


"""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()
_snake_case        =				logging.get_logger(__name__)



def 	snake_case    (					_a:  Tuple		, _a:  int=False				)->		Any:
      '''simple docstring'''


      lowerCamelCase__							     =		[]

      # 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"
            lowerCamelCase__							     =		[(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 	snake_case    (					_a:  Optional[Any]		, _a:  Tuple		, _a:  Optional[Any]=False				)->		int:
      '''simple docstring'''


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



def 	snake_case    (					_a:  Dict				)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__							     =		['head.weight', 'head.bias']
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase__		, UpperCamelCase__				)



def 	snake_case    (					_a:  Tuple		, _a:  Optional[int]		, _a:  Any				)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__							     =		dct.pop(UpperCamelCase__				)
      lowerCamelCase__							     =		val



def 	snake_case    (					)->		str:
      '''simple docstring'''


      lowerCamelCase__							     =		'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase__							     =		Image.open(requests.get(UpperCamelCase__		, stream=UpperCamelCase__				).raw				)
      return im



@torch.no_grad()
def 	snake_case    (					_a:  List[Any]		, _a:  Union[str, Any]		, _a:  Tuple=False				)->		Dict:
      '''simple docstring'''


      lowerCamelCase__							     =		BitConfig(
          global_padding='same'		, layer_type='bottleneck'		, depths=(3, 4, 9)		, out_features=['stage3']		, embedding_dynamic_padding=UpperCamelCase__		, )
      lowerCamelCase__							     =		ViTHybridConfig(backbone_config=UpperCamelCase__		, image_size=384		, num_labels=1000				)
      lowerCamelCase__							     =		False

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

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

      lowerCamelCase__							     =		'huggingface/label-files'
      lowerCamelCase__							     =		'imagenet-1k-id2label.json'
      lowerCamelCase__							     =		json.load(open(hf_hub_download(UpperCamelCase__		, UpperCamelCase__		, repo_type='dataset'				)		, 'r'				)				)
      lowerCamelCase__							     =		{int(UpperCamelCase__				): v for k, v in idalabel.items()}
      lowerCamelCase__							     =		idalabel
      lowerCamelCase__							     =		{v: k for k, v in idalabel.items()}

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

      # create image processor
      lowerCamelCase__							     =		create_transform(**resolve_data_config({}		, model=UpperCamelCase__				)				)
      lowerCamelCase__							     =		transform.transforms

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

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

      lowerCamelCase__							     =		prepare_img()
      lowerCamelCase__							     =		transform(UpperCamelCase__				).unsqueeze(0				)
      lowerCamelCase__							     =		processor(UpperCamelCase__		, return_tensors='pt'				).pixel_values

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

      # verify logits
      with torch.no_grad():
            lowerCamelCase__							     =		model(UpperCamelCase__				)
            lowerCamelCase__							     =		outputs.logits

      print('Predicted class:'		, logits.argmax(-1				).item()				)
      if base_model:
            lowerCamelCase__							     =		timm_model.forward_features(UpperCamelCase__				)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(UpperCamelCase__		, outputs.pooler_output		, atol=1E-3				)
      else:
            lowerCamelCase__							     =		timm_model(UpperCamelCase__				)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(UpperCamelCase__		, outputs.logits		, atol=1E-3				)
      print('Looks ok!'				)

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

      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__":
   _snake_case        =				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."
   )

   _snake_case        =				parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt


def lowerCAmelCase_   (					UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			np.ndarray					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						cva.getAffineTransform(UpperCamelCase__					, UpperCamelCase__			)
 return cva.warpAffine(UpperCamelCase__					, UpperCamelCase__					, (rows, cols)			)


if __name__ == "__main__":
       # read original image
       UpperCAmelCase__					=cva.imread(
           str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
       )
       # turn image in gray scale value
       UpperCAmelCase__					=cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       UpperCAmelCase__				,   UpperCAmelCase__					=gray_img.shape

       # set different points to rotate image
       UpperCAmelCase__					=np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       UpperCAmelCase__					=np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       UpperCAmelCase__					=np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       UpperCAmelCase__					=[
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       UpperCAmelCase__					=plt.figure(1)
       UpperCAmelCase__					=["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
              plt.title(titles[i])
              plt.axis("off")
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()
