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     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= None






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=0.9_9_9     ,  lowerCAmelCase__="cosine"     ,  ):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase__     ):
                     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(lowerCAmelCase__     ):
                     return math.exp(t * -1_2.0     )

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

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



class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 @register_to_config
 def __init__(      self :  Dict      ,   lowercase__ :  int = 1_000      ,   lowercase__ :  str = "fixed_small_log"      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[float] = 1.0      ,   lowercase__ :  str = "epsilon"      ,   lowercase__ :  str = "squaredcos_cap_v2"      ,   ):







        '''simple docstring'''



        if beta_schedule != "squaredcos_cap_v2":
               raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

        lowerCAmelCase__					     = betas_for_alpha_bar(lowercase__)

        lowerCAmelCase__					     = 1.0 - self.betas
        lowerCAmelCase__					     = torch.cumprod(self.alphas      ,   dim=0)
        lowerCAmelCase__					     = torch.tensor(1.0)

        # standard deviation of the initial noise distribution
        lowerCAmelCase__					     = 1.0

        # setable values
        lowerCAmelCase__					     = None
        lowerCAmelCase__					     = torch.from_numpy(np.arange(0      ,   lowercase__)[::-1].copy())

        lowerCAmelCase__					     = variance_type




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  Optional[int] = None):







        '''simple docstring'''



        return sample




 def 		__snake_case						(      self :  str      ,   lowercase__ :  int      ,   lowercase__ :  Union[str, torch.device] = None):







        '''simple docstring'''



        lowerCAmelCase__					     = num_inference_steps
        lowerCAmelCase__					     = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
        lowerCAmelCase__					     = (np.arange(0      ,   lowercase__) * step_ratio).round()[::-1].copy().astype(np.intaa)
        lowerCAmelCase__					     = torch.from_numpy(lowercase__).to(lowercase__)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Tuple=None      ,   lowercase__ :  Any=None      ,   lowercase__ :  Any=None):







        '''simple docstring'''



        if prev_timestep is None:
               lowerCAmelCase__					     = t - 1

        lowerCAmelCase__					     = self.alphas_cumprod[t]
        lowerCAmelCase__					     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowerCAmelCase__					     = 1 - alpha_prod_t
        lowerCAmelCase__					     = 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
               lowerCAmelCase__					     = self.betas[t]
        else:
               lowerCAmelCase__					     = 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
        lowerCAmelCase__					     = beta_prod_t_prev / beta_prod_t * beta

        if variance_type is None:
               lowerCAmelCase__					     = self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small_log":
               lowerCAmelCase__					     = torch.log(torch.clamp(lowercase__      ,   min=1e-20))
               lowerCAmelCase__					     = torch.exp(0.5 * variance)
        elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               lowerCAmelCase__					     = variance.log()
               lowerCAmelCase__					     = beta.log()

               lowerCAmelCase__					     = (predicted_variance + 1) / 2
               lowerCAmelCase__					     = frac * max_log + (1 - frac) * min_log

        return variance




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  int      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Dict=None      ,   lowercase__ :  bool = True      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = timestep

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

        # 1. compute alphas, betas
        if prev_timestep is None:
               lowerCAmelCase__					     = t - 1

        lowerCAmelCase__					     = self.alphas_cumprod[t]
        lowerCAmelCase__					     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
        lowerCAmelCase__					     = 1 - alpha_prod_t
        lowerCAmelCase__					     = 1 - alpha_prod_t_prev

        if prev_timestep == t - 1:
               lowerCAmelCase__					     = self.betas[t]
               lowerCAmelCase__					     = self.alphas[t]
        else:
               lowerCAmelCase__					     = 1 - alpha_prod_t / alpha_prod_t_prev
               lowerCAmelCase__					     = 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":
               lowerCAmelCase__					     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
               lowerCAmelCase__					     = 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:
               lowerCAmelCase__					     = torch.clamp(
                   lowercase__      ,   -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
        lowerCAmelCase__					     = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
        lowerCAmelCase__					     = 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
        lowerCAmelCase__					     = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        lowerCAmelCase__					     = 0
        if t > 0:
               lowerCAmelCase__					     = randn_tensor(
                   model_output.shape      ,   dtype=model_output.dtype      ,   generator=lowercase__      ,   device=model_output.device)

               lowerCAmelCase__					     = self._get_variance(
                   lowercase__      ,   predicted_variance=lowercase__      ,   prev_timestep=lowercase__      ,   )

               if self.variance_type == "fixed_small_log":
                      lowerCAmelCase__					     = variance
               elif self.variance_type == "learned_range":
                      lowerCAmelCase__					     = (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.')

               lowerCAmelCase__					     = variance * variance_noise

        lowerCAmelCase__					     = pred_prev_sample + variance

        if not return_dict:
               return (pred_prev_sample,)

        return UnCLIPSchedulerOutput(prev_sample=lowercase__      ,   pred_original_sample=lowercase__)




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  torch.IntTensor      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.alphas_cumprod.to(device=original_samples.device      ,   dtype=original_samples.dtype)
        lowerCAmelCase__					     = timesteps.to(original_samples.device)

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

        lowerCAmelCase__					     = (1 - alphas_cumprod[timesteps]) ** 0.5
        lowerCAmelCase__					     = sqrt_one_minus_alpha_prod.flatten()
        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
               lowerCAmelCase__					     = sqrt_one_minus_alpha_prod.unsqueeze(-1)

        lowerCAmelCase__					     = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
        return noisy_samples

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       if not len(lowerCAmelCase__     ) == rows * cols:
              raise ValueError('The specified number of rows and columns are not correct.'     )

       lowerCAmelCase__     , lowerCAmelCase__					     = imgs[0].size
       lowerCAmelCase__					     = Image.new('RGB'     ,  size=(cols * w, rows * h)     )
       lowerCAmelCase__     , lowerCAmelCase__					     = grid.size

       for i, img in enumerate(lowerCAmelCase__     ):
              grid.paste(lowerCAmelCase__     ,  box=(i % cols * w, i // cols * h)     )
       return grid






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  rows=_rows     ,  cols=num_images_per_prompt // _rows     )
       return grid, images


lowerCAmelCase__     						=			parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__     						=			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__     						=			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__     						=			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__     						=			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase__     						=			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__     						=			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
    lowerCAmelCase__     						=			load(args.pretrained_model_name_or_path, model=unet)
    unet.eval()
    setattr(pipeline, 'unet', unet)
else:
    lowerCAmelCase__     						=			unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__     						=			pipeline.to(unet.device)
lowerCAmelCase__						,		lowerCAmelCase__     						=			generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__     						=			os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
    image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
