






'''simple docstring'''
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

   return name




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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

   return orig_state_dict




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    List[str]           =     "http://images.cocodataset.org/val2017/000000039769.jpg"
   lowercase_	:    int           =     Image.open(requests.get(_UpperCamelCase						,		stream=_UpperCamelCase							).raw							)
   return im




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase="groupvit-gcc-yfcc"						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     GroupViTConfig()
   lowercase_	:    Tuple           =     GroupViTModel(_UpperCamelCase							).eval()

   lowercase_	:    int           =     torch.load(_UpperCamelCase						,		map_location="cpu"							)["model"]
   lowercase_	:    Tuple           =     convert_state_dict(_UpperCamelCase						,		_UpperCamelCase							)
   lowercase_    ,		lowercase_	:    Tuple           =     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
   lowercase_	:    Tuple           =     CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
   lowercase_	:    List[Any]           =     prepare_img()
   lowercase_	:    List[str]           =     processor(text=["a photo of a cat", "a photo of a dog"]						,		images=_UpperCamelCase						,		padding=_UpperCamelCase						,		return_tensors="pt"							)

   with torch.no_grad():
      lowercase_	:    int           =     model(**_UpperCamelCase							)

   if model_name == "groupvit-gcc-yfcc":
      lowercase_	:    List[Any]           =     torch.tensor([[13.3523, 6.3629]]							)
   elif model_name == "groupvit-gcc-redcaps":
      lowercase_	:    Optional[Any]           =     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__":
 UpperCamelCase__ =			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`.',
 )
 UpperCamelCase__ =			parser.parse_args()

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












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     0
   lowercase_	:    Optional[Any]           =     len(_UpperCamelCase							)  # No of vertices in graph
   lowercase_	:    Union[str, Any]           =     [0] * n
   lowercase_	:    Optional[int]           =     [False] * n

   def dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Union[str, Any]           =     True
      lowercase_	:    Dict           =     id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		id_							)
            lowercase_	:    str           =     min(low[at]						,		low[to]							)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)							)
         else:
            # This edge is a back edge and cannot be a bridge
            lowercase_	:    Optional[int]           =     min(low[at]						,		low[to]							)

   lowercase_	:    list[tuple[int, int]]           =     []
   for i in range(_UpperCamelCase							):
      if not visited[i]:
         dfs(_UpperCamelCase						,		-1						,		_UpperCamelCase						,		id_							)
   return bridges


if __name__ == "__main__":
 import doctest

 doctest.testmod()




