"""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()
_a   :	Dict 					=     logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Optional[Any]=False			) ->   Tuple:
  _lowerCAmelCase		: List[Any]						    =  []
  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		: 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"""),
        ]			)

  return rename_keys





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





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





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: List[Any]    ,_lowerCamelCase		: Dict			) ->   List[Any]:
  _lowerCAmelCase		: Optional[Any]						    =  dct.pop(_lowerCamelCase			)
  _lowerCAmelCase		: List[str]						    =  val





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





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

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

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

  # load HuggingFace model
  if base_model:
    _lowerCAmelCase		: Dict						    =  ViTModel(_lowerCamelCase    ,add_pooling_layer=_lowerCamelCase			).eval()
  else:
    _lowerCAmelCase		: Any						    =  ViTForImageClassification(_lowerCamelCase			).eval()
  model.load_state_dict(_lowerCamelCase			)

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

  if base_model:
    _lowerCAmelCase		: Union[str, Any]						    =  original_model(_lowerCamelCase			)
    assert torch.allclose(_lowerCamelCase    ,outputs.last_hidden_state[:, 0, :]    ,atol=1e-1			)
  else:
    _lowerCAmelCase		: Optional[Any]						    =  original_model(_lowerCamelCase			)
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(_lowerCamelCase    ,outputs.logits    ,atol=1e-3			)

  Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
  print(f"Saving model {model_name} to {pytorch_dump_folder_path}"			)
  model.save_pretrained(_lowerCamelCase			)
  print(f"Saving image processor to {pytorch_dump_folder_path}"			)
  image_processor.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
     _a   :	Any 					=     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)
     _a   :	List[Any] 					=     parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

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

  return img.point(_lowerCamelCase			)


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




