
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__     : int											=					name.replace('''img_encoder.pos_embed'''			,		'''vision_model.embeddings.position_embeddings'''		)
      if "img_encoder.patch_embed.proj" in name:
            UpperCamelCase__     : List[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__     : Tuple											=					name.replace('''img_encoder.layers'''			,		'''vision_model.encoder.stages'''		)
      if "blocks" in name and "res" not in name:
            UpperCamelCase__     : str											=					name.replace('''blocks'''			,		'''layers'''		)
      if "attn" in name and "pre_assign" not in name:
            UpperCamelCase__     : Any											=					name.replace('''attn'''			,		'''self_attn'''		)
      if "proj" in name and "self_attn" in name and "text" not in name:
            UpperCamelCase__     : int											=					name.replace('''proj'''			,		'''out_proj'''		)
      if "pre_assign_attn.attn.proj" in name:
            UpperCamelCase__     : Union[str, Any]											=					name.replace('''pre_assign_attn.attn.proj'''			,		'''pre_assign_attn.attn.out_proj'''		)
      if "norm1" in name:
            UpperCamelCase__     : Union[str, Any]											=					name.replace('''norm1'''			,		'''layer_norm1'''		)
      if "norm2" in name and "pre_assign" not in name:
            UpperCamelCase__     : int											=					name.replace('''norm2'''			,		'''layer_norm2'''		)
      if "img_encoder.norm" in name:
            UpperCamelCase__     : List[Any]											=					name.replace('''img_encoder.norm'''			,		'''vision_model.layernorm'''		)
      # text encoder
      if "text_encoder.token_embedding" in name:
            UpperCamelCase__     : Optional[Any]											=					name.replace('''text_encoder.token_embedding'''			,		'''text_model.embeddings.token_embedding'''		)
      if "text_encoder.positional_embedding" in name:
            UpperCamelCase__     : List[Any]											=					name.replace('''text_encoder.positional_embedding'''			,		'''text_model.embeddings.position_embedding.weight'''		)
      if "text_encoder.transformer.resblocks." in name:
            UpperCamelCase__     : Optional[int]											=					name.replace('''text_encoder.transformer.resblocks.'''			,		'''text_model.encoder.layers.'''		)
      if "ln_1" in name:
            UpperCamelCase__     : str											=					name.replace('''ln_1'''			,		'''layer_norm1'''		)
      if "ln_2" in name:
            UpperCamelCase__     : Union[str, Any]											=					name.replace('''ln_2'''			,		'''layer_norm2'''		)
      if "c_fc" in name:
            UpperCamelCase__     : Optional[Any]											=					name.replace('''c_fc'''			,		'''fc1'''		)
      if "c_proj" in name:
            UpperCamelCase__     : Dict											=					name.replace('''c_proj'''			,		'''fc2'''		)
      if "text_encoder" in name:
            UpperCamelCase__     : Tuple											=					name.replace('''text_encoder'''			,		'''text_model'''		)
      if "ln_final" in name:
            UpperCamelCase__     : Optional[int]											=					name.replace('''ln_final'''			,		'''final_layer_norm'''		)
      # projection layers
      if "img_projector.linear_hidden." in name:
            UpperCamelCase__     : Tuple											=					name.replace('''img_projector.linear_hidden.'''			,		'''visual_projection.'''		)
      if "img_projector.linear_out." in name:
            UpperCamelCase__     : Optional[Any]											=					name.replace('''img_projector.linear_out.'''			,		'''visual_projection.3.'''		)
      if "text_projector.linear_hidden" in name:
            UpperCamelCase__     : Tuple											=					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__     : str											=					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__     : Any											=					key.split('''.'''		)
                  UpperCamelCase__					,UpperCamelCase__     : str											=					int(key_split[2]		), int(key_split[4]		)
                  UpperCamelCase__     : Dict											=					config.vision_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase__     : Union[str, Any]											=					val[:dim, :]
                        UpperCamelCase__     : Optional[Any]											=					val[dim : dim * 2, :]
                        UpperCamelCase__     : str											=					val[-dim:, :]
                  else:
                        UpperCamelCase__     : List[Any]											=					val[:dim]
                        UpperCamelCase__     : Union[str, Any]											=					val[dim : dim * 2]
                        UpperCamelCase__     : int											=					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__     : List[Any]											=					key.split('''.'''		)
                  UpperCamelCase__     : int											=					int(key_split[3]		)
                  UpperCamelCase__     : List[Any]											=					config.text_config.hidden_size
                  if "weight" in key:
                        UpperCamelCase__     : Optional[Any]											=					val[:dim, :]
                        UpperCamelCase__     : Optional[int]											=					val[
                            dim : dim * 2, :
                        ]
                        UpperCamelCase__     : Optional[Any]											=					val[-dim:, :]
                  else:
                        UpperCamelCase__     : Any											=					val[:dim]
                        UpperCamelCase__     : int											=					val[dim : dim * 2]
                        UpperCamelCase__     : Optional[int]											=					val[-dim:]
            else:
                  UpperCamelCase__     : Optional[int]											=					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__     : List[str]											=					val.squeeze_()
                  else:
                        UpperCamelCase__     : Union[str, Any]											=					val

      return orig_state_dict


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

      UpperCamelCase__     : Any											=					torch.load(UpperCamelCase__			,		map_location='''cpu'''		)['''model''']
      UpperCamelCase__     : 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__     : Optional[Any]											=					CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
      UpperCamelCase__     : Optional[Any]											=					prepare_img()
      UpperCamelCase__     : List[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__     : str											=					model(**UpperCamelCase__		)

      if model_name == "groupvit-gcc-yfcc":
            UpperCamelCase__     : Union[str, Any]											=					torch.tensor([[13.3523, 6.3629]]		)
      elif model_name == "groupvit-gcc-redcaps":
            UpperCamelCase__     : int											=					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 logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCamelCase  ={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

lowerCamelCase  =logging.WARNING


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					os.getenv('''DATASETS_VERBOSITY'''			,		UpperCamelCase__		)
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                      f'''has to be one of: { ", ".join(log_levels.keys()		) }'''		)
      return _default_log_level


def 				SCREAMING_SNAKE_CASE_   (      ):
      return __name__.split('''.'''		)[0]


def 				SCREAMING_SNAKE_CASE_   (      ):
      return logging.getLogger(_get_library_name()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      # Apply our default configuration to the library root logger.
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(_get_default_logging_level()		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					_get_library_root_logger()
      library_root_logger.setLevel(logging.NOTSET		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__ = None		):
      if name is None:
            UpperCamelCase__     : Union[str, Any]											=					_get_library_name()
      return logging.getLogger(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return _get_library_root_logger().getEffectiveLevel()


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      _get_library_root_logger().setLevel(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      return set_verbosity(UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : str											=					False


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : List[Any]											=					True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()




class 					_lowerCamelCase			:





     """simple docstring"""







     def __init__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> int:  # pylint: disable=unused-argument




           """simple docstring"""







           UpperCamelCase__     : Dict											=					args[0] if args else None






     def __iter__(				self	)	-> Dict:




           """simple docstring"""







           return iter(self._iterator	)






     def __getattr__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           def empty_fn(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	):  # pylint: disable=unused-argument
                 return

           return empty_fn






     def __enter__(				self	)	-> Optional[int]:




           """simple docstring"""







           return self






     def __exit__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           return
lowerCamelCase  =True




class 					_lowerCamelCase			:





     """simple docstring"""







     def __call__(				self    ,    *__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=False    ,    **__SCREAMING_SNAKE_CASE	)	-> int:




           """simple docstring"""







           if _tqdm_active and not disable:
                 return tqdm_lib.tqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           else:
                 return EmptyTqdm(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : Any											=					None
           if _tqdm_active:
                 return tqdm_lib.tqdm.set_lock(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           if _tqdm_active:
                 return tqdm_lib.tqdm.get_lock()
lowerCamelCase  =_tqdm_cls()


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      return bool(_tqdm_active		)


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Dict											=					True


def 				SCREAMING_SNAKE_CASE_   (      ):
      global _tqdm_active
      UpperCamelCase__     : Union[str, Any]											=					False


