






"""simple docstring"""





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel







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

  return name







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		str )      ->				int:
  for key in orig_state_dict.copy().keys():
    lowerCamelCase_         =						orig_state_dict.pop(_lowerCamelCase )

    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_         =						key.split('.' )
      lowerCamelCase_     ,	lowerCamelCase_         =						int(key_split[2] ), int(key_split[4] )
      lowerCamelCase_         =						config.vision_config.hidden_size
      if "weight" in key:
        lowerCamelCase_         =						val[:dim, :]
        lowerCamelCase_         =						val[dim : dim * 2, :]
        lowerCamelCase_         =						val[-dim:, :]
      else:
        lowerCamelCase_         =						val[:dim]
        lowerCamelCase_         =						val[dim : dim * 2]
        lowerCamelCase_         =						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_         =						key.split('.' )
      lowerCamelCase_         =						int(key_split[3] )
      lowerCamelCase_         =						config.text_config.hidden_size
      if "weight" in key:
        lowerCamelCase_         =						val[:dim, :]
        lowerCamelCase_         =						val[
            dim : dim * 2, :
        ]
        lowerCamelCase_         =						val[-dim:, :]
      else:
        lowerCamelCase_         =						val[:dim]
        lowerCamelCase_         =						val[dim : dim * 2]
        lowerCamelCase_         =						val[-dim:]
    else:
      lowerCamelCase_         =						rename_key(_lowerCamelCase )
      # 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_         =						val.squeeze_()
      else:
        lowerCamelCase_         =						val

  return orig_state_dict







def      lowerCamelCase__       (							)      ->				Union[str, Any]:
  lowerCamelCase_         =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCamelCase_         =						Image.open(requests.get(_lowerCamelCase		,				stream=_lowerCamelCase ).raw )
  return im







@torch.no_grad()
def      lowerCamelCase__       (							_lowerCamelCase       :		Optional[Any]		,				_lowerCamelCase       :		List[str]		,				_lowerCamelCase       :		List[str]="groupvit-gcc-yfcc"		,				_lowerCamelCase       :		Optional[Any]=False )      ->				Optional[Any]:
  lowerCamelCase_         =						GroupViTConfig()
  lowerCamelCase_         =						GroupViTModel(_lowerCamelCase ).eval()

  lowerCamelCase_         =						torch.load(_lowerCamelCase		,				map_location='cpu' )['model']
  lowerCamelCase_         =						convert_state_dict(_lowerCamelCase		,				_lowerCamelCase )
  lowerCamelCase_     ,	lowerCamelCase_         =						model.load_state_dict(_lowerCamelCase		,				strict=_lowerCamelCase )
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCamelCase ) == 0)

  # verify result
  lowerCamelCase_         =						CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32' )
  lowerCamelCase_         =						prepare_img()
  lowerCamelCase_         =						processor(text=['a photo of a cat', 'a photo of a dog']		,				images=_lowerCamelCase		,				padding=_lowerCamelCase		,				return_tensors='pt' )

  with torch.no_grad():
    lowerCamelCase_         =						model(**_lowerCamelCase )

  if model_name == "groupvit-gcc-yfcc":
    lowerCamelCase_         =						torch.tensor([[13.35_23, 6.36_29]] )
  elif model_name == "groupvit-gcc-redcaps":
    lowerCamelCase_         =						torch.tensor([[16.18_73, 8.62_30]] )
  else:
    raise ValueError(F'''Model name {model_name} not supported.''' )
  assert torch.allclose(outputs.logits_per_image		,				_lowerCamelCase		,				atol=1e-3 )

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

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


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE					:				Optional[int]					    =						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`.''',
  )
  _SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						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 typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE					:				Optional[Any]					    =						TypeVar('''T''')






class      a						(					Generic[T]		):






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      lowerCamelCase_         =						data
      lowerCamelCase_         =						self
      lowerCamelCase_         =						0






class      a						(					Generic[T]		):






    def __init__(       self     :							Any					)     ->		None:
      # map from node name to the node object
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # create a new set with x as its member
      lowerCamelCase_         =						DisjointSetTreeNode(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		DisjointSetTreeNode[T]:
      # find the set x belongs to (with path-compression)
      lowerCamelCase_         =						self.map[data]
      if elem_ref != elem_ref.parent:
        lowerCamelCase_         =						self.find_set(elem_ref.parent.data					)
      return elem_ref.parent






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]       ,						__SCREAMING_SNAKE_CASE     :							DisjointSetTreeNode[T]					)     ->		None:
      # helper function for union operation
      if nodea.rank > nodea.rank:
        lowerCamelCase_         =						nodea
      else:
        lowerCamelCase_         =						nodea
        if nodea.rank == nodea.rank:
          nodea.rank += 1






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # merge 2 disjoint sets
      self.link(self.find_set(__SCREAMING_SNAKE_CASE					)       ,						self.find_set(__SCREAMING_SNAKE_CASE					)					)






class      a						(					Generic[T]		):






    def __init__(       self     :							Optional[int]					)     ->		None:
      # connections: map from the node to the neighbouring nodes (with weights)
      lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							T					)     ->		None:
      # add a node ONLY if its not present in the graph
      if node not in self.connections:
        lowerCamelCase_         =						{}






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							T       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		None:
      # add an edge with the given weight
      self.add_node(__SCREAMING_SNAKE_CASE					)
      self.add_node(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						weight
      lowerCamelCase_         =						weight






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		GraphUndirectedWeighted[T]:
      lowerCamelCase_         =						[]
      lowerCamelCase_         =						set()
      for start in self.connections:
        for end in self.connections[start]:
          if (start, end) not in seen:
            seen.add((end, start)					)
            edges.append((start, end, self.connections[start][end])					)
      edges.sort(key=lambda __SCREAMING_SNAKE_CASE					: x[2]					)

      # creating the disjoint set
      lowerCamelCase_         =						DisjointSetTree[T]()
      for node in self.connections:
        disjoint_set.make_set(__SCREAMING_SNAKE_CASE					)

      # MST generation
      lowerCamelCase_         =						0
      lowerCamelCase_         =						0
      lowerCamelCase_         =						GraphUndirectedWeighted[T]()
      while num_edges < len(self.connections					) - 1:
        lowerCamelCase_     ,	lowerCamelCase_     ,	lowerCamelCase_         =						edges[index]
        index += 1
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        lowerCamelCase_         =						disjoint_set.find_set(__SCREAMING_SNAKE_CASE					)
        if parent_u != parent_v:
          num_edges += 1
          graph.add_edge(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          disjoint_set.union(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
      return graph




