





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase





def    __UpperCamelCase      (    a) ->None:
					lowerCamelCase__    ,		lowerCamelCase__       =       analyze_text(a)
					lowerCamelCase__       =       list(" " + ascii_lowercase)
					# what is our total sum of probabilities.
					lowerCamelCase__       =       sum(single_char_strings.values())

					# one length string
					lowerCamelCase__       =       0
					# for each alpha we go in our dict and if it is in it we calculate entropy
					for ch in my_alphas:
										if ch in single_char_strings:
															lowerCamelCase__       =       single_char_strings[ch]
															lowerCamelCase__       =       my_str / all_sum
															my_fir_sum += prob * math.loga(a)  # entropy formula.

    # print entropy
					print(f"{round(-1 * my_fir_sum):.1f}")

					# two len string
					lowerCamelCase__       =       sum(two_char_strings.values())
					lowerCamelCase__       =       0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															lowerCamelCase__       =       cha + cha
															if sequence in two_char_strings:
																				lowerCamelCase__       =       two_char_strings[sequence]
																				lowerCamelCase__       =       int(a) / all_sum
																				my_sec_sum += prob * math.loga(a)

    # print second entropy
					print(f"{round(-1 * my_sec_sum):.1f}")

					# print the difference between them
					print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)):.1f}")





def    __UpperCamelCase      (    a) ->tuple[dict, dict]:
					lowerCamelCase__       =       Counter()  # type: ignore
					lowerCamelCase__       =       Counter()  # type: ignore
					single_char_strings[text[-1]] += 1

					# first case when we have space at start.
					two_char_strings[" " + text[0]] += 1
					for i in range(0,     len(a) - 1):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings





def    __UpperCamelCase      (    ) ->Optional[Any]:
					import doctest

					doctest.testmod()
					# text = (
					#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
					#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
					#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
					#     "beloved evident through no service elderly is. Blind there if every no so "
					#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
					#     "of message cottage windows do besides against uncivil.  Delightful "
					#     "unreserved impossible few estimating men favourable see entreaties. She "
					#     "propriety immediate was improving. He or entrance humoured likewise "
					#     "moderate. Much nor game son say feel. Fat make met can must form into "
					#     "gate. Me we offending prevailed discovery. "
					# )

					# calculate_prob(text)


if __name__ == "__main__":
		main()








import importlib

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





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





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





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

