




"""simple docstring"""


import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class _lowerCamelCase    :






   def __init__(		self       :		Optional[Any]   ,     UpperCamelCase       :		List[Any] = "cpu"   ,     UpperCamelCase       :		Union[str, Any] = "openai/clip-vit-large-patch14"   )			->						None:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	device
         lowerCAmelCase__      :			int   =	CLIPTokenizerFast.from_pretrained(lowerCAmelCase__   )
         lowerCAmelCase__      :			Union[str, Any]   =	[0.4814_5466, 0.457_8275, 0.4082_1073]
         lowerCAmelCase__      :			Tuple   =	[0.2686_2954, 0.2613_0258, 0.2757_7711]
         lowerCAmelCase__      :			int   =	torchvision.transforms.Normalize(self.image_mean   ,     self.image_std   )
         lowerCAmelCase__      :			Optional[int]   =	torchvision.transforms.Resize(2_24   )
         lowerCAmelCase__      :			List[str]   =	torchvision.transforms.CenterCrop(2_24   )






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		str   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.resize(lowerCAmelCase__   )
         lowerCAmelCase__      :			str   =	self.center_crop(lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[Any]   =	self.normalize(lowerCAmelCase__   )
         return images






   def __call__(		self       :		List[str]   ,     UpperCamelCase       :		Union[str, Any]=None   ,     UpperCamelCase       :		int=None   ,     **UpperCamelCase       :		Optional[Any]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.tokenizer(text=lowerCAmelCase__   ,     **lowerCAmelCase__   )
         lowerCAmelCase__      :			int   =	self.preprocess_img(lowerCAmelCase__   )
         lowerCAmelCase__      :			int   =	{key: value.to(self.device   ) for (key, value) in encoding.items()}
         return encoding






class _lowerCamelCase    (					nn.Module						):






   def __init__(		self       :		Optional[int]   ,     UpperCamelCase       :		Optional[int]=10   ,     UpperCamelCase       :		Union[str, Any]=0.01   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		List[str]=None   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		Any=False   ,     UpperCamelCase       :		Tuple=True   ,     UpperCamelCase       :		Dict="image"   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		List[Any]=False   ,     UpperCamelCase       :		int=False   ,     UpperCamelCase       :		str=False   ,     )			->						None:

         """simple docstring"""
         super().__init__()
         lowerCAmelCase__      :			Any   =	None
         lowerCAmelCase__      :			Any   =	device if device else get_device()
         if vqgan:
               lowerCAmelCase__      :			Dict   =	vqgan
         else:
               lowerCAmelCase__      :			Dict   =	load_vqgan(self.device   ,     conf_path=lowerCAmelCase__   ,     ckpt_path=lowerCAmelCase__   )
         self.vqgan.eval()
         if clip:
               lowerCAmelCase__      :			int   =	clip
         else:
               lowerCAmelCase__      :			Union[str, Any]   =	CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""   )
         self.clip.to(self.device   )
         lowerCAmelCase__      :			Any   =	ProcessorGradientFlow(device=self.device   )

         lowerCAmelCase__      :			List[str]   =	iterations
         lowerCAmelCase__      :			List[Any]   =	lr
         lowerCAmelCase__      :			List[str]   =	log
         lowerCAmelCase__      :			Dict   =	make_grid
         lowerCAmelCase__      :			Union[str, Any]   =	return_val
         lowerCAmelCase__      :			Optional[int]   =	quantize
         lowerCAmelCase__      :			Any   =	self.vqgan.decoder.z_shape






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		List[str]=None   ,     UpperCamelCase       :		int=None   ,     UpperCamelCase       :		Tuple=5   ,     UpperCamelCase       :		Dict=True   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	[]
         if output_path is None:
               lowerCAmelCase__      :			Union[str, Any]   =	"./animation.gif"
         if input_path is None:
               lowerCAmelCase__      :			Dict   =	self.save_path
         lowerCAmelCase__      :			List[str]   =	sorted(glob(input_path + """/*"""   )   )
         if not len(lowerCAmelCase__   ):
               raise ValueError(
                   """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
                   """ function?)"""   )
         if len(lowerCAmelCase__   ) == 1:
               print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""   )
         lowerCAmelCase__      :			List[str]   =	total_duration / len(lowerCAmelCase__   )
         lowerCAmelCase__      :			List[str]   =	[frame_duration] * len(lowerCAmelCase__   )
         if extend_frames:
               lowerCAmelCase__      :			Tuple   =	1.5
               lowerCAmelCase__      :			List[str]   =	3
         for file_name in paths:
               if file_name.endswith(""".png"""   ):
                     images.append(imageio.imread(lowerCAmelCase__   )   )
         imageio.mimsave(lowerCAmelCase__   ,     lowerCAmelCase__   ,     duration=lowerCAmelCase__   )
         print(f"""gif saved to {output_path}"""   )






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		List[str]=None   ,     UpperCamelCase       :		List[Any]=None   )			->						Dict:

         """simple docstring"""
         if not (path or img):
               raise ValueError("""Input either path or tensor"""   )
         if img is not None:
               raise NotImplementedError
         lowerCAmelCase__      :			Optional[int]   =	preprocess(Image.open(lowerCAmelCase__   )   ,     target_image_size=2_56   ).to(self.device   )
         lowerCAmelCase__      :			Optional[int]   =	preprocess_vqgan(lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[int]   =	self.vqgan.encode(lowerCAmelCase__   )
         return z






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		int   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.latent.detach().requires_grad_()
         lowerCAmelCase__      :			Tuple   =	base_latent + transform_vector
         if self.quantize:
               lowerCAmelCase__      :			str   =	self.vqgan.quantize(lowerCAmelCase__   )
         else:
               lowerCAmelCase__      :			List[str]   =	trans_latent
         return self.vqgan.decode(lowerCAmelCase__   )






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Union[str, Any]=None   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self.clip_preprocessor(text=lowerCAmelCase__   ,     images=lowerCAmelCase__   ,     return_tensors="""pt"""   ,     padding=lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[int]   =	self.clip(**lowerCAmelCase__   )
         lowerCAmelCase__      :			List[str]   =	clip_outputs.logits_per_image
         if weights is not None:
               lowerCAmelCase__      :			Dict   =	similarity_logits * weights
         return similarity_logits.sum()






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		List[Any]   )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self._get_clip_similarity(pos_prompts["""prompts"""]   ,     lowerCAmelCase__   ,     weights=(1 / pos_prompts["""weights"""])   )
         if neg_prompts:
               lowerCAmelCase__      :			List[str]   =	self._get_clip_similarity(neg_prompts["""prompts"""]   ,     lowerCAmelCase__   ,     weights=neg_prompts["""weights"""]   )
         else:
               lowerCAmelCase__      :			str   =	torch.tensor([1]   ,     device=self.device   )
         lowerCAmelCase__      :			List[str]   =	-torch.log(lowerCAmelCase__   ) + torch.log(lowerCAmelCase__   )
         return loss






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Tuple   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	torch.randn_like(self.latent   ,     requires_grad=lowerCAmelCase__   ,     device=self.device   )
         lowerCAmelCase__      :			Optional[Any]   =	torch.optim.Adam([vector]   ,     lr=self.lr   )

         for i in range(self.iterations   ):
               optim.zero_grad()
               lowerCAmelCase__      :			Union[str, Any]   =	self._add_vector(lowerCAmelCase__   )
               lowerCAmelCase__      :			Tuple   =	loop_post_process(lowerCAmelCase__   )
               lowerCAmelCase__      :			Dict   =	self._get_CLIP_loss(lowerCAmelCase__   ,     lowerCAmelCase__   ,     lowerCAmelCase__   )
               print("""CLIP loss"""   ,     lowerCAmelCase__   )
               if self.log:
                     wandb.log({"""CLIP Loss""": clip_loss}   )
               clip_loss.backward(retain_graph=lowerCAmelCase__   )
               optim.step()
               if self.return_val == "image":
                     yield custom_to_pil(transformed_img[0]   )
               else:
                     yield vector






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   )			->						List[str]:

         """simple docstring"""
         wandb.init(reinit=lowerCAmelCase__   ,     project="""face-editor"""   )
         wandb.config.update({"""Positive Prompts""": positive_prompts}   )
         wandb.config.update({"""Negative Prompts""": negative_prompts}   )
         wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}   )
         if image_path:
               lowerCAmelCase__      :			List[Any]   =	Image.open(lowerCAmelCase__   )
               lowerCAmelCase__      :			str   =	image.resize((2_56, 2_56)   )
               wandb.log("""Original Image"""   ,     wandb.Image(lowerCAmelCase__   )   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		int   )			->						Any:

         """simple docstring"""
         if not prompts:
               return []
         lowerCAmelCase__      :			List[Any]   =	[]
         lowerCAmelCase__      :			Tuple   =	[]
         if isinstance(lowerCAmelCase__   ,     lowerCAmelCase__   ):
               lowerCAmelCase__      :			Optional[Any]   =	[prompt.strip() for prompt in prompts.split("""|"""   )]
         for prompt in prompts:
               if isinstance(lowerCAmelCase__   ,     (tuple, list)   ):
                     lowerCAmelCase__      :			Dict   =	prompt[0]
                     lowerCAmelCase__      :			str   =	float(prompt[1]   )
               elif ":" in prompt:
                     lowerCAmelCase__      :			Tuple   =	prompt.split(""":"""   )
                     lowerCAmelCase__      :			List[str]   =	float(lowerCAmelCase__   )
               else:
                     lowerCAmelCase__      :			Dict   =	prompt
                     lowerCAmelCase__      :			Dict   =	1.0
               processed_prompts.append(lowerCAmelCase__   )
               weights.append(lowerCAmelCase__   )
         return {
             "prompts": processed_prompts,
             "weights": torch.tensor(lowerCAmelCase__   ,     device=self.device   ),
         }






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		Dict   ,     UpperCamelCase       :		str=None   ,     UpperCamelCase       :		Tuple=None   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		Union[str, Any]=False   ,     UpperCamelCase       :		Optional[Any]=True   ,     UpperCamelCase       :		Optional[int]=True   ,     UpperCamelCase       :		Tuple=None   ,     )			->						Tuple:

         """simple docstring"""
         if image_path:
               lowerCAmelCase__      :			Optional[Any]   =	self._get_latent(lowerCAmelCase__   )
         else:
               lowerCAmelCase__      :			int   =	torch.randn(self.latent_dim   ,     device=self.device   )
         if self.log:
               self._init_logging(lowerCAmelCase__   ,     lowerCAmelCase__   ,     lowerCAmelCase__   )

         assert pos_prompts, "You must provide at least one positive prompt."
         lowerCAmelCase__      :			List[str]   =	self.process_prompts(lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[Any]   =	self.process_prompts(lowerCAmelCase__   )
         if save_final and save_path is None:
               lowerCAmelCase__      :			Union[str, Any]   =	os.path.join("""./outputs/"""   ,     """_""".join(pos_prompts["""prompts"""]   )   )
         if not os.path.exists(lowerCAmelCase__   ):
               os.makedirs(lowerCAmelCase__   )
         else:
               lowerCAmelCase__      :			Optional[int]   =	save_path + "_" + get_timestamp()
               os.makedirs(lowerCAmelCase__   )
         lowerCAmelCase__      :			Optional[Any]   =	save_path

         lowerCAmelCase__      :			Optional[int]   =	self.vqgan.decode(self.latent   )[0]
         if show_intermediate:
               print("""Original Image"""   )
               show_pil(custom_to_pil(lowerCAmelCase__   )   )

         lowerCAmelCase__      :			Dict   =	loop_post_process(lowerCAmelCase__   )
         for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase__   ,     lowerCAmelCase__   ,     lowerCAmelCase__   )   ):
               if show_intermediate:
                     show_pil(lowerCAmelCase__   )
               if save_intermediate:
                     transformed_img.save(os.path.join(self.save_path   ,     f"""iter_{iter:03d}.png"""   )   )
               if self.log:
                     wandb.log({"""Image""": wandb.Image(lowerCAmelCase__   )}   )
         if show_final:
               show_pil(lowerCAmelCase__   )
         if save_final:
               transformed_img.save(os.path.join(self.save_path   ,     f"""iter_{iter:03d}_final.png"""   )   )











"""simple docstring"""


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 	lowercase_				(			__UpperCAmelCase				)						->    None:
      lowerCAmelCase__ ,   lowerCAmelCase__      :			int   =	analyze_text(__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	list(""" """ + ascii_lowercase				)
      # what is our total sum of probabilities.
      lowerCAmelCase__      :			List[str]   =	sum(single_char_strings.values()				)

      # one length string
      lowerCAmelCase__      :			List[str]   =	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__      :			List[Any]   =	single_char_strings[ch]
                  lowerCAmelCase__      :			int   =	my_str / all_sum
                  my_fir_sum += prob * math.loga(__UpperCAmelCase				)  # entropy formula.

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

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

    # 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 	lowercase_				(			__UpperCAmelCase				)						->    tuple[dict, dict]:
      lowerCAmelCase__      :			Any   =	Counter()  # type: ignore
      lowerCAmelCase__      :			Tuple   =	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(__UpperCAmelCase				) - 1				):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def 	lowercase_				(			)						->    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()





