





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def      _lowerCAmelCase		(	_lowerCAmelCase  ):
   '''simple docstring'''


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

   return name



def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase  ):
   '''simple docstring'''


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

   return orig_state_dict



def      _lowerCAmelCase		(	):
   '''simple docstring'''


   A_  :					List[str]         =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   A_  :					Union[str, Any]         =						Image.open(requests.get(_lowerCAmelCase	,stream=_lowerCAmelCase  ).raw  )
   return im



@torch.no_grad()
def      _lowerCAmelCase		(	_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase="groupvit-gcc-yfcc"	,_lowerCAmelCase=False  ):
   '''simple docstring'''


   A_  :					int         =						GroupViTConfig()
   A_  :					List[str]         =						GroupViTModel(_lowerCAmelCase  ).eval()

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

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

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














from typing import Any

import numpy as np
def 		_a       ( a					:np.ndarray					)				->     bool:
 return np.array_equal(a      ,				matrix.conjugate().T					)
def 		_a       ( a					:np.ndarray      ,				a					:np.ndarray					)				->     Any:
 a						    = v.conjugate().T
 a						    = v_star.dot(a					)
 assert isinstance(a      ,				np.ndarray					)
 return (v_star_dot.dot(a					)) / (v_star.dot(a					))
def 		_a       ( )				->     None:
 a						    = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]					)
 a						    = np.array([[1], [2], [3]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 print(rayleigh_quotient(a      ,				a					)					)

 a						    = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]					)
 assert is_hermitian(a					), F"""{a} is not hermitian."""
 assert rayleigh_quotient(a      ,				a					) == float(3					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     tests()


