






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def   snake_case (UpperCAmelCase__  )	->    Union[str, Any]:
  # vision encoder
  if "img_encoder.pos_embed" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('img_encoder.pos_embed'     ,   'vision_model.embeddings.position_embeddings'  )
  if "img_encoder.patch_embed.proj" in name:
    UpperCamelCase_: List[str]   				=    name.replace('img_encoder.patch_embed.proj'     ,   'vision_model.embeddings.patch_embeddings.projection'  )
  if "img_encoder.patch_embed.norm" in name:
    UpperCamelCase_: List[str]   				=    name.replace('img_encoder.patch_embed.norm'     ,   'vision_model.embeddings.layernorm'  )
  if "img_encoder.layers" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('img_encoder.layers'     ,   'vision_model.encoder.stages'  )
  if "blocks" in name and "res" not in name:
    UpperCamelCase_: str   				=    name.replace('blocks'     ,   'layers'  )
  if "attn" in name and "pre_assign" not in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('attn'     ,   'self_attn'  )
  if "proj" in name and "self_attn" in name and "text" not in name:
    UpperCamelCase_: Dict   				=    name.replace('proj'     ,   'out_proj'  )
  if "pre_assign_attn.attn.proj" in name:
    UpperCamelCase_: Any   				=    name.replace('pre_assign_attn.attn.proj'     ,   'pre_assign_attn.attn.out_proj'  )
  if "norm1" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('norm1'     ,   'layer_norm1'  )
  if "norm2" in name and "pre_assign" not in name:
    UpperCamelCase_: Dict   				=    name.replace('norm2'     ,   'layer_norm2'  )
  if "img_encoder.norm" in name:
    UpperCamelCase_: Any   				=    name.replace('img_encoder.norm'     ,   'vision_model.layernorm'  )
  # text encoder
  if "text_encoder.token_embedding" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('text_encoder.token_embedding'     ,   'text_model.embeddings.token_embedding'  )
  if "text_encoder.positional_embedding" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('text_encoder.positional_embedding'     ,   'text_model.embeddings.position_embedding.weight'  )
  if "text_encoder.transformer.resblocks." in name:
    UpperCamelCase_: str   				=    name.replace('text_encoder.transformer.resblocks.'     ,   'text_model.encoder.layers.'  )
  if "ln_1" in name:
    UpperCamelCase_: List[Any]   				=    name.replace('ln_1'     ,   'layer_norm1'  )
  if "ln_2" in name:
    UpperCamelCase_: int   				=    name.replace('ln_2'     ,   'layer_norm2'  )
  if "c_fc" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('c_fc'     ,   'fc1'  )
  if "c_proj" in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('c_proj'     ,   'fc2'  )
  if "text_encoder" in name:
    UpperCamelCase_: int   				=    name.replace('text_encoder'     ,   'text_model'  )
  if "ln_final" in name:
    UpperCamelCase_: str   				=    name.replace('ln_final'     ,   'final_layer_norm'  )
  # projection layers
  if "img_projector.linear_hidden." in name:
    UpperCamelCase_: Union[str, Any]   				=    name.replace('img_projector.linear_hidden.'     ,   'visual_projection.'  )
  if "img_projector.linear_out." in name:
    UpperCamelCase_: Optional[Any]   				=    name.replace('img_projector.linear_out.'     ,   'visual_projection.3.'  )
  if "text_projector.linear_hidden" in name:
    UpperCamelCase_: Optional[int]   				=    name.replace('text_projector.linear_hidden'     ,   'text_projection'  )
  if "text_projector.linear_out" in name:
    UpperCamelCase_: int   				=    name.replace('text_projector.linear_out'     ,   'text_projection.3'  )

  return name

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[int]:
  for key in orig_state_dict.copy().keys():
    UpperCamelCase_: Tuple   				=    orig_state_dict.pop(UpperCAmelCase__  )

    if "qkv" in key:
      # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      UpperCamelCase_: Tuple   				=    key.split('.'  )
      UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    int(key_split[2]  ), int(key_split[4]  )
      UpperCamelCase_: str   				=    config.vision_config.hidden_size
      if "weight" in key:
        UpperCamelCase_: List[str]   				=    val[:dim, :]
        UpperCamelCase_: Optional[int]   				=    val[dim : dim * 2, :]
        UpperCamelCase_: Union[str, Any]   				=    val[-dim:, :]
      else:
        UpperCamelCase_: int   				=    val[:dim]
        UpperCamelCase_: List[str]   				=    val[dim : dim * 2]
        UpperCamelCase_: List[str]   				=    val[-dim:]
    elif "in_proj" in key:
      # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      UpperCamelCase_: List[str]   				=    key.split('.'  )
      UpperCamelCase_: Any   				=    int(key_split[3]  )
      UpperCamelCase_: Tuple   				=    config.text_config.hidden_size
      if "weight" in key:
        UpperCamelCase_: Tuple   				=    val[:dim, :]
        UpperCamelCase_: str   				=    val[
            dim : dim * 2, :
        ]
        UpperCamelCase_: str   				=    val[-dim:, :]
      else:
        UpperCamelCase_: Union[str, Any]   				=    val[:dim]
        UpperCamelCase_: Tuple   				=    val[dim : dim * 2]
        UpperCamelCase_: Optional[Any]   				=    val[-dim:]
    else:
      UpperCamelCase_: Dict   				=    rename_key(UpperCAmelCase__  )
      # squeeze if necessary
      if (
          "text_projection.0" in new_name
          or "text_projection.3" in new_name
          or "visual_projection.0" in new_name
          or "visual_projection.3" in new_name
      ):
        UpperCamelCase_: Dict   				=    val.squeeze_()
      else:
        UpperCamelCase_: Optional[Any]   				=    val

  return orig_state_dict

def   snake_case ()	->    Any:
  UpperCamelCase_: Any   				=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_: str   				=    Image.open(requests.get(UpperCAmelCase__     ,   stream=UpperCAmelCase__  ).raw  )
  return im

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__="groupvit-gcc-yfcc"     ,   UpperCAmelCase__=False  )	->    Any:
  UpperCamelCase_: Any   				=    GroupViTConfig()
  UpperCamelCase_: List[Any]   				=    GroupViTModel(UpperCAmelCase__  ).eval()

  UpperCamelCase_: Any   				=    torch.load(UpperCAmelCase__     ,   map_location='cpu'  )['model']
  UpperCamelCase_: List[str]   				=    convert_state_dict(UpperCAmelCase__     ,   UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(UpperCAmelCase__  ) == 0)

  # verify result
  UpperCamelCase_: Tuple   				=    CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'  )
  UpperCamelCase_: List[Any]   				=    prepare_img()
  UpperCamelCase_: Dict   				=    processor(text=['a photo of a cat', 'a photo of a dog']     ,   images=UpperCAmelCase__     ,   padding=UpperCAmelCase__     ,   return_tensors='pt'  )

  with torch.no_grad():
    UpperCamelCase_: int   				=    model(**UpperCAmelCase__  )

  if model_name == "groupvit-gcc-yfcc":
    UpperCamelCase_: List[str]   				=    torch.tensor([[13.3523, 6.3629]]  )
  elif model_name == "groupvit-gcc-redcaps":
    UpperCamelCase_: Tuple   				=    torch.tensor([[16.1873, 8.6230]]  )
  else:
    raise ValueError(F'''Model name {model_name} not supported.'''  )
  assert torch.allclose(outputs.logits_per_image     ,   UpperCAmelCase__     ,   atol=1E-3  )

  processor.save_pretrained(UpperCAmelCase__  )
  model.save_pretrained(UpperCAmelCase__  )
  print('Successfully saved processor and model to'     ,   UpperCAmelCase__  )

  if push_to_hub:
    print('Pushing to the hub...'  )
    processor.push_to_hub(UpperCAmelCase__     ,   organization='nielsr'  )
    model.push_to_hub(UpperCAmelCase__     ,   organization='nielsr'  )


if __name__ == "__main__":
   A_		:		Optional[Any]		     =			argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
   )
   parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
   parser.add_argument(
       '--model_name',
       default='groupvit-gccy-fcc',
       type=str,
       help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
   )
   A_		:		List[Any]		     =			parser.parse_args()

   convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  while b:
    UpperCamelCase_				,UpperCamelCase_: int   				=    b, a % b
  return a

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

def   snake_case ()	->    int:
  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()