
import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


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

      return name


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

      return orig_state_dict


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Union[str, Any]					=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__    :   Union[str, Any]					=				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		):
      UpperCamelCase__    :   List[Any]					=				GroupViTConfig()
      UpperCamelCase__    :   Dict					=				GroupViTModel(UpperCamelCase__		).eval()

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

      with torch.no_grad():
            UpperCamelCase__    :   Optional[int]					=				model(**UpperCamelCase__		)

      if model_name == "groupvit-gcc-yfcc":
            UpperCamelCase__    :   str					=				torch.tensor([[13.3523, 6.3629]]		)
      elif model_name == "groupvit-gcc-redcaps":
            UpperCamelCase__    :   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__":
    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)




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


