






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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase	:							Union[str, Any]				=logging.get_logger(__name__)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase=False			) ->	Any:
     UpperCamelCase__			:   Any       				=  []

     # fmt: off
     # stem:
     rename_keys.append(("cls_token", "vit.embeddings.cls_token")			)
     rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")			)

     rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")			)
     rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")			)

     # backbone
     rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")			)
     rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")			)
     rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")			)

     for stage_idx in range(len(config.backbone_config.depths			)			):
          for layer_idx in range(config.backbone_config.depths[stage_idx]			):
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')			)
               rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')			)

          rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')			)
          rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')			)
          rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')			)

     # transformer encoder
     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')			)

     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"
          UpperCamelCase__			:   Dict       				=  [(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"),
              ]			)
     # fmt: on

     return rename_keys







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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Any:
     UpperCamelCase__			:   Optional[Any]       				=  ["head.weight", "head.bias"]
     for k in ignore_keys:
          state_dict.pop(__lowerCAmelCase					,    __lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	Dict:
     UpperCamelCase__			:   Optional[Any]       				=  dct.pop(__lowerCAmelCase			)
     UpperCamelCase__			:   str       				=  val







def 		SCREAMING_SNAKE_CASE							(					) ->	Tuple:
     UpperCamelCase__			:   Optional[int]       				=  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCamelCase__			:   str       				=  Image.open(requests.get(__lowerCAmelCase					,    stream=__lowerCAmelCase			).raw			)
     return im







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase=False			) ->	Any:
     UpperCamelCase__			:   Dict       				=  BitConfig(
         global_padding="same"					,    layer_type="bottleneck"					,    depths=(3, 4, 9)					,    out_features=["stage3"]					,    embedding_dynamic_padding=__lowerCAmelCase					,    )
     UpperCamelCase__			:   Optional[Any]       				=  ViTHybridConfig(backbone_config=__lowerCAmelCase					,    image_size=384					,    num_labels=1000			)
     UpperCamelCase__			:   Tuple       				=  False

     # load original model from timm
     UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     UpperCamelCase__			:   Dict       				=  timm_model.state_dict()
     if base_model:
          remove_classification_head_(__lowerCAmelCase			)
     UpperCamelCase__			:   Dict       				=  create_rename_keys(__lowerCAmelCase					,    __lowerCAmelCase			)
     for src, dest in rename_keys:
          rename_key(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)
     read_in_q_k_v(__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			)

     UpperCamelCase__			:   Any       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Any       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   str       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}
     UpperCamelCase__			:   Optional[Any]       				=  idalabel
     UpperCamelCase__			:   Dict       				=  {v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          UpperCamelCase__			:   int       				=  ViTHybridModel(__lowerCAmelCase			).eval()
     else:
          UpperCamelCase__			:   str       				=  ViTHybridForImageClassification(__lowerCAmelCase			).eval()
     model.load_state_dict(__lowerCAmelCase			)

     # create image processor
     UpperCamelCase__			:   List[Any]       				=  create_transform(**resolve_data_config({}					,    model=__lowerCAmelCase			)			)
     UpperCamelCase__			:   List[str]       				=  transform.transforms

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "bilinear": PILImageResampling.BILINEAR,
         "bicubic": PILImageResampling.BICUBIC,
         "nearest": PILImageResampling.NEAREST,
     }

     UpperCamelCase__			:   Union[str, Any]       				=  ViTHybridImageProcessor(
         do_resize=__lowerCAmelCase					,    size={"shortest_edge": timm_transforms[0].size}					,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,    do_center_crop=__lowerCAmelCase					,    crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}					,    do_normalize=__lowerCAmelCase					,    image_mean=timm_transforms[-1].mean.tolist()					,    image_std=timm_transforms[-1].std.tolist()					,    )

     UpperCamelCase__			:   Optional[int]       				=  prepare_img()
     UpperCamelCase__			:   Union[str, Any]       				=  transform(__lowerCAmelCase			).unsqueeze(0			)
     UpperCamelCase__			:   Optional[Any]       				=  processor(__lowerCAmelCase					,    return_tensors="pt"			).pixel_values

     # verify pixel values
     assert torch.allclose(__lowerCAmelCase					,    __lowerCAmelCase			)

     # verify logits
     with torch.no_grad():
          UpperCamelCase__			:   List[str]       				=  model(__lowerCAmelCase			)
          UpperCamelCase__			:   Optional[int]       				=  outputs.logits

     print("Predicted class:"					,    logits.argmax(-1			).item()			)
     if base_model:
          UpperCamelCase__			:   Union[str, Any]       				=  timm_model.forward_features(__lowerCAmelCase			)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__lowerCAmelCase					,    outputs.pooler_output					,    atol=1E-3			)
     else:
          UpperCamelCase__			:   Dict       				=  timm_model(__lowerCAmelCase			)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(__lowerCAmelCase					,    outputs.logits					,    atol=1E-3			)
     print("Looks ok!"			)

     if pytorch_dump_folder_path is not None:
          Path(__lowerCAmelCase			).mkdir(exist_ok=__lowerCAmelCase			)
          print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'			)
          model.save_pretrained(__lowerCAmelCase			)
          print(f'Saving processor to {pytorch_dump_folder_path}'			)
          processor.save_pretrained(__lowerCAmelCase			)

     if push_to_hub:
          print(f'Pushing model and processor to the hub {vit_name}'			)
          model.push_to_hub(f'ybelkada/{vit_name}'			)
          processor.push_to_hub(f'ybelkada/{vit_name}'			)


if __name__ == "__main__":
 lowerCamelCase	:							Dict				=argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--vit_name''',
     default='''vit_base_r50_s16_384''',
     type=str,
     help='''Name of the hybrid 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.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
 )

 lowerCamelCase	:							Tuple				=parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     while b:
          UpperCamelCase__				,     UpperCamelCase__			:   int       				=  b, a % b
     return a







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	int:
     return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase					,    a % b			)







def 		SCREAMING_SNAKE_CASE							(					) ->	str:
     print(f'euclidean_gcd(3, 5) = {euclidean_gcd(3					,    5			)}'			)
     print(f'euclidean_gcd(5, 3) = {euclidean_gcd(5					,    3			)}'			)
     print(f'euclidean_gcd(1, 3) = {euclidean_gcd(1					,    3			)}'			)
     print(f'euclidean_gcd(3, 6) = {euclidean_gcd(3					,    6			)}'			)
     print(f'euclidean_gcd(6, 3) = {euclidean_gcd(6					,    3			)}'			)

     print(f'euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,    5			)}'			)
     print(f'euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,    3			)}'			)
     print(f'euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,    3			)}'			)
     print(f'euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,    6			)}'			)
     print(f'euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,    3			)}'			)


if __name__ == "__main__":
 main()