





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

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

   return name

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

   return orig_state_dict

def    __snake_case  (    )       ->  Dict:
   A_    : Optional[Any]								=	'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    __snake_case  (    _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Union[str, Any] , _lowerCAmelCase       :       Tuple="groupvit-gcc-yfcc" , _lowerCAmelCase       :       str=False		)       ->  Optional[int]:
   A_    : Dict								=	GroupViTConfig()
   A_    : Any								=	GroupViTModel(_lowerCAmelCase		).eval()

   A_    : str								=	torch.load(_lowerCAmelCase , map_location="cpu"		)['model']
   A_    : str								=	convert_state_dict(_lowerCAmelCase , _lowerCAmelCase		)
   A_    : int								=	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_    : Any								=	CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
   A_    : Union[str, Any]								=	prepare_img()
   A_    : 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_    : List[Any]								=	model(**_lowerCAmelCase		)

   if model_name == "groupvit-gcc-yfcc":
      A_    : str								=	torch.tensor([[13.35_23, 6.36_29]]		)
   elif model_name == "groupvit-gcc-redcaps":
      A_    : Dict								=	torch.tensor([[16.18_73, 8.62_30]]		)
   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							:					Optional[int]		    =		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							:					Tuple		    =		parser.parse_args()

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







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					return str(a  ) == str(a  )[::-1]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					return int(a  ) + int(str(a  )[::-1]  )







def  _SCREAMING_SNAKE_CASE				(   a = 1_00_00  )      ->      int:
					__A	:						int       =				[]
					for num in range(1					,						a  ):
										__A	:						List[str]       =				0
										__A	:						List[Any]       =				num
										while iterations < 50:
															__A	:						str       =				sum_reverse(a  )
															iterations += 1
															if is_palindrome(a  ):
																				break
										else:
															lychrel_nums.append(a  )
					return len(a  )


if __name__ == "__main__":
				print(F"""{solution() = }""")




