






"""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        a     :







    def __init__(			self							,		_lowerCamelCase = "cpu"							,		_lowerCamelCase = "openai/clip-vit-large-patch14"       ):
      lowercase							    =       device
      lowercase							    =       CLIPTokenizerFast.from_pretrained(_lowerCamelCase       )
      lowercase							    =       [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
      lowercase							    =       [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
      lowercase							    =       torchvision.transforms.Normalize(self.image_mean							,		self.image_std       )
      lowercase							    =       torchvision.transforms.Resize(2_2_4       )
      lowercase							    =       torchvision.transforms.CenterCrop(2_2_4       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       self.resize(_lowerCamelCase       )
      lowercase							    =       self.center_crop(_lowerCamelCase       )
      lowercase							    =       self.normalize(_lowerCamelCase       )
      return images







    def __call__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       self.tokenizer(text=_lowerCamelCase							,		**_lowerCamelCase       )
      lowercase							    =       self.preprocess_img(_lowerCamelCase       )
      lowercase							    =       {key: value.to(self.device       ) for (key, value) in encoding.items()}
      return encoding


class        a     (   nn.Module	):







    def __init__(			self							,		_lowerCamelCase=1_0							,		_lowerCamelCase=0.0_1							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=False							,		_lowerCamelCase=True							,		_lowerCamelCase="image"							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		_lowerCamelCase=False							,		):
      super().__init__()
      lowercase							    =       None
      lowercase							    =       device if device else get_device()
      if vqgan:
        lowercase							    =       vqgan
      else:
        lowercase							    =       load_vqgan(self.device							,		conf_path=_lowerCamelCase							,		ckpt_path=_lowerCamelCase       )
      self.vqgan.eval()
      if clip:
        lowercase							    =       clip
      else:
        lowercase							    =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'       )
      self.clip.to(self.device       )
      lowercase							    =       ProcessorGradientFlow(device=self.device       )

      lowercase							    =       iterations
      lowercase							    =       lr
      lowercase							    =       log
      lowercase							    =       make_grid
      lowercase							    =       return_val
      lowercase							    =       quantize
      lowercase							    =       self.vqgan.decoder.z_shape







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=5							,		_lowerCamelCase=True       ):
      lowercase							    =       []
      if output_path is None:
        lowercase							    =       './animation.gif'
      if input_path is None:
        lowercase							    =       self.save_path
      lowercase							    =       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?)'       )
      lowercase							    =       total_duration / len(_lowerCamelCase       )
      lowercase							    =       [frame_duration] * len(_lowerCamelCase       )
      if extend_frames:
        lowercase							    =       1.5
        lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None       ):
      if not (path or img):
        raise ValueError('Input either path or tensor'       )
      if img is not None:
        raise NotImplementedError
      lowercase							    =       preprocess(Image.open(_lowerCamelCase       )							,		target_image_size=2_5_6       ).to(self.device       )
      lowercase							    =       preprocess_vqgan(_lowerCamelCase       )
      lowercase    ,     *lowercase							    =       self.vqgan.encode(_lowerCamelCase       )
      return z







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       self.latent.detach().requires_grad_()
      lowercase							    =       base_latent + transform_vector
      if self.quantize:
        lowercase    ,     *lowercase							    =       self.vqgan.quantize(_lowerCamelCase       )
      else:
        lowercase							    =       trans_latent
      return self.vqgan.decode(_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=None       ):
      lowercase							    =       self.clip_preprocessor(text=_lowerCamelCase							,		images=_lowerCamelCase							,		return_tensors='pt'							,		padding=_lowerCamelCase       )
      lowercase							    =       self.clip(**_lowerCamelCase       )
      lowercase							    =       clip_outputs.logits_per_image
      if weights is not None:
        lowercase							    =       similarity_logits * weights
      return similarity_logits.sum()







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self._get_clip_similarity(pos_prompts['prompts']							,		_lowerCamelCase							,		weights=(1 / pos_prompts['weights'])       )
      if neg_prompts:
        lowercase							    =       self._get_clip_similarity(neg_prompts['prompts']							,		_lowerCamelCase							,		weights=neg_prompts['weights']       )
      else:
        lowercase							    =       torch.tensor([1]							,		device=self.device       )
      lowercase							    =       -torch.log(_lowerCamelCase       ) + torch.log(_lowerCamelCase       )
      return loss







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       torch.randn_like(self.latent							,		requires_grad=_lowerCamelCase							,		device=self.device       )
      lowercase							    =       torch.optim.Adam([vector]							,		lr=self.lr       )

      for i in range(self.iterations       ):
        optim.zero_grad()
        lowercase							    =       self._add_vector(_lowerCamelCase       )
        lowercase							    =       loop_post_process(_lowerCamelCase       )
        lowercase							    =       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 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      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:
        lowercase							    =       Image.open(_lowerCamelCase       )
        lowercase							    =       image.resize((2_5_6, 2_5_6)       )
        wandb.log('Original Image'							,		wandb.Image(_lowerCamelCase       )       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      if not prompts:
        return []
      lowercase							    =       []
      lowercase							    =       []
      if isinstance(_lowerCamelCase							,		_lowerCamelCase       ):
        lowercase							    =       [prompt.strip() for prompt in prompts.split('|'       )]
      for prompt in prompts:
        if isinstance(_lowerCamelCase							,		(tuple, list)       ):
          lowercase							    =       prompt[0]
          lowercase							    =       float(prompt[1]       )
        elif ":" in prompt:
          lowercase    ,     lowercase							    =       prompt.split(':'       )
          lowercase							    =       float(_lowerCamelCase       )
        else:
          lowercase							    =       prompt
          lowercase							    =       1.0
        processed_prompts.append(_lowerCamelCase       )
        weights.append(_lowerCamelCase       )
      return {
          "prompts": processed_prompts,
          "weights": torch.tensor(_lowerCamelCase							,		device=self.device       ),
      }







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=False							,		_lowerCamelCase=True							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		):
      if image_path:
        lowercase							    =       self._get_latent(_lowerCamelCase       )
      else:
        lowercase							    =       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."
      lowercase							    =       self.process_prompts(_lowerCamelCase       )
      lowercase							    =       self.process_prompts(_lowerCamelCase       )
      if save_final and save_path is None:
        lowercase							    =       os.path.join('./outputs/'							,		'_'.join(pos_prompts['prompts']       )       )
      if not os.path.exists(_lowerCamelCase       ):
        os.makedirs(_lowerCamelCase       )
      else:
        lowercase							    =       save_path + '_' + get_timestamp()
        os.makedirs(_lowerCamelCase       )
      lowercase							    =       save_path

      lowercase							    =       self.vqgan.decode(self.latent       )[0]
      if show_intermediate:
        print('Original Image'       )
        show_pil(custom_to_pil(_lowerCamelCase       )       )

      lowercase							    =       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"""



_UpperCamelCase		:  List[str]						=		{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_UpperCamelCase		:  str						=		{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 1_2,
    'Pm': 1_5,
    'Em': 1_8,
    'Zm': 2_1,
    'Ym': 2_4,
}
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  float		,					__snake_case			:  str		,					__snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       from_type.lower().strip('s'    )
  lowercase							    =       to_type.lower().strip('s'    )

  lowercase							    =       UNIT_SYMBOL.get(__snake_case		,					__snake_case    )
  lowercase							    =       UNIT_SYMBOL.get(__snake_case		,					__snake_case    )

  if from_sanitized not in METRIC_CONVERSION:
    lowercase							    =       (
        f'Invalid \'from_type\' value: {from_type!r}.\n'
        f'Conversion abbreviations are: {", ".join(__snake_case    )}'
    )
    raise ValueError(__snake_case    )
  if to_sanitized not in METRIC_CONVERSION:
    lowercase							    =       (
        f'Invalid \'to_type\' value: {to_type!r}.\n'
        f'Conversion abbreviations are: {", ".join(__snake_case    )}'
    )
    raise ValueError(__snake_case    )
  lowercase							    =       METRIC_CONVERSION[from_sanitized]
  lowercase							    =       METRIC_CONVERSION[to_sanitized]
  lowercase							    =       1

  if from_exponent > to_exponent:
    lowercase							    =       from_exponent - to_exponent
  else:
    lowercase							    =       -(to_exponent - from_exponent)

  return value * pow(10		,					__snake_case    )


if __name__ == "__main__":
    from doctest import testmod

    testmod()


