


"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def       A_   (   ):






     """simple docstring"""

     _a	,      _a    	=				9, 14  # noqa: F841
     _a    	=				[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a    	=				defaultdict(_lowerCAmelCase       )
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]       )
          adjancency[nodea].append([nodea, cost]       )

     _a    	=				mst(_lowerCAmelCase       )

     _a    	=				[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a    	=				tuple(answer[:2]       )
          _a    	=				tuple(edge[::-1]       )
          assert edge in result or reverse in result



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

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

     return name
def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _a    	=				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
               _a    	=				key.split('''.'''       )
               _a	,      _a    	=				int(key_split[2]       ), int(key_split[4]       )
               _a    	=				config.vision_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[dim : dim * 2, :]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				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
               _a    	=				key.split('''.'''       )
               _a    	=				int(key_split[3]       )
               _a    	=				config.text_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[
                        dim : dim * 2, :
                    ]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          else:
               _a    	=				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
               ):
                    _a    	=				val.squeeze_()
               else:
                    _a    	=				val

     return orig_state_dict
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict="groupvit-gcc-yfcc",     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				GroupViTConfig()
     _a    	=				GroupViTModel(_lowerCAmelCase       ).eval()

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     _a    	=				convert_state_dict(_lowerCAmelCase,     _lowerCAmelCase       )
     _a	,      _a    	=				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
     _a    	=				CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
     _a    	=				prepare_img()
     _a    	=				processor(text=['''a photo of a cat''', '''a photo of a dog'''],     images=_lowerCAmelCase,     padding=_lowerCAmelCase,     return_tensors='''pt'''       )

     with torch.no_grad():
          _a    	=				model(**_lowerCAmelCase       )

     if model_name == "groupvit-gcc-yfcc":
          _a    	=				torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
     elif model_name == "groupvit-gcc-redcaps":
          _a    	=				torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]       )
     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__":
   __snake_case		=       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`.''',
   )
   __snake_case		=       parser.parse_args()

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