


import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]     ):







 """simple docstring"""



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

 return name

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 for key in orig_state_dict.copy().keys():
  UpperCamelCase__					:    Tuple          =   orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

  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__					:    Any          =   int(key_split[2]     ), int(key_split[4]     )
   UpperCamelCase__					:    List[str]          =   config.vision_config.hidden_size
   if "weight" in key:
    UpperCamelCase__					:    Union[str, Any]          =   val[:dim, :]
    UpperCamelCase__					:    Dict          =   val[dim : dim * 2, :]
    UpperCamelCase__					:    Dict          =   val[-dim:, :]
   else:
    UpperCamelCase__					:    Tuple          =   val[:dim]
    UpperCamelCase__					:    Tuple          =   val[dim : dim * 2]
    UpperCamelCase__					:    Optional[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
   UpperCamelCase__					:    List[Any]          =   key.split('''.'''     )
   UpperCamelCase__					:    Any          =   int(key_split[3]     )
   UpperCamelCase__					:    Optional[Any]          =   config.text_config.hidden_size
   if "weight" in key:
    UpperCamelCase__					:    List[Any]          =   val[:dim, :]
    UpperCamelCase__					:    List[str]          =   val[
        dim : dim * 2, :
    ]
    UpperCamelCase__					:    Union[str, Any]          =   val[-dim:, :]
   else:
    UpperCamelCase__					:    Dict          =   val[:dim]
    UpperCamelCase__					:    List[str]          =   val[dim : dim * 2]
    UpperCamelCase__					:    List[Any]          =   val[-dim:]
  else:
   UpperCamelCase__					:    Dict          =   rename_key(SCREAMING_SNAKE_CASE     )
   # 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__					:    Optional[Any]          =   val.squeeze_()
   else:
    UpperCamelCase__					:    Dict          =   val

 return orig_state_dict

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    str          =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 UpperCamelCase__					:    Dict          =   Image.open(requests.get(SCREAMING_SNAKE_CASE    , stream=SCREAMING_SNAKE_CASE     ).raw     )
 return im

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Tuple    , SCREAMING_SNAKE_CASE	:				Any="groupvit-gcc-yfcc"    , SCREAMING_SNAKE_CASE	:				List[str]=False     ):







 """simple docstring"""



 UpperCamelCase__					:    List[Any]          =   GroupViTConfig()
 UpperCamelCase__					:    Optional[int]          =   GroupViTModel(SCREAMING_SNAKE_CASE     ).eval()

 UpperCamelCase__					:    Union[str, Any]          =   torch.load(SCREAMING_SNAKE_CASE    , map_location='''cpu'''     )['''model''']
 UpperCamelCase__					:    List[Any]          =   convert_state_dict(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   model.load_state_dict(SCREAMING_SNAKE_CASE    , strict=SCREAMING_SNAKE_CASE     )
 assert missing_keys == ["text_model.embeddings.position_ids"]
 assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(SCREAMING_SNAKE_CASE     ) == 0)

 # verify result
 UpperCamelCase__					:    Optional[int]          =   CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''     )
 UpperCamelCase__					:    Optional[int]          =   prepare_img()
 UpperCamelCase__					:    Dict          =   processor(text=['''a photo of a cat''', '''a photo of a dog''']    , images=SCREAMING_SNAKE_CASE    , padding=SCREAMING_SNAKE_CASE    , return_tensors='''pt'''     )

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

 if model_name == "groupvit-gcc-yfcc":
  UpperCamelCase__					:    Union[str, Any]          =   torch.tensor([[13.3523, 6.3629]]     )
 elif model_name == "groupvit-gcc-redcaps":
  UpperCamelCase__					:    Any          =   torch.tensor([[16.1873, 8.6230]]     )
 else:
  raise ValueError(F"Model name {model_name} not supported."     )
 assert torch.allclose(outputs.logits_per_image    , SCREAMING_SNAKE_CASE    , atol=1E-3     )

 processor.save_pretrained(SCREAMING_SNAKE_CASE     )
 model.save_pretrained(SCREAMING_SNAKE_CASE     )
 print('''Successfully saved processor and model to'''    , SCREAMING_SNAKE_CASE     )

 if push_to_hub:
  print('''Pushing to the hub...'''     )
  processor.push_to_hub(SCREAMING_SNAKE_CASE    , organization='''nielsr'''     )
  model.push_to_hub(SCREAMING_SNAKE_CASE    , organization='''nielsr'''     )


if __name__ == "__main__":
       __UpperCamelCase       :       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`.",
       )
       __UpperCamelCase       :       List[Any]						       =				parser.parse_args()

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






import math
import os
import sys

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   ''''''
 try:
  with open(SCREAMING_SNAKE_CASE    , '''rb'''     ) as binary_file:
   UpperCamelCase__					:    int          =   binary_file.read()
  for dat in data:
   UpperCamelCase__					:    Dict          =   F"{dat:08b}"
   result += curr_byte
  return result
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				dict[str, str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 lexicon.pop(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   last_match_id

 if math.loga(SCREAMING_SNAKE_CASE     ).is_integer():
  for curr_key in lexicon:
   UpperCamelCase__					:    Any          =   '''0''' + lexicon[curr_key]

 UpperCamelCase__					:    Dict          =   bin(SCREAMING_SNAKE_CASE     )[2:]

def 			_a  (				SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   {'''0''': '''0''', '''1''': '''1'''}
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   '''''', ''''''
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )

 for i in range(len(SCREAMING_SNAKE_CASE     )     ):
  curr_string += data_bits[i]
  if curr_string not in lexicon:
   continue

  UpperCamelCase__					:    List[str]          =   lexicon[curr_string]
  result += last_match_id
  add_key_to_lexicon(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
  index += 1
  UpperCamelCase__					:    Dict          =   ''''''

 while curr_string != "" and curr_string not in lexicon:
  curr_string += "0"

 if curr_string != "":
  UpperCamelCase__					:    Union[str, Any]          =   lexicon[curr_string]
  result += last_match_id

 return result

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    int          =   os.path.getsize(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   bin(SCREAMING_SNAKE_CASE     )[2:]
 UpperCamelCase__					:    Tuple          =   len(SCREAMING_SNAKE_CASE     )

 return "0" * (length_length - 1) + file_length_binary + compressed

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   8
 try:
  with open(SCREAMING_SNAKE_CASE    , '''wb'''     ) as opened_file:
   UpperCamelCase__					:    Optional[Any]          =   [
       to_write[i : i + byte_length]
       for i in range(0    , len(SCREAMING_SNAKE_CASE     )    , SCREAMING_SNAKE_CASE     )
   ]

   if len(result_byte_array[-1]     ) % byte_length == 0:
    result_byte_array.append('''10000000'''     )
   else:
    result_byte_array[-1] += "1" + "0" * (
        byte_length - len(result_byte_array[-1]     ) - 1
    )

   for elem in result_byte_array:
    opened_file.write(int(SCREAMING_SNAKE_CASE    , 2     ).to_bytes(1    , byteorder='''big'''     )     )
 except OSError:
  print('''File not accessible'''     )
  sys.exit()

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[Any]          =   read_file_binary(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   compress_data(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   add_file_length(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 write_file_binary(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       compress(sys.argv[1], sys.argv[2])


