





"""simple docstring"""
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_       =		logging.get_logger(__name__)



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		List[Any]=False							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  []

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



def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				str:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(lowerCamelCase__			,       lowerCamelCase__							)



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Any			,       a_					:		Tuple							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  dct.pop(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  val



def 					__lowerCamelCase       (							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Image.open(requests.get(lowerCamelCase__			,       stream=lowerCamelCase__							).raw							)
      return im



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Dict			,       a_					:		Tuple=False							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :Any		    =  BitConfig(
          global_padding='''same'''			,       layer_type='''bottleneck'''			,       depths=(3, 4, 9)			,       out_features=['''stage3''']			,       embedding_dynamic_padding=lowerCamelCase__			,       )
      __SCREAMING_SNAKE_CASE       :Dict		    =  ViTHybridConfig(backbone_config=lowerCamelCase__			,       image_size=3_84			,       num_labels=10_00							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  False

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

      # load state_dict of original model, remove and rename some keys
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  timm_model.state_dict()
      if base_model:
            remove_classification_head_(lowerCamelCase__							)
      __SCREAMING_SNAKE_CASE       :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__							)

      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''huggingface/label-files'''
      __SCREAMING_SNAKE_CASE       :str		    =  '''imagenet-1k-id2label.json'''
      __SCREAMING_SNAKE_CASE       :str		    =  json.load(open(hf_hub_download(lowerCamelCase__			,       lowerCamelCase__			,       repo_type='''dataset'''							)			,       '''r'''							)							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  {int(lowerCamelCase__							): v for k, v in idalabel.items()}
      __SCREAMING_SNAKE_CASE       :Tuple		    =  idalabel
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {v: k for k, v in idalabel.items()}

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ViTHybridModel(lowerCamelCase__							).eval()
      else:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ViTHybridForImageClassification(lowerCamelCase__							).eval()
      model.load_state_dict(lowerCamelCase__							)

      # create image processor
      __SCREAMING_SNAKE_CASE       :Dict		    =  create_transform(**resolve_data_config({}			,       model=lowerCamelCase__							)							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  transform.transforms

      __SCREAMING_SNAKE_CASE       :str		    =  {
          '''bilinear''': PILImageResampling.BILINEAR,
          '''bicubic''': PILImageResampling.BICUBIC,
          '''nearest''': PILImageResampling.NEAREST,
      }

      __SCREAMING_SNAKE_CASE       :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()			,       )

      __SCREAMING_SNAKE_CASE       :Tuple		    =  prepare_img()
      __SCREAMING_SNAKE_CASE       :int		    =  transform(lowerCamelCase__							).unsqueeze(0							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  processor(lowerCamelCase__			,       return_tensors='''pt'''							).pixel_values

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

      # verify logits
      with torch.no_grad():
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  model(lowerCamelCase__							)
            __SCREAMING_SNAKE_CASE       :Dict		    =  outputs.logits

      print('''Predicted class:'''			,       logits.argmax(-1							).item()							)
      if base_model:
            __SCREAMING_SNAKE_CASE       :int		    =  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:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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_       =		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_       =		parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  len(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  (
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      __SCREAMING_SNAKE_CASE       :list		    =  []
      for char_count in range(a_							):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]							)
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]							)
      return "".join(a_							)


if __name__ == "__main__":
  print(alternative_string_arrange("AB", "XYZ"), end=" ")