


"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__snake_case             =   {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
__snake_case             =   """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
__snake_case             =   """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						dict[str, int]:


    """simple docstring"""





    snake_case   :				Any				 =   {letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def   __lowerCAmelCase						(						lowercase  :  tuple			)     ->						str:


    """simple docstring"""





    return x[0]







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				Tuple				 =   get_letter_count(lowercase			)
    snake_case   :				dict[int, list[str]]				 =   {
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(lowercase			)

    snake_case   :				dict[int, str]				 =   {}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find						,							reverse=lowercase			)
        snake_case   :				Optional[int]				 =   "".join(freq_to_letter[freq]			)

    snake_case   :				Optional[int]				 =   list(freq_to_letter_str.items()			)
    freq_pairs.sort(key=lowercase						,							reverse=lowercase			)

    snake_case   :				list[str]				 =   [freq_pair[1] for freq_pair in freq_pairs]

    return "".join(lowercase			)







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						int:


    """simple docstring"""





    snake_case   :				str				 =   get_frequency_order(lowercase			)
    snake_case   :				Optional[Any]				 =   0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
  import doctest

  doctest.testmod()









"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int]=False			)     ->						Union[str, Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   OmegaConf.load(lowercase			)
    if display:
        print(yaml.dump(OmegaConf.to_container(lowercase			)			)			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Dict=None						,							lowercase  :  Dict=None			)     ->						Union[str, Any]:


    """simple docstring"""





    if conf_path is None:
        snake_case   :				Optional[Any]				 =   "./model_checkpoints/vqgan_only.yaml"
    snake_case   :				Union[str, Any]				 =   load_config(lowercase						,							display=lowercase			)
    snake_case   :				List[Any]				 =   VQModel(**config.model.params			)
    if ckpt_path is None:
        snake_case   :				Optional[int]				 =   "./model_checkpoints/vqgan_only.pt"
    snake_case   :				Union[str, Any]				 =   torch.load(lowercase						,							map_location=lowercase			)
    if ".ckpt" in ckpt_path:
        snake_case   :				Union[str, Any]				 =   sd["state_dict"]
    model.load_state_dict(lowercase						,							strict=lowercase			)
    model.to(lowercase			)
    del sd
    return model







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  List[str]			)     ->						List[str]:


    """simple docstring"""





    snake_case ,snake_case ,snake_case   :				List[Any]				 =   model.encode(lowercase			)
    print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
    snake_case   :				Union[str, Any]				 =   model.decode(lowercase			)
    return xrec







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  str=False			)     ->						Optional[int]:


    """simple docstring"""





    snake_case ,snake_case   :				Any				 =   string.rsplit("."						,							1			)
    if reload:
        snake_case   :				List[Any]				 =   importlib.import_module(lowercase			)
        importlib.reload(lowercase			)
    return getattr(importlib.import_module(lowercase						,							package=lowercase			)						,							cls			)







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Union[str, Any]:


    """simple docstring"""





    if "target" not in config:
        raise KeyError("Expected key `target` to instantiate."			)
    return get_obj_from_str(config["target"]			)(**config.get("params"						,							{}			)			)







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  List[str]						,							lowercase  :  Tuple=True						,							lowercase  :  Optional[Any]=True			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   instantiate_from_config(lowercase			)
    if sd is not None:
        model.load_state_dict(lowercase			)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  int						,							lowercase  :  Dict						,							lowercase  :  Union[str, Any]			)     ->						Union[str, Any]:


    """simple docstring"""





    if ckpt:
        snake_case   :				Dict				 =   torch.load(lowercase						,							map_location="cpu"			)
        snake_case   :				Any				 =   pl_sd["global_step"]
        print(F'loaded model from global step {global_step}.'			)
    else:
        snake_case   :				Any				 =   {"state_dict": None}
        snake_case   :				List[str]				 =   None
    snake_case   :				Dict				 =   load_model_from_config(config.model						,							pl_sd["state_dict"]						,							gpu=lowercase						,							eval_mode=lowercase			)["model"]
    return model, global_step





