





'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Union[str, Any]:
     """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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          __a       =   orig_state_dict.pop(a__		)

          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(a__		)
               # 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        __lowercase						(      )				->					Optional[Any]:
     """simple docstring"""




     __a       =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
     __a       =   Image.open(requests.get(a__				,  stream=a__		).raw		)
     return im




@torch.no_grad()
def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="groupvit-gcc-yfcc"				,  __SCREAMING_SNAKE_CASE=False		)				->					Any:
     """simple docstring"""




     __a       =   GroupViTConfig()
     __a       =   GroupViTModel(a__		).eval()

     __a       =   torch.load(a__				,  map_location="""cpu"""		)["""model"""]
     __a       =   convert_state_dict(a__				,  a__		)
     __a ,						__a       =   model.load_state_dict(a__				,  strict=a__		)
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(a__		) == 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=a__				,  padding=a__				,  return_tensors="""pt"""		)

     with torch.no_grad():
          __a       =   model(**a__		)

     if model_name == "groupvit-gcc-yfcc":
          __a       =   torch.tensor([[13.3_523, 6.3_629]]		)
     elif model_name == "groupvit-gcc-redcaps":
          __a       =   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				,  a__				,  atol=1E-3		)

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

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


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

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










'''simple docstring'''







def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					bool:
     """simple docstring"""




     __a       =   [int(__SCREAMING_SNAKE_CASE		) for i in ip_va_address.split("""."""		) if i.isdigit()]
     return len(__SCREAMING_SNAKE_CASE		) == 4 and all(0 <= int(__SCREAMING_SNAKE_CASE		) <= 254 for octet in octets		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			input().strip()
 SCREAMING_SNAKE_CASE_      =			'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")






