





'''simple docstring'''






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   _lowercase						(							a				):
   _UpperCamelCase       		=   42
   _UpperCamelCase       		=   None







def 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : List[str]=0.9_9_9    ,       UpperCamelCase__    : str="cosine"    ,       )		->  int:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase__    : Tuple		):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase__    : Optional[Any]		):
            return math.exp(t * -12.0		)

    else:
        raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

    A       :       Union[str, Any]							=						[]
    for i in range(UpperCamelCase__		):
        A       :       str							=						i / num_diffusion_timesteps
        A       :       Union[str, Any]							=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(UpperCamelCase__		) / alpha_bar_fn(UpperCamelCase__		)    ,       UpperCamelCase__		)		)
    return torch.tensor(UpperCamelCase__    ,       dtype=torch.floataa		)





class   _lowercase						(							a		,					a				):


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase = 1_000    ,   _UpperCAmelCase = "fixed_small_log"    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1.0    ,   _UpperCAmelCase = "epsilon"    ,   _UpperCAmelCase = "squaredcos_cap_v2"    ,   ):
       if beta_schedule != "squaredcos_cap_v2":
           raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''					)

       A       :       str							=						betas_for_alpha_bar(_UpperCAmelCase					)

       A       :       str							=						1.0 - self.betas
       A       :       Optional[int]							=						torch.cumprod(self.alphas    ,   dim=0					)
       A       :       Tuple							=						torch.tensor(1.0					)

       # standard deviation of the initial noise distribution
       A       :       List[str]							=						1.0

       # setable values
       A       :       Tuple							=						None
       A       :       str							=						torch.from_numpy(np.arange(0    ,   _UpperCAmelCase					)[::-1].copy()					)

       A       :       Any							=						variance_type


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       return sample


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       List[Any]							=						num_inference_steps
       A       :       Dict							=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       A       :       Dict							=						(np.arange(0    ,   _UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
       A       :       Optional[int]							=						torch.from_numpy(_UpperCAmelCase					).to(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if prev_timestep is None:
           A       :       int							=						t - 1

       A       :       Optional[int]							=						self.alphas_cumprod[t]
       A       :       Optional[int]							=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       A       :       Optional[Any]							=						1 - alpha_prod_t
       A       :       str							=						1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
           A       :       Union[str, Any]							=						self.betas[t]
       else:
           A       :       int							=						1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       A       :       List[Any]							=						beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
           A       :       Tuple							=						self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
           A       :       str							=						torch.log(torch.clamp(_UpperCAmelCase    ,   min=1E-2_0					)					)
           A       :       List[str]							=						torch.exp(0.5 * variance					)
       elif variance_type == "learned_range":
           # NOTE difference with DDPM scheduler
           A       :       Any							=						variance.log()
           A       :       Optional[Any]							=						beta.log()

           A       :       str							=						(predicted_variance + 1) / 2
           A       :       Union[str, Any]							=						frac * max_log + (1 - frac) * min_log

       return variance


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase = True    ,   ):
       A       :       Optional[Any]							=						timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
           A       :       Dict							=						torch.split(_UpperCAmelCase    ,   sample.shape[1]    ,   dim=1					)
       else:
           A       :       Tuple							=						None

       # 1. compute alphas, betas
       if prev_timestep is None:
           A       :       List[str]							=						t - 1

       A       :       Optional[int]							=						self.alphas_cumprod[t]
       A       :       List[Any]							=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       A       :       str							=						1 - alpha_prod_t
       A       :       Tuple							=						1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
           A       :       Union[str, Any]							=						self.betas[t]
           A       :       Optional[Any]							=						self.alphas[t]
       else:
           A       :       List[str]							=						1 - alpha_prod_t / alpha_prod_t_prev
           A       :       Optional[int]							=						1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
           A       :       Any							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
           A       :       Union[str, Any]							=						model_output
       else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
               ''' for the UnCLIPScheduler.'''					)

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
           A       :       str							=						torch.clamp(
               _UpperCAmelCase    ,   -self.config.clip_sample_range    ,   self.config.clip_sample_range					)

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A       :       List[Any]							=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       A       :       int							=						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A       :       List[Any]							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       A       :       str							=						0
       if t > 0:
           A       :       Dict							=						randn_tensor(
               model_output.shape    ,   dtype=model_output.dtype    ,   generator=_UpperCAmelCase    ,   device=model_output.device					)

           A       :       Optional[Any]							=						self._get_variance(
               _UpperCAmelCase    ,   predicted_variance=_UpperCAmelCase    ,   prev_timestep=_UpperCAmelCase    ,   )

           if self.variance_type == "fixed_small_log":
               A       :       int							=						variance
           elif self.variance_type == "learned_range":
               A       :       str							=						(0.5 * variance).exp()
           else:
               raise ValueError(
                   f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                   ''' for the UnCLIPScheduler.'''					)

           A       :       Union[str, Any]							=						variance * variance_noise

       A       :       Union[str, Any]							=						pred_prev_sample + variance

       if not return_dict:
           return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=_UpperCAmelCase    ,   pred_original_sample=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
       A       :       Dict							=						self.alphas_cumprod.to(device=original_samples.device    ,   dtype=original_samples.dtype					)
       A       :       Optional[Any]							=						timesteps.to(original_samples.device					)

       A       :       Tuple							=						alphas_cumprod[timesteps] ** 0.5
       A       :       Tuple							=						sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
           A       :       Any							=						sqrt_alpha_prod.unsqueeze(-1					)

       A       :       Any							=						(1 - alphas_cumprod[timesteps]) ** 0.5
       A       :       Tuple							=						sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
           A       :       List[str]							=						sqrt_one_minus_alpha_prod.unsqueeze(-1					)

       A       :       Union[str, Any]							=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples







'''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   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase = "cpu"    ,   _UpperCAmelCase = "openai/clip-vit-large-patch14"					):
       A       :       List[Any]							=						device
       A       :       Optional[int]							=						CLIPTokenizerFast.from_pretrained(_UpperCAmelCase					)
       A       :       Dict							=						[0.48145466, 0.4578275, 0.40821073]
       A       :       List[str]							=						[0.26862954, 0.26130258, 0.27577711]
       A       :       List[Any]							=						torchvision.transforms.Normalize(self.image_mean    ,   self.image_std					)
       A       :       Dict							=						torchvision.transforms.Resize(224					)
       A       :       List[str]							=						torchvision.transforms.CenterCrop(224					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						self.resize(_UpperCAmelCase					)
       A       :       Optional[int]							=						self.center_crop(_UpperCAmelCase					)
       A       :       List[Any]							=						self.normalize(_UpperCAmelCase					)
       return images


   def __call__( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase					):
       A       :       Any							=						self.tokenizer(text=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       A       :       List[str]							=						self.preprocess_img(_UpperCAmelCase					)
       A       :       Any							=						{key: value.to(self.device					) for (key, value) in encoding.items()}
       return encoding







class   _lowercase						(							nn.Module				):


   def __init__( self    ,   _UpperCAmelCase=10    ,   _UpperCAmelCase=0.01    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="image"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   ):
       super().__init__()
       A       :       Any							=						None
       A       :       Dict							=						device if device else get_device()
       if vqgan:
           A       :       Union[str, Any]							=						vqgan
       else:
           A       :       List[str]							=						load_vqgan(self.device    ,   conf_path=_UpperCAmelCase    ,   ckpt_path=_UpperCAmelCase					)
       self.vqgan.eval()
       if clip:
           A       :       str							=						clip
       else:
           A       :       Tuple							=						CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''					)
       self.clip.to(self.device					)
       A       :       Dict							=						ProcessorGradientFlow(device=self.device					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=True					):
       A       :       Any							=						[]
       if output_path is None:
           A       :       List[str]							=						'''./animation.gif'''
       if input_path is None:
           A       :       Union[str, Any]							=						self.save_path
       A       :       Optional[int]							=						sorted(glob(input_path + '''/*'''					)					)
       if not len(_UpperCAmelCase					):
           raise ValueError(
               '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
               ''' function?)'''					)
       if len(_UpperCAmelCase					) == 1:
           print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''					)
       A       :       Optional[int]							=						total_duration / len(_UpperCAmelCase					)
       A       :       str							=						[frame_duration] * len(_UpperCAmelCase					)
       if extend_frames:
           A       :       Any							=						1.5
           A       :       Optional[int]							=						3
       for file_name in paths:
           if file_name.endswith('''.png'''					):
               images.append(imageio.imread(_UpperCAmelCase					)					)
       imageio.mimsave(_UpperCAmelCase    ,   _UpperCAmelCase    ,   duration=_UpperCAmelCase					)
       print(f'''gif saved to {output_path}'''					)


   def 					snake_case				( self    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if not (path or img):
           raise ValueError('''Input either path or tensor'''					)
       if img is not None:
           raise NotImplementedError
       A       :       Tuple							=						preprocess(Image.open(_UpperCAmelCase					)    ,   target_image_size=256					).to(self.device					)
       A       :       List[str]							=						preprocess_vqgan(_UpperCAmelCase					)
       A,	*A       :       Dict							=						self.vqgan.encode(_UpperCAmelCase					)
       return z


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       int							=						self.latent.detach().requires_grad_()
       A       :       List[str]							=						base_latent + transform_vector
       if self.quantize:
           A,	*A       :       List[str]							=						self.vqgan.quantize(_UpperCAmelCase					)
       else:
           A       :       Any							=						trans_latent
       return self.vqgan.decode(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):
       A       :       List[str]							=						self.clip_preprocessor(text=_UpperCAmelCase    ,   images=_UpperCAmelCase    ,   return_tensors='''pt'''    ,   padding=_UpperCAmelCase					)
       A       :       int							=						self.clip(**_UpperCAmelCase					)
       A       :       Dict							=						clip_outputs.logits_per_image
       if weights is not None:
           A       :       Optional[Any]							=						similarity_logits * weights
       return similarity_logits.sum()


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						self._get_clip_similarity(pos_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=(1 / pos_prompts['''weights'''])					)
       if neg_prompts:
           A       :       str							=						self._get_clip_similarity(neg_prompts['''prompts''']    ,   _UpperCAmelCase    ,   weights=neg_prompts['''weights''']					)
       else:
           A       :       Union[str, Any]							=						torch.tensor([1]    ,   device=self.device					)
       A       :       Optional[int]							=						-torch.log(_UpperCAmelCase					) + torch.log(_UpperCAmelCase					)
       return loss


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Optional[int]							=						torch.randn_like(self.latent    ,   requires_grad=_UpperCAmelCase    ,   device=self.device					)
       A       :       Optional[int]							=						torch.optim.Adam([vector]    ,   lr=self.lr					)

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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       wandb.init(reinit=_UpperCAmelCase    ,   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:
           A       :       Optional[Any]							=						Image.open(_UpperCAmelCase					)
           A       :       Dict							=						image.resize((256, 256)					)
           wandb.log('''Original Image'''    ,   wandb.Image(_UpperCAmelCase					)					)


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       if image_path:
           A       :       List[str]							=						self._get_latent(_UpperCAmelCase					)
       else:
           A       :       List[Any]							=						torch.randn(self.latent_dim    ,   device=self.device					)
       if self.log:
           self._init_logging(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)

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

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

       A       :       List[Any]							=						loop_post_process(_UpperCAmelCase					)
       for iter, transformed_img in enumerate(self._optimize_CLIP(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)					):
           if show_intermediate:
               show_pil(_UpperCAmelCase					)
           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(_UpperCAmelCase					)}					)
       if show_final:
           show_pil(_UpperCAmelCase					)
       if save_final:
           transformed_img.save(os.path.join(self.save_path    ,   f'''iter_{iter:03d}_final.png'''					)					)
