


"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







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

  return name
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    lowerCAmelCase     :     str								=							orig_state_dict.pop(SCREAMING_SNAKE_CASE				)

    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
      lowerCAmelCase     :     Dict								=							key.split("."				)
      lowerCAmelCase						,  lowerCAmelCase     :     Any								=							int(key_split[2]				), int(key_split[4]				)
      lowerCAmelCase     :     str								=							config.vision_config.hidden_size
      if "weight" in key:
        lowerCAmelCase     :     Dict								=							val[:dim, :]
        lowerCAmelCase     :     Any								=							val[dim : dim * 2, :]
        lowerCAmelCase     :     str								=							val[-dim:, :]
      else:
        lowerCAmelCase     :     List[str]								=							val[:dim]
        lowerCAmelCase     :     List[str]								=							val[dim : dim * 2]
        lowerCAmelCase     :     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
      lowerCAmelCase     :     str								=							key.split("."				)
      lowerCAmelCase     :     Tuple								=							int(key_split[3]				)
      lowerCAmelCase     :     Optional[int]								=							config.text_config.hidden_size
      if "weight" in key:
        lowerCAmelCase     :     List[str]								=							val[:dim, :]
        lowerCAmelCase     :     List[Any]								=							val[
            dim : dim * 2, :
        ]
        lowerCAmelCase     :     Union[str, Any]								=							val[-dim:, :]
      else:
        lowerCAmelCase     :     Tuple								=							val[:dim]
        lowerCAmelCase     :     Dict								=							val[dim : dim * 2]
        lowerCAmelCase     :     Tuple								=							val[-dim:]
    else:
      lowerCAmelCase     :     str								=							rename_key(SCREAMING_SNAKE_CASE				)
      # 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
      ):
        lowerCAmelCase     :     int								=							val.squeeze_()
      else:
        lowerCAmelCase     :     Optional[Any]								=							val

  return orig_state_dict
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Union[str, Any]								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase     :     str								=							Image.open(requests.get(SCREAMING_SNAKE_CASE				,      stream=SCREAMING_SNAKE_CASE				).raw				)
  return im
@torch.no_grad()
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    Optional[Any]="groupvit-gcc-yfcc"				,      SCREAMING_SNAKE_CASE    :    int=False				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							GroupViTConfig()
  lowerCAmelCase     :     Optional[int]								=							GroupViTModel(SCREAMING_SNAKE_CASE				).eval()

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

  # verify result
  lowerCAmelCase     :     Union[str, Any]								=							CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"				)
  lowerCAmelCase     :     int								=							prepare_img()
  lowerCAmelCase     :     int								=							processor(text=["a photo of a cat", "a photo of a dog"]				,      images=SCREAMING_SNAKE_CASE				,      padding=SCREAMING_SNAKE_CASE				,      return_tensors="pt"				)

  with torch.no_grad():
    lowerCAmelCase     :     List[Any]								=							model(**SCREAMING_SNAKE_CASE				)

  if model_name == "groupvit-gcc-yfcc":
    lowerCAmelCase     :     Optional[Any]								=							torch.tensor([[13.3_523, 6.3_629]]				)
  elif model_name == "groupvit-gcc-redcaps":
    lowerCAmelCase     :     Dict								=							torch.tensor([[16.1_873, 8.6_230]]				)
  else:
    raise ValueError(f"""Model name {model_name} not supported."""				)
  assert torch.allclose(outputs.logits_per_image				,      SCREAMING_SNAKE_CASE				,      atol=1E-3				)

  processor.save_pretrained(SCREAMING_SNAKE_CASE				)
  model.save_pretrained(SCREAMING_SNAKE_CASE				)
  print("Successfully saved processor and model to"				,      SCREAMING_SNAKE_CASE				)

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


if __name__ == "__main__":
     lowerCAmelCase__      				=   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`.''',
     )
     lowerCAmelCase__      				=   parser.parse_args()

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









"""simple docstring"""







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_nodes(node.left				) + count_nodes(node.right				) + 1

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_coins(node.left				) + count_coins(node.right				) + node.data

  if count_nodes(SCREAMING_SNAKE_CASE				) != count_coins(SCREAMING_SNAKE_CASE				):
    raise ValueError("The nodes number should be same as the number of coins"				)

  # Main calculation
  def get_distrib(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

    if node is None:
      return CoinsDistribResult(0				,      1				)

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							node.data - coins_to_left - coins_to_right

    return CoinsDistribResult(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  return get_distrib(SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





