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
  
  
  A_														=      getLogger(__name__)
  
  
  A_														=      4
  A_														=      7
  
  A_														=      {
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					if isinstance(UpperCAmelCase				,torch.Tensor      ):
  										return image
  					elif isinstance(UpperCAmelCase				,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(UpperCAmelCase				,axis=0      )
  										SCREAMING_SNAKE_CASE_	      =       np.array(UpperCAmelCase      ).astype(np.floataa      ) / 2_5_5.0
  										SCREAMING_SNAKE_CASE_	      =       image.transpose(0				,3				,1				,2      )
  										SCREAMING_SNAKE_CASE_	      =       2.0 * image - 1.0
  										SCREAMING_SNAKE_CASE_	      =       torch.from_numpy(UpperCAmelCase      )
  					elif isinstance(image[0]				,torch.Tensor      ):
  										SCREAMING_SNAKE_CASE_	      =       torch.cat(UpperCAmelCase				,dim=0      )
  
  					return image
  
  
  
  
  
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     OnnxRuntimeModel							,							lowerCAmelCase_     :     DDPMScheduler							,							lowerCAmelCase_     :     Any							,							lowerCAmelCase_     :     int = 350							,							)					->					Optional[int]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(
  								    vae=lowerCAmelCase_							,							text_encoder=lowerCAmelCase_							,							tokenizer=lowerCAmelCase_							,							unet=lowerCAmelCase_							,							low_res_scheduler=lowerCAmelCase_							,							scheduler=lowerCAmelCase_							,							safety_checker=lowerCAmelCase_							,							feature_extractor=lowerCAmelCase_							,							watermarker=lowerCAmelCase_							,							max_noise_level=lowerCAmelCase_							,							)
  
  			def __call__(	self     :     List[str]							,							lowerCAmelCase_     :     Union[str, List[str]]							,							lowerCAmelCase_     :     Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]							,							lowerCAmelCase_     :     int = 75							,							lowerCAmelCase_     :     float = 9.0							,							lowerCAmelCase_     :     int = 20							,							lowerCAmelCase_     :     Optional[Union[str, List[str]]] = None							,							lowerCAmelCase_     :     Optional[int] = 1							,							lowerCAmelCase_     :     float = 0.0							,							lowerCAmelCase_     :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							lowerCAmelCase_     :     Optional[np.ndarray] = None							,							lowerCAmelCase_     :     Optional[np.ndarray] = None							,							lowerCAmelCase_     :     Optional[str] = "pil"							,							lowerCAmelCase_     :     bool = True							,							lowerCAmelCase_     :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,							lowerCAmelCase_     :     Optional[int] = 1							,							)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self.check_inputs(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# 2. Define call parameters
  								if prompt is not None and isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       1
  								elif prompt is not None and isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					)
  								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(
  								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							prompt_embeds=lowerCAmelCase_							,							negative_prompt_embeds=lowerCAmelCase_							,							)
  
  								SCREAMING_SNAKE_CASE_	      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype					)]
  
  								# 4. Preprocess image
  								SCREAMING_SNAKE_CASE_	      =       preprocess(lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       image.cpu()
  
  								# 5. set timesteps
  								self.scheduler.set_timesteps(lowerCAmelCase_							,							device=lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       self.scheduler.timesteps
  
  								# 5. Add noise to image
  								SCREAMING_SNAKE_CASE_	      =       torch.tensor([noise_level]							,							dtype=torch.long							,							device=lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       torch.randn(image.shape							,							generator=lowerCAmelCase_							,							device=lowerCAmelCase_							,							dtype=lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       self.low_res_scheduler.add_noise(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								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							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							)
  
  								# 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(lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								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(lowerCAmelCase_					) - num_inference_steps * self.scheduler.order
  								with self.progress_bar(total=lowerCAmelCase_					) as progress_bar:
  													for i, t in enumerate(lowerCAmelCase_					):
  																		# 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(lowerCAmelCase_							,							lowerCAmelCase_					)
  																		SCREAMING_SNAKE_CASE_	      =       np.concatenate([latent_model_input, image]							,							axis=1					)
  
  																		# timestep to tensor
  																		SCREAMING_SNAKE_CASE_	      =       np.array([t]							,							dtype=lowerCAmelCase_					)
  
  																		# predict the noise residual
  																		SCREAMING_SNAKE_CASE_	      =       self.unet(
  																		    sample=lowerCAmelCase_							,							timestep=lowerCAmelCase_							,							encoder_hidden_states=lowerCAmelCase_							,							class_labels=noise_level.astype(np.intaa					)							,							)[0]
  
  																		# perform guidance
  																		if do_classifier_free_guidance:
  																							SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       np.split(lowerCAmelCase_							,							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(lowerCAmelCase_					)							,							lowerCAmelCase_							,							lowerCAmelCase_							,							**lowerCAmelCase_					).prev_sample
  
  																		# call the callback, if provided
  																		if i == len(lowerCAmelCase_					) - 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(lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  
          # 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(lowerCAmelCase_					)
  
  								if not return_dict:
  													return (image,)
  
  								return ImagePipelineOutput(images=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Optional[int]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       1 / 0.08_333 * latents
  								SCREAMING_SNAKE_CASE_	      =       self.vae(latent_sample=lowerCAmelCase_					)[0]
  								SCREAMING_SNAKE_CASE_	      =       np.clip(image / 2 + 0.5							,							0							,							1					)
  								SCREAMING_SNAKE_CASE_	      =       image.transpose((0, 2, 3, 1)					)
  								return image
  
  
  
  
  			def _lowercase (	self     :     Dict							,							lowerCAmelCase_     :     Union[str, List[str]]							,							lowerCAmelCase_     :     int							,							lowerCAmelCase_     :     Optional[int]							,							lowerCAmelCase_     :     bool							,							lowerCAmelCase_     :     Optional[str]							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if prompt is not None and isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       1
  								elif prompt is not None and isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       prompt_embeds.shape[0]
  
  								if prompt_embeds is None:
  													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(
  													    lowerCAmelCase_							,							padding='''max_length'''							,							max_length=self.tokenizer.model_max_length							,							truncation=lowerCAmelCase_							,							return_tensors='''pt'''							,							)
  													SCREAMING_SNAKE_CASE_	      =       text_inputs.input_ids
  													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(lowerCAmelCase_							,							padding='''longest'''							,							return_tensors='''pt'''					).input_ids
  
  													if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
  													    lowerCAmelCase_							,							lowerCAmelCase_					):
  																		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(lowerCAmelCase_					)							,							)
  													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							,							lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       prompt_embeds.reshape(bs_embed * num_images_per_prompt							,							lowerCAmelCase_							,							-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(lowerCAmelCase_					) is not type(lowerCAmelCase_					):
  																		raise TypeError(
  																		    F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_					)} !='''
  																		    F''' {type(lowerCAmelCase_					)}.'''					)
  													elif isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       [negative_prompt]
  													elif batch_size != len(lowerCAmelCase_					):
  																		raise ValueError(
  																		    F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_					)}, 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(
  													    lowerCAmelCase_							,							padding='''max_length'''							,							max_length=lowerCAmelCase_							,							truncation=lowerCAmelCase_							,							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(lowerCAmelCase_					)							,							)
  													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							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings.reshape(batch_size * num_images_per_prompt							,							lowerCAmelCase_							,							-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: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_														=      {
      "configuration_switch_transformers": [
          "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "SwitchTransformersConfig",
          "SwitchTransformersOnnxConfig",
      ]
  }
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		A_														=      [
  		    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
  		    "SwitchTransformersEncoderModel",
  		    "SwitchTransformersForConditionalGeneration",
  		    "SwitchTransformersModel",
  		    "SwitchTransformersPreTrainedModel",
  		    "SwitchTransformersTop1Router",
  		    "SwitchTransformersSparseMLP",
  		]
  
  
  if TYPE_CHECKING:
  		from .configuration_switch_transformers import (
  		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
  		    SwitchTransformersConfig,
  		    SwitchTransformersOnnxConfig,
  		)
  
  		try:
  				if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  		except OptionalDependencyNotAvailable:
  				pass
  		else:
  				from .modeling_switch_transformers import (
  				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
  				    SwitchTransformersEncoderModel,
  				    SwitchTransformersForConditionalGeneration,
  				    SwitchTransformersModel,
  				    SwitchTransformersPreTrainedModel,
  				    SwitchTransformersSparseMLP,
  				    SwitchTransformersTopaRouter,
  				)
  
  
  else:
  		import sys
  
  		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  