


"""simple docstring"""
import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
UpperCAmelCase				=						logging.get_logger(__name__)

def 				_snake_case      (   __snake_case							:      Optional[Any]						, __snake_case							:      Union[str, Any]=False   ):




   """simple docstring"""







   _lowerCamelCase	:					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'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"""),
          ]   )

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      _lowerCamelCase	:					Optional[int]   =		[(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      (   __snake_case							:      Optional[Any]						, __snake_case							:      Dict						, __snake_case							:      Tuple=False   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      Tuple   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		["""head.weight""", """head.bias"""]
   for k in ignore_keys:
      state_dict.pop(__snake_case						, __snake_case   )

def 				_snake_case      (   __snake_case							:      Optional[int]						, __snake_case							:      Dict						, __snake_case							:      Union[str, Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		dct.pop(__snake_case   )
   _lowerCamelCase	:					Any   =		val

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Optional[Any]   =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _lowerCamelCase	:					int   =		Image.open(requests.get(__snake_case						, stream=__snake_case   ).raw   )
   return im

@torch.no_grad()
def 				_snake_case      (   __snake_case							:      int						, __snake_case							:      str						, __snake_case							:      Any=True   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		ViTConfig()
   # patch_size
   if model_name[-1] == "8":
      _lowerCamelCase	:					str   =		8
   # set labels if required
   if not base_model:
      _lowerCamelCase	:					List[str]   =		1000
      _lowerCamelCase	:					List[Any]   =		"""huggingface/label-files"""
      _lowerCamelCase	:					Any   =		"""imagenet-1k-id2label.json"""
      _lowerCamelCase	:					int   =		json.load(open(hf_hub_download(__snake_case						, __snake_case						, repo_type="""dataset"""   )						, """r"""   )   )
      _lowerCamelCase	:					Optional[Any]   =		{int(__snake_case   ): v for k, v in idalabel.items()}
      _lowerCamelCase	:					Tuple   =		idalabel
      _lowerCamelCase	:					List[str]   =		{v: k for k, v in idalabel.items()}
   # size of the architecture
   if model_name in ["dino_vits8", "dino_vits16"]:
      _lowerCamelCase	:					Union[str, Any]   =		384
      _lowerCamelCase	:					Tuple   =		1536
      _lowerCamelCase	:					List[str]   =		12
      _lowerCamelCase	:					Dict   =		6

   # load original model from torch hub
   _lowerCamelCase	:					Any   =		torch.hub.load("""facebookresearch/dino:main"""						, __snake_case   )
   original_model.eval()

   # load state_dict of original model, remove and rename some keys
   _lowerCamelCase	:					Any   =		original_model.state_dict()
   if base_model:
      remove_classification_head_(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		create_rename_keys(__snake_case						, base_model=__snake_case   )
   for src, dest in rename_keys:
      rename_key(__snake_case						, __snake_case						, __snake_case   )
   read_in_q_k_v(__snake_case						, __snake_case						, __snake_case   )

   # load HuggingFace model
   if base_model:
      _lowerCamelCase	:					Optional[Any]   =		ViTModel(__snake_case						, add_pooling_layer=__snake_case   ).eval()
   else:
      _lowerCamelCase	:					Optional[Any]   =		ViTForImageClassification(__snake_case   ).eval()
   model.load_state_dict(__snake_case   )

   # Check outputs on an image, prepared by ViTImageProcessor
   _lowerCamelCase	:					Dict   =		ViTImageProcessor()
   _lowerCamelCase	:					Union[str, Any]   =		image_processor(images=prepare_img()						, return_tensors="""pt"""   )
   _lowerCamelCase	:					Optional[int]   =		encoding["""pixel_values"""]
   _lowerCamelCase	:					str   =		model(__snake_case   )

   if base_model:
      _lowerCamelCase	:					Optional[int]   =		original_model(__snake_case   )
      assert torch.allclose(__snake_case						, outputs.last_hidden_state[:, 0, :]						, atol=1E-1   )
   else:
      _lowerCamelCase	:					Optional[Any]   =		original_model(__snake_case   )
      assert logits.shape == outputs.logits.shape
      assert torch.allclose(__snake_case						, outputs.logits						, atol=1E-3   )

   Path(__snake_case   ).mkdir(exist_ok=__snake_case   )
   print(F'Saving model {model_name} to {pytorch_dump_folder_path}'   )
   model.save_pretrained(__snake_case   )
   print(F'Saving image processor to {pytorch_dump_folder_path}'   )
   image_processor.save_pretrained(__snake_case   )


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""dino_vitb16""",
     type=str,
     help="""Name of the model trained with DINO 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(
     """--base_model""",
     action="""store_true""",
     help="""Whether to only convert the base model (no projection head weights).""",
 )

 parser.set_defaults(base_model=True)
 UpperCAmelCase				=						parser.parse_args()
 convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





"""simple docstring"""

from PIL import Image


def lowerCAmelCase_   (					UpperCamelCase__						:			Image					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						(259 * (level + 255)) / (255 * (259 - level))

 def contrast(UpperCamelCase__						:			int			) -> int:
  return int(128 + factor * (c - 128)			)

 return img.point(UpperCamelCase__			)


if __name__ == "__main__":
       # Load image
       with Image.open("image_data/lena.jpg") as img:
              # Change contrast to 170
              UpperCAmelCase__					=change_contrast(img, 170)
              cont_img.save("image_data/lena_high_contrast.png", format="png")
