code: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import math
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  __A :		str      							=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  torch.FloatTensor
     lowercase       :  Optional[torch.FloatTensor]      =				None
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       List[str]=0.999						,snake_case_     :       str="cosine"						,):
  
  
  
  
  
  
  
      '''simple docstring'''
      if alpha_transform_type == "cosine":
  
          def alpha_bar_fn(snake_case_     :       int    ):
              return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2
  
      elif alpha_transform_type == "exp":
  
          def alpha_bar_fn(snake_case_     :       int    ):
              return math.exp(t * -12.0    )
  
      else:
          raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'    )
  
      UpperCamelCase				:   Optional[int]          =							[]
      for i in range(snake_case_    ):
          UpperCamelCase				:   List[str]          =							i / num_diffusion_timesteps
          UpperCamelCase				:   Optional[Any]          =							(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 							lowerCamelCase		( _UpperCAmelCase	,  _UpperCAmelCase				):
     lowercase       :  Union[str, Any]      =				[e.name for e in KarrasDiffusionSchedulers]
     lowercase       :  Optional[Any]      =				1
  
  
  
  
  
  
  
     @register_to_config
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ = 1000    ,      SCREAMING_SNAKE_CASE_ = 0.0001    ,      SCREAMING_SNAKE_CASE_ = 0.02    ,      SCREAMING_SNAKE_CASE_ = "linear"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "epsilon"    ,      SCREAMING_SNAKE_CASE_ = "linear"    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = "linspace"    ,      SCREAMING_SNAKE_CASE_ = 0    ,      ):
         if trained_betas is not None:
             UpperCamelCase				:   Tuple          =							torch.tensor(SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         elif beta_schedule == "linear":
             UpperCamelCase				:   Optional[Any]          =							torch.linspace(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             UpperCamelCase				:   Optional[Any]          =							(
                 torch.linspace(beta_start**0.5    ,      beta_end**0.5    ,      SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa ) ** 2
             )
         elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase				:   Tuple          =							betas_for_alpha_bar(SCREAMING_SNAKE_CASE_ )
         else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
  
         UpperCamelCase				:   Dict          =							1.0 - self.betas
         UpperCamelCase				:   Dict          =							torch.cumprod(self.alphas    ,      dim=0 )
  
         UpperCamelCase				:   int          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCamelCase				:   List[str]          =							np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
         UpperCamelCase				:   str          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ )
  
         # setable values
         UpperCamelCase				:   Tuple          =							None
         UpperCamelCase				:   Any          =							np.linspace(0    ,      num_train_timesteps - 1    ,      SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )[::-1].copy()
         UpperCamelCase				:   Tuple          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							False
         UpperCamelCase				:   Union[str, Any]          =							use_karras_sigmas
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()
  
         return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         if isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   Dict          =							timestep.to(self.timesteps.device )
         UpperCamelCase				:   int          =							(self.timesteps == timestep).nonzero().item()
         UpperCamelCase				:   List[Any]          =							self.sigmas[step_index]
  
         UpperCamelCase				:   Optional[Any]          =							sample / ((sigma**2 + 1) ** 0.5)
  
         UpperCamelCase				:   List[str]          =							True
         return sample
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   List[Any]          =							num_inference_steps
  
         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
             UpperCamelCase				:   Optional[int]          =							np.linspace(0    ,      self.config.num_train_timesteps - 1    ,      SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )[
                 ::-1
             ].copy()
         elif self.config.timestep_spacing == "leading":
             UpperCamelCase				:   Any          =							self.config.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
             UpperCamelCase				:   List[str]          =							(np.arange(0    ,      SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_ )
             timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
             UpperCamelCase				:   Optional[Any]          =							self.config.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
             UpperCamelCase				:   Optional[int]          =							(np.arange(self.config.num_train_timesteps    ,      0    ,      -step_ratio )).round().copy().astype(SCREAMING_SNAKE_CASE_ )
             timesteps -= 1
         else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
  
         UpperCamelCase				:   Dict          =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         UpperCamelCase				:   Optional[int]          =							np.log(SCREAMING_SNAKE_CASE_ )
  
         if self.config.interpolation_type == "linear":
             UpperCamelCase				:   int          =							np.interp(SCREAMING_SNAKE_CASE_    ,      np.arange(0    ,      len(SCREAMING_SNAKE_CASE_ ) )    ,      SCREAMING_SNAKE_CASE_ )
         elif self.config.interpolation_type == "log_linear":
             UpperCamelCase				:   Optional[Any]          =							torch.linspace(np.log(sigmas[-1] )    ,      np.log(sigmas[0] )    ,      num_inference_steps + 1 ).exp()
         else:
             raise ValueError(
                 f'{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'
                 """ 'linear' or 'log_linear'""" )
  
         if self.use_karras_sigmas:
             UpperCamelCase				:   str          =							self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_    ,      num_inference_steps=self.num_inference_steps )
             UpperCamelCase				:   Tuple          =							np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) for sigma in sigmas] )
  
         UpperCamelCase				:   Any          =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
         UpperCamelCase				:   Optional[int]          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )
         if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ):
             # mps does not support float64
             UpperCamelCase				:   List[str]          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_    ,      dtype=torch.floataa )
         else:
             UpperCamelCase				:   int          =							torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         # get log sigma
         UpperCamelCase				:   int          =							np.log(SCREAMING_SNAKE_CASE_ )
  
         # get distribution
         UpperCamelCase				:   int          =							log_sigma - log_sigmas[:, np.newaxis]
  
         # get sigmas range
         UpperCamelCase				:   List[str]          =							np.cumsum((dists >= 0)    ,      axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
         UpperCamelCase				:   int          =							low_idx + 1
  
         UpperCamelCase				:   List[str]          =							log_sigmas[low_idx]
         UpperCamelCase				:   str          =							log_sigmas[high_idx]
  
         # interpolate sigmas
         UpperCamelCase				:   Optional[Any]          =							(low - log_sigma) / (low - high)
         UpperCamelCase				:   Dict          =							np.clip(SCREAMING_SNAKE_CASE_    ,      0    ,      1 )
  
         # transform interpolation to time range
         UpperCamelCase				:   Any          =							(1 - w) * low_idx + w * high_idx
         UpperCamelCase				:   Any          =							t.reshape(sigma.shape )
         return t
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   float          =							in_sigmas[-1].item()
         UpperCamelCase				:   float          =							in_sigmas[0].item()
  
         UpperCamelCase				:   Tuple          =							7.0  # 7.0 is the value used in the paper
         UpperCamelCase				:   List[str]          =							np.linspace(0    ,      1    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							sigma_min ** (1 / rho)
         UpperCamelCase				:   List[str]          =							sigma_max ** (1 / rho)
         UpperCamelCase				:   Union[str, Any]          =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = float("""inf""" )    ,      SCREAMING_SNAKE_CASE_ = 1.0    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      ):
  
         if (
             isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
             or isinstance(SCREAMING_SNAKE_CASE_    ,      torch.IntTensor )
             or isinstance(SCREAMING_SNAKE_CASE_    ,      torch.LongTensor )
         ):
             raise ValueError(
                 (
                     """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                     """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                     """ one of the `scheduler.timesteps` as a timestep."""
                 )    ,      )
  
         if not self.is_scale_input_called:
             logger.warning(
                 """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                 """See `StableDiffusionPipeline` for a usage example.""" )
  
         if isinstance(SCREAMING_SNAKE_CASE_    ,      torch.Tensor ):
             UpperCamelCase				:   Any          =							timestep.to(self.timesteps.device )
  
         UpperCamelCase				:   str          =							(self.timesteps == timestep).nonzero().item()
         UpperCamelCase				:   Any          =							self.sigmas[step_index]
  
         UpperCamelCase				:   Union[str, Any]          =							min(s_churn / (len(self.sigmas ) - 1)    ,      2**0.5 - 1 ) if s_tmin <= sigma <= s_tmax else 0.0
  
         UpperCamelCase				:   List[str]          =							randn_tensor(
             model_output.shape    ,      dtype=model_output.dtype    ,      device=model_output.device    ,      generator=SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[Any]          =							noise * s_noise
         UpperCamelCase				:   str          =							sigma * (gamma + 1)
  
         if gamma > 0:
             UpperCamelCase				:   List[str]          =							sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
  
         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         # NOTE: "original_sample" should not be an expected prediction_type but is left in for
         # backwards compatibility
         if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
             UpperCamelCase				:   Tuple          =							model_output
         elif self.config.prediction_type == "epsilon":
             UpperCamelCase				:   int          =							sample - sigma_hat * model_output
         elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             UpperCamelCase				:   Optional[int]          =							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
         else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
  
         # 2. Convert to an ODE derivative
         UpperCamelCase				:   Any          =							(sample - pred_original_sample) / sigma_hat
  
         UpperCamelCase				:   int          =							self.sigmas[step_index + 1] - sigma_hat
  
         UpperCamelCase				:   Any          =							sample + derivative * dt
  
         if not return_dict:
             return (prev_sample,)
  
         return EulerDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    ,      pred_original_sample=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         UpperCamelCase				:   Optional[Any]          =							self.sigmas.to(device=original_samples.device    ,      dtype=original_samples.dtype )
         if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_ ):
             # mps does not support float64
             UpperCamelCase				:   List[Any]          =							self.timesteps.to(original_samples.device    ,      dtype=torch.floataa )
             UpperCamelCase				:   Dict          =							timesteps.to(original_samples.device    ,      dtype=torch.floataa )
         else:
             UpperCamelCase				:   Optional[int]          =							self.timesteps.to(original_samples.device )
             UpperCamelCase				:   int          =							timesteps.to(original_samples.device )
  
         UpperCamelCase				:   Tuple          =							[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
         UpperCamelCase				:   Union[str, Any]          =							sigmas[step_indices].flatten()
         while len(sigma.shape ) < len(original_samples.shape ):
             UpperCamelCase				:   int          =							sigma.unsqueeze(-1 )
  
         UpperCamelCase				:   Tuple          =							original_samples + noise * sigma
         return noisy_samples
  
  
  
  
  
  
  
     def __len__(							self ):
         return self.config.num_train_timesteps
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  __A :		Optional[Any]      							=    getLogger(__name__)
  
  
  __A :		Any      							=    4
  __A :		str      							=    7
  
  __A :		Any      							=    {
      '''float16''': torch.floataa,
      '''float32''': torch.floataa,
  }
  def 					A_			(		snake_case_     :       Optional[int]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      if isinstance(snake_case_						,torch.Tensor    ):
          return image
      elif isinstance(snake_case_						,PIL.Image.Image    ):
          UpperCamelCase				:   List[Any]          =							[image]
  
      if isinstance(image[0]						,PIL.Image.Image    ):
          UpperCamelCase       ,							UpperCamelCase				:   Any          =							image[0].size
          UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32
  
          UpperCamelCase				:   Tuple          =							[np.array(i.resize((w, h)    )    )[None, :] for i in image]
          UpperCamelCase				:   Union[str, Any]          =							np.concatenate(snake_case_						,axis=0    )
          UpperCamelCase				:   Union[str, Any]          =							np.array(snake_case_    ).astype(np.floataa    ) / 255.0
          UpperCamelCase				:   Dict          =							image.transpose(0						,3						,1						,2    )
          UpperCamelCase				:   Tuple          =							2.0 * image - 1.0
          UpperCamelCase				:   Tuple          =							torch.from_numpy(snake_case_    )
      elif isinstance(image[0]						,torch.Tensor    ):
          UpperCamelCase				:   Union[str, Any]          =							torch.cat(snake_case_						,dim=0    )
  
      return image
  
  
  class 							lowerCamelCase		( _UpperCAmelCase				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 350    ,      ):
         super().__init__(
             vae=SCREAMING_SNAKE_CASE_    ,      text_encoder=SCREAMING_SNAKE_CASE_    ,      tokenizer=SCREAMING_SNAKE_CASE_    ,      unet=SCREAMING_SNAKE_CASE_    ,      low_res_scheduler=SCREAMING_SNAKE_CASE_    ,      scheduler=SCREAMING_SNAKE_CASE_    ,      safety_checker=SCREAMING_SNAKE_CASE_    ,      feature_extractor=SCREAMING_SNAKE_CASE_    ,      watermarker=SCREAMING_SNAKE_CASE_    ,      max_noise_level=SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = 75    ,      SCREAMING_SNAKE_CASE_ = 9.0    ,      SCREAMING_SNAKE_CASE_ = 20    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      SCREAMING_SNAKE_CASE_ = 0.0    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "pil"    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = 1    ,      ):
         self.check_inputs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         # 2. Define call parameters
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Tuple          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         UpperCamelCase				:   Optional[int]          =							self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCamelCase				:   str          =							guidance_scale > 1.0
  
         # 3. Encode input prompt
         UpperCamelCase				:   Dict          =							self._encode_prompt(
             SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      prompt_embeds=SCREAMING_SNAKE_CASE_    ,      negative_prompt_embeds=SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   Optional[int]          =							ORT_TO_PT_TYPE[str(text_embeddings.dtype )]
  
         # 4. Preprocess image
         UpperCamelCase				:   List[Any]          =							preprocess(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							image.cpu()
  
         # 5. set timesteps
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[str]          =							self.scheduler.timesteps
  
         # 5. Add noise to image
         UpperCamelCase				:   Union[str, Any]          =							torch.tensor([noise_level]    ,      dtype=torch.long    ,      device=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							torch.randn(image.shape    ,      generator=SCREAMING_SNAKE_CASE_    ,      device=SCREAMING_SNAKE_CASE_    ,      dtype=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Dict          =							2 if do_classifier_free_guidance else 1
         UpperCamelCase				:   List[Any]          =							np.concatenate([image] * batch_multiplier * num_images_per_prompt )
         UpperCamelCase				:   Any          =							np.concatenate([noise_level] * image.shape[0] )
  
         # 6. Prepare latent variables
         UpperCamelCase       ,							UpperCamelCase				:   Optional[int]          =							image.shape[2:]
         UpperCamelCase				:   Any          =							self.prepare_latents(
             batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      )
  
         # 7. Check that sizes of image and latents match
         UpperCamelCase				:   Any          =							image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                 f' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'
                 f' `num_channels_image`: {num_channels_image} '
                 f' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'
                 """ `pipeline.unet` or your `image` input.""" )
  
         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         UpperCamelCase				:   Union[str, Any]          =							self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Optional[int]          =							next(
             (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")    ,      """tensor(float)""" )
         UpperCamelCase				:   Tuple          =							ORT_TO_NP_TYPE[timestep_dtype]
  
         # 9. Denoising loop
         UpperCamelCase				:   Optional[Any]          =							len(SCREAMING_SNAKE_CASE_ ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=SCREAMING_SNAKE_CASE_ ) as progress_bar:
             for i, t in enumerate(SCREAMING_SNAKE_CASE_ ):
                 # expand the latents if we are doing classifier free guidance
                 UpperCamelCase				:   int          =							np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents
  
                 # concat latents, mask, masked_image_latents in the channel dimension
                 UpperCamelCase				:   Dict          =							self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   List[str]          =							np.concatenate([latent_model_input, image]    ,      axis=1 )
  
                 # timestep to tensor
                 UpperCamelCase				:   Any          =							np.array([t]    ,      dtype=SCREAMING_SNAKE_CASE_ )
  
                 # predict the noise residual
                 UpperCamelCase				:   List[Any]          =							self.unet(
                     sample=SCREAMING_SNAKE_CASE_    ,      timestep=SCREAMING_SNAKE_CASE_    ,      encoder_hidden_states=SCREAMING_SNAKE_CASE_    ,      class_labels=noise_level.astype(np.intaa )    ,      )[0]
  
                 # perform guidance
                 if do_classifier_free_guidance:
                     UpperCamelCase       ,							UpperCamelCase				:   Dict          =							np.split(SCREAMING_SNAKE_CASE_    ,      2 )
                 UpperCamelCase				:   Tuple          =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase				:   Tuple          =							self.scheduler.step(
                     torch.from_numpy(SCREAMING_SNAKE_CASE_ )    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ).prev_sample
  
                 # call the callback, if provided
                 if i == len(SCREAMING_SNAKE_CASE_ ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
          # 10. Post-processing
         UpperCamelCase				:   List[str]          =							self.decode_latents(latents.float() )
  
         # 11. Convert to PIL
         if output_type == "pil":
             UpperCamelCase				:   str          =							self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
  
         if not return_dict:
             return (image,)
  
         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							1 / 0.08333 * latents
         UpperCamelCase				:   Union[str, Any]          =							self.vae(latent_sample=SCREAMING_SNAKE_CASE_ )[0]
         UpperCamelCase				:   Union[str, Any]          =							np.clip(image / 2 + 0.5    ,      0    ,      1 )
         UpperCamelCase				:   Any          =							image.transpose((0, 2, 3, 1) )
         return image
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
         if prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   List[Any]          =							1
         elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   Dict          =							len(SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Dict          =							prompt_embeds.shape[0]
  
         if prompt_embeds is None:
             UpperCamelCase				:   List[Any]          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
             UpperCamelCase				:   int          =							text_inputs.input_ids
             UpperCamelCase				:   List[Any]          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      padding="""longest"""    ,      return_tensors="""pt""" ).input_ids
  
             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   Dict          =							self.tokenizer.batch_decode(
                     untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
                 logger.warning(
                     """The following part of your input was truncated because CLIP can only handle sequences up to"""
                     f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
  
             # no positional arguments to text_encoder
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   str          =							prompt_embeds[0]
  
         UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase				:   Tuple          =							prompt_embeds.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							prompt_embeds.reshape(bs_embed * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -1 )
  
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             UpperCamelCase				:   List[str]
             if negative_prompt is None:
                 UpperCamelCase				:   Optional[int]          =							[""""""] * batch_size
             elif type(SCREAMING_SNAKE_CASE_ ) is not type(SCREAMING_SNAKE_CASE_ ):
                 raise TypeError(
                     f'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_ )} !='
                     f' {type(SCREAMING_SNAKE_CASE_ )}.' )
             elif isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
                 UpperCamelCase				:   List[Any]          =							[negative_prompt]
             elif batch_size != len(SCREAMING_SNAKE_CASE_ ):
                 raise ValueError(
                     f'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_ )}, but `prompt`:'
                     f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                     """ the batch size of `prompt`.""" )
             else:
                 UpperCamelCase				:   Optional[Any]          =							negative_prompt
  
             UpperCamelCase				:   Union[str, Any]          =							text_input_ids.shape[-1]
             UpperCamelCase				:   Tuple          =							self.tokenizer(
                 SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=SCREAMING_SNAKE_CASE_    ,      truncation=SCREAMING_SNAKE_CASE_    ,      return_tensors="""pt"""    ,      )
  
             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None
  
             UpperCamelCase				:   List[Any]          =							self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE_ )    ,      )
             UpperCamelCase				:   Tuple          =							uncond_embeddings[0]
  
         if do_classifier_free_guidance:
             UpperCamelCase				:   Dict          =							uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCamelCase				:   Tuple          =							uncond_embeddings.repeat(1    ,      SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[str]          =							uncond_embeddings.reshape(batch_size * num_images_per_prompt    ,      SCREAMING_SNAKE_CASE_    ,      -1 )
  
             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCamelCase				:   Any          =							np.concatenate([uncond_embeddings, prompt_embeds] )
  
         return prompt_embeds
  
  
  
  
  