import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






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'''






 UpperCAmelCase_     			= [e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase_     			= 2




 @register_to_config
 def __init__(      self :  int      ,   lowercase__ :  int = 1_000      ,   lowercase__ :  float = 0.00_085      ,   lowercase__ :  float = 0.012      ,   lowercase__ :  str = "linear"      ,   lowercase__ :  Optional[Union[np.ndarray, List[float]]] = None      ,   lowercase__ :  str = "epsilon"      ,   lowercase__ :  str = "linspace"      ,   lowercase__ :  int = 0      ,   ):







        '''simple docstring'''



        if trained_betas is not None:
               lowerCAmelCase__					     = torch.tensor(lowercase__      ,   dtype=torch.floataa)
        elif beta_schedule == "linear":
               lowerCAmelCase__					     = torch.linspace(lowercase__      ,   lowercase__      ,   lowercase__      ,   dtype=torch.floataa)
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               lowerCAmelCase__					     = (
                   torch.linspace(beta_start**0.5      ,   beta_end**0.5      ,   lowercase__      ,   dtype=torch.floataa) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               lowerCAmelCase__					     = betas_for_alpha_bar(lowercase__)
        else:
               raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}""")

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

        #  set all values
        self.set_timesteps(lowercase__      ,   lowercase__      ,   lowercase__)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[int]=None):







        '''simple docstring'''



        if schedule_timesteps is None:
               lowerCAmelCase__					     = self.timesteps

        lowerCAmelCase__					     = (schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter) == 0:
               lowerCAmelCase__					     = 1 if len(lowercase__) > 1 else 0
        else:
               lowerCAmelCase__					     = timestep.cpu().item() if torch.is_tensor(lowercase__) else timestep
               lowerCAmelCase__					     = self._index_counter[timestep_int]

        return indices[pos].item()




 @property
 def 		__snake_case						(      self :  Optional[Any]):







        '''simple docstring'''



        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5




 def 		__snake_case						(      self :  str      ,   lowercase__ :  torch.FloatTensor      ,   lowercase__ :  Union[float, torch.FloatTensor]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.index_for_timestep(lowercase__)

        if self.state_in_first_order:
               lowerCAmelCase__					     = self.sigmas[step_index]
        else:
               lowerCAmelCase__					     = self.sigmas_interpol[step_index]

        lowerCAmelCase__					     = sample / ((sigma**2 + 1) ** 0.5)
        return sample




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







        '''simple docstring'''



        lowerCAmelCase__					     = num_inference_steps

        lowerCAmelCase__					     = num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
               lowerCAmelCase__					     = np.linspace(0      ,   num_train_timesteps - 1      ,   lowercase__      ,   dtype=lowercase__)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               lowerCAmelCase__					     = num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowerCAmelCase__					     = (np.arange(0      ,   lowercase__) * step_ratio).round()[::-1].copy().astype(lowercase__)
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               lowerCAmelCase__					     = num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               lowerCAmelCase__					     = (np.arange(lowercase__      ,   0      ,   -step_ratio)).round().copy().astype(lowercase__)
               timesteps -= 1
        else:
               raise ValueError(
                   F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

        lowerCAmelCase__					     = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
        lowerCAmelCase__					     = torch.from_numpy(np.log(lowercase__)).to(lowercase__)

        lowerCAmelCase__					     = np.interp(lowercase__      ,   np.arange(0      ,   len(lowercase__))      ,   lowercase__)
        lowerCAmelCase__					     = np.concatenate([sigmas, [0.0]]).astype(np.floataa)
        lowerCAmelCase__					     = torch.from_numpy(lowercase__).to(device=lowercase__)

        # interpolate sigmas
        lowerCAmelCase__					     = sigmas.log().lerp(sigmas.roll(1).log()      ,   0.5).exp()

        lowerCAmelCase__					     = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
        lowerCAmelCase__					     = torch.cat(
            [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

        if str(lowercase__).startswith('mps'):
               # mps does not support float64
               lowerCAmelCase__					     = torch.from_numpy(lowercase__).to(lowercase__      ,   dtype=torch.floataa)
        else:
               lowerCAmelCase__					     = torch.from_numpy(lowercase__).to(lowercase__)

        # interpolate timesteps
        lowerCAmelCase__					     = self.sigma_to_t(lowercase__).to(lowercase__      ,   dtype=timesteps.dtype)
        lowerCAmelCase__					     = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])      ,   dim=-1).flatten()

        lowerCAmelCase__					     = torch.cat([timesteps[:1], interleaved_timesteps])

        lowerCAmelCase__					     = None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        lowerCAmelCase__					     = defaultdict(lowercase__)




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = sigma.log()

        # get distribution
        lowerCAmelCase__					     = log_sigma - self.log_sigmas[:, None]

        # get sigmas range
        lowerCAmelCase__					     = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
        lowerCAmelCase__					     = low_idx + 1

        lowerCAmelCase__					     = self.log_sigmas[low_idx]
        lowerCAmelCase__					     = self.log_sigmas[high_idx]

        # interpolate sigmas
        lowerCAmelCase__					     = (low - log_sigma) / (low - high)
        lowerCAmelCase__					     = w.clamp(0      ,   1)

        # transform interpolation to time range
        lowerCAmelCase__					     = (1 - w) * low_idx + w * high_idx
        lowerCAmelCase__					     = t.view(sigma.shape)
        return t




 @property
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        return self.sample is None




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Union[torch.FloatTensor, np.ndarray]      ,   lowercase__ :  Union[float, torch.FloatTensor]      ,   lowercase__ :  Union[torch.FloatTensor, np.ndarray]      ,   lowercase__ :  bool = True      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self.index_for_timestep(lowercase__)

        # advance index counter by 1
        lowerCAmelCase__					     = timestep.cpu().item() if torch.is_tensor(lowercase__) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
               lowerCAmelCase__					     = self.sigmas[step_index]
               lowerCAmelCase__					     = self.sigmas_interpol[step_index + 1]
               lowerCAmelCase__					     = self.sigmas[step_index + 1]
        else:
               # 2nd order / KDPM2's method
               lowerCAmelCase__					     = self.sigmas[step_index - 1]
               lowerCAmelCase__					     = self.sigmas_interpol[step_index]
               lowerCAmelCase__					     = self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        lowerCAmelCase__					     = 0
        lowerCAmelCase__					     = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               lowerCAmelCase__					     = sigma_hat if self.state_in_first_order else sigma_interpol
               lowerCAmelCase__					     = sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               lowerCAmelCase__					     = sigma_hat if self.state_in_first_order else sigma_interpol
               lowerCAmelCase__					     = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
        elif self.config.prediction_type == "sample":
               raise NotImplementedError('prediction_type not implemented yet: sample')
        else:
               raise ValueError(
                   F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

        if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               lowerCAmelCase__					     = (sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               lowerCAmelCase__					     = sigma_interpol - sigma_hat

               # store for 2nd order step
               lowerCAmelCase__					     = sample
        else:
               # DPM-Solver-2
               # 2. Convert to an ODE derivative for 2nd order
               lowerCAmelCase__					     = (sample - pred_original_sample) / sigma_interpol

               # 3. delta timestep
               lowerCAmelCase__					     = sigma_next - sigma_hat

               lowerCAmelCase__					     = self.sample
               lowerCAmelCase__					     = None

        lowerCAmelCase__					     = sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=lowercase__)




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







        '''simple docstring'''



        lowerCAmelCase__					     = self.sigmas.to(device=original_samples.device      ,   dtype=original_samples.dtype)
        if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__):
               # mps does not support float64
               lowerCAmelCase__					     = self.timesteps.to(original_samples.device      ,   dtype=torch.floataa)
               lowerCAmelCase__					     = timesteps.to(original_samples.device      ,   dtype=torch.floataa)
        else:
               lowerCAmelCase__					     = self.timesteps.to(original_samples.device)
               lowerCAmelCase__					     = timesteps.to(original_samples.device)

        lowerCAmelCase__					     = [self.index_for_timestep(lowercase__      ,   lowercase__) for t in timesteps]

        lowerCAmelCase__					     = sigmas[step_indices].flatten()
        while len(sigma.shape) < len(original_samples.shape):
               lowerCAmelCase__					     = sigma.unsqueeze(-1)

        lowerCAmelCase__					     = original_samples + noise * sigma
        return noisy_samples




 def __len__(      self :  Union[str, Any]):







        '''simple docstring'''



        return self.config.num_train_timesteps

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)))
