code: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					getLogger(__name__)
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					4
  SCREAMING_SNAKE_CASE__    :    List[str]								=					7
  
  SCREAMING_SNAKE_CASE__    :    Any								=					{
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			if isinstance(SCREAMING_SNAKE_CASE					,						torch.Tensor   ):
  						return image
  			elif isinstance(SCREAMING_SNAKE_CASE					,						PIL.Image.Image   ):
  						SCREAMING_SNAKE_CASE_			=	[image]
  
  			if isinstance(image[0]					,						PIL.Image.Image   ):
  						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image[0].size
  						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
  						SCREAMING_SNAKE_CASE_			=	[np.array(i.resize((w, h)   )   )[None, :] for i in image]
  						SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE					,						axis=0   )
  						SCREAMING_SNAKE_CASE_			=	np.array(SCREAMING_SNAKE_CASE   ).astype(np.floataa   ) / 255.0
  						SCREAMING_SNAKE_CASE_			=	image.transpose(0					,						3					,						1					,						2   )
  						SCREAMING_SNAKE_CASE_			=	2.0 * image - 1.0
  						SCREAMING_SNAKE_CASE_			=	torch.from_numpy(SCREAMING_SNAKE_CASE   )
  			elif isinstance(image[0]					,						torch.Tensor   ):
  						SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE					,						dim=0   )
  
  			return image
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  
  
  
  
  
  	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			,    )     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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			,    )     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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			):
  							SCREAMING_SNAKE_CASE_			=	1
  				elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	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.
  				SCREAMING_SNAKE_CASE_			=	guidance_scale > 1.0
  
  				# 3. Encode input prompt
  				SCREAMING_SNAKE_CASE_			=	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			,    )
  
  				SCREAMING_SNAKE_CASE_			=	ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]
  
  				# 4. Preprocess image
  				SCREAMING_SNAKE_CASE_			=	preprocess(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	image.cpu()
  
  				# 5. set timesteps
  				self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.scheduler.timesteps
  
  				# 5. Add noise to image
  				SCREAMING_SNAKE_CASE_			=	torch.tensor([noise_level]			,    dtype=torch.long			,    device=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	torch.randn(image.shape			,    generator=SCREAMING_SNAKE_CASE			,    device=SCREAMING_SNAKE_CASE			,    dtype=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	self.low_res_scheduler.add_noise(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	2 if do_classifier_free_guidance else 1
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([image] * batch_multiplier * num_images_per_prompt			)
  				SCREAMING_SNAKE_CASE_			=	np.concatenate([noise_level] * image.shape[0]			)
  
  				# 6. Prepare latent variables
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape[2:]
  				SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	self.prepare_extra_step_kwargs(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	next(
  				    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')			,    'tensor(float)'			)
  				SCREAMING_SNAKE_CASE_			=	ORT_TO_NP_TYPE[timestep_dtype]
  
  				# 9. Denoising loop
  				SCREAMING_SNAKE_CASE_			=	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
  										SCREAMING_SNAKE_CASE_			=	np.concatenate([latents] * 2			) if do_classifier_free_guidance else latents
  
  										# concat latents, mask, masked_image_latents in the channel dimension
  										SCREAMING_SNAKE_CASE_			=	self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  										SCREAMING_SNAKE_CASE_			=	np.concatenate([latent_model_input, image]			,    axis=1			)
  
  										# timestep to tensor
  										SCREAMING_SNAKE_CASE_			=	np.array([t]			,    dtype=SCREAMING_SNAKE_CASE			)
  
  										# predict the noise residual
  										SCREAMING_SNAKE_CASE_			=	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:
  													SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	np.split(SCREAMING_SNAKE_CASE			,    2			)
  										SCREAMING_SNAKE_CASE_			=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  										# compute the previous noisy sample x_t -> x_t-1
  										SCREAMING_SNAKE_CASE_			=	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
  				SCREAMING_SNAKE_CASE_			=	self.decode_latents(latents.float()			)
  
  				# 11. Convert to PIL
  				if output_type == "pil":
  							SCREAMING_SNAKE_CASE_			=	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			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	1 / 0.0_8_3_3_3 * latents
  				SCREAMING_SNAKE_CASE_			=	self.vae(latent_sample=SCREAMING_SNAKE_CASE			)[0]
  				SCREAMING_SNAKE_CASE_			=	np.clip(image / 2 + 0.5			,    0			,    1			)
  				SCREAMING_SNAKE_CASE_			=	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			,    )     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	1
  				elif prompt is not None and isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	len(SCREAMING_SNAKE_CASE			)
  				else:
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape[0]
  
  				if prompt_embeds is None:
  							SCREAMING_SNAKE_CASE_			=	self.tokenizer(
  							    SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=self.tokenizer.model_max_length			,    truncation=SCREAMING_SNAKE_CASE			,    return_tensors='pt'			,    )
  							SCREAMING_SNAKE_CASE_			=	text_inputs.input_ids
  							SCREAMING_SNAKE_CASE_			=	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			):
  										SCREAMING_SNAKE_CASE_			=	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
  							SCREAMING_SNAKE_CASE_			=	self.text_encoder(
  							    input_ids=text_input_ids.int().to(SCREAMING_SNAKE_CASE			)			,    )
  							SCREAMING_SNAKE_CASE_			=	prompt_embeds[0]
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	prompt_embeds.shape
  				# duplicate text embeddings for each generation per prompt, using mps friendly method
  				SCREAMING_SNAKE_CASE_			=	prompt_embeds.repeat(1			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	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:
  							SCREAMING_SNAKE_CASE_			=	42
  							if negative_prompt is None:
  										SCREAMING_SNAKE_CASE_			=	[''] * 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			):
  										SCREAMING_SNAKE_CASE_			=	[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:
  										SCREAMING_SNAKE_CASE_			=	negative_prompt
  
  							SCREAMING_SNAKE_CASE_			=	text_input_ids.shape[-1]
  							SCREAMING_SNAKE_CASE_			=	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
  
  							SCREAMING_SNAKE_CASE_			=	self.text_encoder(
  							    input_ids=uncond_input.input_ids.int().to(SCREAMING_SNAKE_CASE			)			,    )
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings[0]
  
  				if do_classifier_free_guidance:
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings.shape[1]
  							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  							SCREAMING_SNAKE_CASE_			=	uncond_embeddings.repeat(1			,    SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	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
  							SCREAMING_SNAKE_CASE_			=	np.concatenate([uncond_embeddings, prompt_embeds]			)
  
  				return prompt_embeds
  
style_context: 
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
      "{processor_class}": "FakeProcessorClass",
      "{model_class}": "FakeModelClass",
      "{object_class}": "FakeObjectClass",
  }
  