

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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_       		=  logging.get_logger(__name__)
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=False      )							->      List[Any]:





    '''simple docstring'''






    A__          =   []
    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'vit.encoder.layer.{i}.layernorm_before.weight')      )
        rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')      )
        rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')      )
        rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')      )
        rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')      )
        rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')      )
        rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')      )
        rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')      )
        rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')      )
        rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')      )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('cls_token', 'vit.embeddings.cls_token'),
            ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
            ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
            ('pos_embed', 'vit.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 "vit" from all keys that start with "vit"
        A__          =   [(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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      Optional[int]:





    '''simple docstring'''






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





    '''simple docstring'''






    A__          =   ['head.weight', 'head.bias']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict      )							->      Optional[int]:





    '''simple docstring'''






    A__          =   dct.pop(SCREAMING_SNAKE_CASE__      )
    A__          =   val
def 						_snake_case(    )							->      Any:





    '''simple docstring'''






    A__          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
    A__          =   Image.open(requests.get(SCREAMING_SNAKE_CASE__    ,			stream=SCREAMING_SNAKE_CASE__      ).raw      )
    return im
@torch.no_grad()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Dict:





    '''simple docstring'''






    A__          =   ViTConfig()
    A__          =   False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        A__          =   True
        A__          =   int(vit_name[-12:-10]      )
        A__          =   int(vit_name[-9:-6]      )
    else:
        A__          =   1000
        A__          =   'huggingface/label-files'
        A__          =   'imagenet-1k-id2label.json'
        A__          =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			repo_type='dataset'      )    ,			'r'      )      )
        A__          =   {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}
        A__          =   idalabel
        A__          =   {v: k for k, v in idalabel.items()}
        A__          =   int(vit_name[-6:-4]      )
        A__          =   int(vit_name[-3:]      )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith('tiny'      ):
            A__          =   192
            A__          =   768
            A__          =   12
            A__          =   3
        elif vit_name[9:].startswith('small'      ):
            A__          =   384
            A__          =   1536
            A__          =   12
            A__          =   6
        else:
            pass
    else:
        if vit_name[4:].startswith('small'      ):
            A__          =   768
            A__          =   2304
            A__          =   8
            A__          =   8
        elif vit_name[4:].startswith('base'      ):
            pass
        elif vit_name[4:].startswith('large'      ):
            A__          =   1024
            A__          =   4096
            A__          =   24
            A__          =   16
        elif vit_name[4:].startswith('huge'      ):
            A__          =   1280
            A__          =   5120
            A__          =   32
            A__          =   16

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

    # load state_dict of original model, remove and rename some keys
    A__          =   timm_model.state_dict()
    if base_model:
        remove_classification_head_(SCREAMING_SNAKE_CASE__      )
    A__          =   create_rename_keys(SCREAMING_SNAKE_CASE__    ,			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__    ,			SCREAMING_SNAKE_CASE__      )

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        A__          =   ViTModel(SCREAMING_SNAKE_CASE__      ).eval()
    else:
        A__          =   ViTForImageClassification(SCREAMING_SNAKE_CASE__      ).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        A__          =   DeiTImageProcessor(size=config.image_size      )
    else:
        A__          =   ViTImageProcessor(size=config.image_size      )
    A__          =   image_processor(images=prepare_img()    ,			return_tensors='pt'      )
    A__          =   encoding['pixel_values']
    A__          =   model(SCREAMING_SNAKE_CASE__      )

    if base_model:
        A__          =   timm_model.forward_features(SCREAMING_SNAKE_CASE__      )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE__    ,			outputs.pooler_output    ,			atol=1E-3      )
    else:
        A__          =   timm_model(SCREAMING_SNAKE_CASE__      )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE__    ,			outputs.logits    ,			atol=1E-3      )

    Path(SCREAMING_SNAKE_CASE__      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__      )
    print(f'Saving model {vit_name} 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__":
     lowercase_       		=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--vit_name",
         default="vit_base_patch16_224",
         type=str,
         help="Name of the ViT 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_       		=  parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

