





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_					(   UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :			Optional[torch.FloatTensor]  			=	None

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:List[str]=0.999      ,				snake_case__	:Dict="cosine"      ,				)   ->    List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(snake_case__	:Optional[int]  ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

  elif alpha_transform_type == "exp":

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

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

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







class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase						):







       """simple docstring"""





       @register_to_config
       def __init__(     self						:						Union[str, Any]    ,__A						:						int = 1000    ,__A						:						str = "fixed_small_log"    ,__A						:						bool = True    ,__A						:						Optional[float] = 1.0    ,__A						:						str = "epsilon"    ,__A						:						str = "squaredcos_cap_v2"    ,)    ->       int:
         if beta_schedule != "squaredcos_cap_v2":
           raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''	)

         _lowercase						    =			betas_for_alpha_bar(__A	)

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

         # standard deviation of the initial noise distribution
         _lowercase						    =			1.0

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

         _lowercase						    =			variance_type







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						torch.FloatTensor    ,__A						:						Optional[int] = None	)    ->       torch.FloatTensor:
         return sample







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						int    ,__A						:						Union[str, torch.device] = None	)    ->       List[str]:
         _lowercase						    =			num_inference_steps
         _lowercase						    =			(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         _lowercase						    =			(np.arange(0    ,__A	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
         _lowercase						    =			torch.from_numpy(__A	).to(__A	)







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						List[Any]    ,__A						:						str=None    ,__A						:						str=None    ,__A						:						List[str]=None	)    ->       List[str]:
         if prev_timestep is None:
           _lowercase						    =			t - 1

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

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

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

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

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

         return variance







       def  __UpperCAmelCase			(     self						:						Any    ,__A						:						torch.FloatTensor    ,__A						:						int    ,__A						:						torch.FloatTensor    ,__A						:						Optional[int] = None    ,__A						:						Optional[int]=None    ,__A						:						bool = True    ,)    ->       Union[UnCLIPSchedulerOutput, Tuple]:
         _lowercase						    =			timestep

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

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

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

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

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

           _lowercase						    =			self._get_variance(
               __A    ,predicted_variance=__A    ,prev_timestep=__A    ,)

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

           _lowercase						    =			variance * variance_noise

         _lowercase						    =			pred_prev_sample + variance

         if not return_dict:
           return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__A    ,pred_original_sample=__A	)







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						torch.FloatTensor    ,__A						:						torch.FloatTensor    ,__A						:						torch.IntTensor    ,)    ->       torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
         _lowercase						    =			self.alphas_cumprod.to(device=original_samples.device    ,dtype=original_samples.dtype	)
         _lowercase						    =			timesteps.to(original_samples.device	)

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

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

         _lowercase						    =			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       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

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

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = 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()))))
__UpperCamelCase:	List[str] = 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)))


