





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

   return orig_state_dict



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


   A_     :				List[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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase="groupvit-gcc-yfcc"  ,_lowerCAmelCase=False     ):
   '''simple docstring'''


   A_     :				int			      =	GroupViTConfig()
   A_     :				Tuple			      =	GroupViTModel(_lowerCAmelCase     ).eval()

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

   with torch.no_grad():
      A_     :				Union[str, Any]			      =	model(**_lowerCAmelCase     )

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













_lowerCAmelCase 				=     """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



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


   A_     :				Any			      =	input("""Enter message: """     )
   A_     :				Dict			      =	input("""Enter key [alphanumeric]: """     )
   A_     :				Dict			      =	input("""Encrypt/Decrypt [e/d]: """     )

   if mode.lower().startswith("""e"""     ):
      A_     :				Union[str, Any]			      =	"""encrypt"""
      A_     :				Optional[int]			      =	encrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )
   elif mode.lower().startswith("""d"""     ):
      A_     :				str			      =	"""decrypt"""
      A_     :				Dict			      =	decrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )

   print(f"""\n{mode.title()}ed message:"""     )
   print(_lowerCAmelCase     )



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


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""encrypt"""     )



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


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""decrypt"""     )



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


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	0
   A_     :				int			      =	key.upper()

   for symbol in message:
      A_     :				Optional[int]			      =	LETTERS.find(symbol.upper()     )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index]     )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index]     )

         num %= len(_lowerCAmelCase     )

         if symbol.isupper():
            translated.append(LETTERS[num]     )
         elif symbol.islower():
            translated.append(LETTERS[num].lower()     )

         key_index += 1
         if key_index == len(_lowerCAmelCase     ):
            A_     :				Dict			      =	0
      else:
         translated.append(_lowerCAmelCase     )
   return "".join(_lowerCAmelCase     )


if __name__ == "__main__":
      main()






