       a__						:     List[str]			=    int(deit_name[-3:]    )
       # size of the architecture
       if deit_name[9:].startswith('tiny'    ):
              a__						:     Tuple			=    192
              a__						:     str			=    768
              a__						:     Any			=    12
              a__						:     Tuple			=    3
       elif deit_name[9:].startswith('small'    ):
              a__						:     Any			=    384
              a__						:     List[str]			=    1536
              a__						:     List[str]			=    12
              a__						:     List[str]			=    6
       if deit_name[9:].startswith('base'    ):
              pass
       elif deit_name[4:].startswith('large'    ):
              a__						:     Tuple			=    1024
              a__						:     Tuple			=    4096
              a__						:     Dict			=    24
              a__						:     str			=    16

       # load original model from timm
       a__						:     Dict			=    timm.create_model(A__		,				pretrained=A__    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       a__						:     List[Any]			=    timm_model.state_dict()
       a__						:     Optional[Any]			=    create_rename_keys(A__		,				A__    )
       for src, dest in rename_keys:
              rename_key(A__		,				A__		,				A__    )
       read_in_q_k_v(A__		,				A__		,				A__    )

       # load HuggingFace model
       a__						:     Union[str, Any]			=    DeiTForImageClassificationWithTeacher(A__    ).eval()
       model.load_state_dict(A__    )

       # Check outputs on an image, prepared by DeiTImageProcessor
       a__						:     int			=    int(
           (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
       a__						:     Any			=    DeiTImageProcessor(size=A__		,				crop_size=config.image_size    )
       a__						:     Dict			=    image_processor(images=prepare_img()		,				return_tensors='pt'    )
       a__						:     Tuple			=    encoding['pixel_values']
       a__						:     Optional[int]			=    model(A__    )

       a__						:     str			=    timm_model(A__    )
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(A__		,				outputs.logits		,				atol=1E-3    )

       Path(A__    ).mkdir(exist_ok=A__    )
       print(F'Saving model {deit_name} 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__":
    lowercase				:	Union[str, Any]      						=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--deit_name""",
        default="""vit_deit_base_distilled_patch16_224""",
        type=str,
        help="""Name of the DeiT timm model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )

    lowercase				:	Optional[Any]      						=  parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









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

import cva
lowercase				:	Optional[Any]      						=  """"""
lowercase				:	int      						=  """"""
lowercase				:	List[Any]      						=  """"""
lowercase				:	Optional[int]      						=  1  # (0 is vertical, 1 is horizontal)



def A_     (		)  ->		None:
       a__			,							a__						:     str			=    get_dataset(A__		,				A__    )
       print('Processing...'    )
       a__			,							a__			,							a__						:     Tuple			=    update_image_and_anno(A__		,				A__		,				A__    )

       for index, image in enumerate(A__    ):
              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a__						:     int			=    random_chars(32    )
              a__						:     Optional[Any]			=    paths[index].split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              a__						:     Optional[int]			=    F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
              cva.imwrite(F'/{file_root}.jpg'		,				A__		,				[cva.IMWRITE_JPEG_QUALITY, 85]    )
              print(F'Success {index+1}/{len(A__    )} with {file_name}'    )
              a__						:     List[str]			=    []
              for anno in new_annos[index]:
                     a__						:     Union[str, 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 A_     (		A__		,				A__    )  ->		tuple[list, list]:
       a__						:     int			=    []
       a__						:     int			=    []
       for label_file in glob.glob(os.path.join(A__		,				'*.txt'    )    ):
              a__						:     Optional[Any]			=    label_file.split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              with open(A__    ) as in_file:
                     a__						:     Tuple			=    in_file.readlines()
              a__						:     Dict			=    os.path.join(A__		,				F'{label_name}.jpg'    )

              a__						:     int			=    []
              for obj_list in obj_lists:
                     a__						:     Union[str, 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 A_     (		A__		,				A__		,				A__ = 1    )  ->		tuple[list, list, list]:
       a__						:     Optional[int]			=    []
       a__						:     Any			=    []
       a__						:     Dict			=    []
       for idx in range(len(A__    )    ):
              a__						:     Optional[int]			=    []
              a__						:     Optional[Any]			=    img_list[idx]
              path_list.append(A__    )
              a__						:     Union[str, Any]			=    anno_list[idx]
              a__						:     List[str]			=    cva.imread(A__    )
              if flip_type == 1:
                     a__						:     List[str]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[Any]			=    1 - bbox[1]
                            new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
              elif flip_type == 0:
                     a__						:     Optional[Any]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[int]			=    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 A_     (		A__ = 32    )  ->		str:
       assert number_char > 1, "The number of character should greater than 1"
       a__						:     Optional[int]			=    ascii_lowercase + digits
       return "".join(random.choice(A__    ) for _ in range(A__    )    )


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


