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
  
  
  snake_case				:	Tuple							   =						getLogger(__name__)
  
  
  snake_case				:	Any							   =						4
  snake_case				:	Optional[int]							   =						7
  
  snake_case				:	str							   =						{
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Union[str, Any]     )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if isinstance(_snake_case   ,     torch.Tensor     ):
  		return image
  	elif isinstance(_snake_case   ,     PIL.Image.Image     ):
  		__magic_name__	:	Tuple			     =  [image]
  
  	if isinstance(image[0]   ,     PIL.Image.Image     ):
  		__magic_name__     ,					__magic_name__	:	Tuple			     =  image[0].size
  		__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  (x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
  		__magic_name__	:	str			     =  [np.array(i.resize((w, h)     )     )[None, :] for i in image]
  		__magic_name__	:	str			     =  np.concatenate(_snake_case   ,     axis=0     )
  		__magic_name__	:	Any			     =  np.array(_snake_case     ).astype(np.floataa     ) / 255.0
  		__magic_name__	:	Union[str, Any]			     =  image.transpose(0   ,     3   ,     1   ,     2     )
  		__magic_name__	:	Tuple			     =  2.0 * image - 1.0
  		__magic_name__	:	Any			     =  torch.from_numpy(_snake_case     )
  	elif isinstance(image[0]   ,     torch.Tensor     ):
  		__magic_name__	:	Optional[Any]			     =  torch.cat(_snake_case   ,     dim=0     )
  
  	return image
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = 350		,     ):
  				super().__init__(
  				    vae=_a		,     text_encoder=_a		,     tokenizer=_a		,     unet=_a		,     low_res_scheduler=_a		,     scheduler=_a		,     safety_checker=_a		,     feature_extractor=_a		,     watermarker=_a		,     max_noise_level=_a		,     )
  
  
  
  
  
  
  
  			def __call__(       self		,     _a		,     _a		,     _a = 75		,     _a = 9.0		,     _a = 20		,     _a = None		,     _a = 1		,     _a = 0.0		,     _a = None		,     _a = None		,     _a = None		,     _a = None		,     _a = "pil"		,     _a = True		,     _a = None		,     _a = 1		,     ):
  				self.check_inputs(_a		,     _a		,     _a		,     _a	)
  
  				# 2. Define call parameters
  				if prompt is not None and isinstance(_a		,     _a	):
  					__magic_name__	:	Union[str, Any]			     =  1
  				elif prompt is not None and isinstance(_a		,     _a	):
  					__magic_name__	:	List[Any]			     =  len(_a	)
  				else:
  					__magic_name__	:	str			     =  prompt_embeds.shape[0]
  
  				__magic_name__	:	Optional[Any]			     =  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.
  				__magic_name__	:	str			     =  guidance_scale > 1.0
  
  				# 3. Encode input prompt
  				__magic_name__	:	Tuple			     =  self._encode_prompt(
  				    _a		,     _a		,     _a		,     _a		,     _a		,     prompt_embeds=_a		,     negative_prompt_embeds=_a		,     )
  
  				__magic_name__	:	int			     =  ORT_TO_PT_TYPE[str(text_embeddings.dtype	)]
  
  				# 4. Preprocess image
  				__magic_name__	:	Tuple			     =  preprocess(_a	)
  				__magic_name__	:	int			     =  image.cpu()
  
  				# 5. set timesteps
  				self.scheduler.set_timesteps(_a		,     device=_a	)
  				__magic_name__	:	Union[str, Any]			     =  self.scheduler.timesteps
  
  				# 5. Add noise to image
  				__magic_name__	:	Optional[int]			     =  torch.tensor([noise_level]		,     dtype=torch.long		,     device=_a	)
  				__magic_name__	:	List[Any]			     =  torch.randn(image.shape		,     generator=_a		,     device=_a		,     dtype=_a	)
  				__magic_name__	:	Union[str, Any]			     =  self.low_res_scheduler.add_noise(_a		,     _a		,     _a	)
  
  				__magic_name__	:	Optional[Any]			     =  2 if do_classifier_free_guidance else 1
  				__magic_name__	:	Dict			     =  np.concatenate([image] * batch_multiplier * num_images_per_prompt	)
  				__magic_name__	:	str			     =  np.concatenate([noise_level] * image.shape[0]	)
  
  				# 6. Prepare latent variables
  				__magic_name__     ,					__magic_name__	:	List[Any]			     =  image.shape[2:]
  				__magic_name__	:	str			     =  self.prepare_latents(
  				    batch_size * num_images_per_prompt		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     )
  
  				# 7. Check that sizes of image and latents match
  				__magic_name__	:	Union[str, 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
  				__magic_name__	:	List[str]			     =  self.prepare_extra_step_kwargs(_a		,     _a	)
  
  				__magic_name__	:	Optional[int]			     =  next(
  				    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")		,     "tensor(float)"	)
  				__magic_name__	:	Tuple			     =  ORT_TO_NP_TYPE[timestep_dtype]
  
  				# 9. Denoising loop
  				__magic_name__	:	int			     =  len(_a	) - num_inference_steps * self.scheduler.order
  				with self.progress_bar(total=_a	) as progress_bar:
  					for i, t in enumerate(_a	):
  						# expand the latents if we are doing classifier free guidance
  						__magic_name__	:	Tuple			     =  np.concatenate([latents] * 2	) if do_classifier_free_guidance else latents
  
  						# concat latents, mask, masked_image_latents in the channel dimension
  						__magic_name__	:	Optional[int]			     =  self.scheduler.scale_model_input(_a		,     _a	)
  						__magic_name__	:	Dict			     =  np.concatenate([latent_model_input, image]		,     axis=1	)
  
  						# timestep to tensor
  						__magic_name__	:	Union[str, Any]			     =  np.array([t]		,     dtype=_a	)
  
  						# predict the noise residual
  						__magic_name__	:	List[str]			     =  self.unet(
  						    sample=_a		,     timestep=_a		,     encoder_hidden_states=_a		,     class_labels=noise_level.astype(np.intaa	)		,     )[0]
  
  						# perform guidance
  						if do_classifier_free_guidance:
  							__magic_name__     ,					__magic_name__	:	Any			     =  np.split(_a		,     2	)
  						__magic_name__	:	Optional[Any]			     =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
  						# compute the previous noisy sample x_t -> x_t-1
  						__magic_name__	:	int			     =  self.scheduler.step(
  						    torch.from_numpy(_a	)		,     _a		,     _a		,     **_a	).prev_sample
  
  						# call the callback, if provided
  						if i == len(_a	) - 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(_a		,     _a		,     _a	)
  
          # 10. Post-processing
  				__magic_name__	:	Tuple			     =  self.decode_latents(latents.float()	)
  
  				# 11. Convert to PIL
  				if output_type == "pil":
  					__magic_name__	:	List[str]			     =  self.numpy_to_pil(_a	)
  
  				if not return_dict:
  					return (image,)
  
  				return ImagePipelineOutput(images=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Union[str, Any]			     =  1 / 0.0_83_33 * latents
  				__magic_name__	:	Any			     =  self.vae(latent_sample=_a	)[0]
  				__magic_name__	:	List[str]			     =  np.clip(image / 2 + 0.5		,     0		,     1	)
  				__magic_name__	:	Any			     =  image.transpose((0, 2, 3, 1)	)
  				return image
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     _a = None		,     ):
  				if prompt is not None and isinstance(_a		,     _a	):
  					__magic_name__	:	List[Any]			     =  1
  				elif prompt is not None and isinstance(_a		,     _a	):
  					__magic_name__	:	str			     =  len(_a	)
  				else:
  					__magic_name__	:	List[Any]			     =  prompt_embeds.shape[0]
  
  				if prompt_embeds is None:
  					__magic_name__	:	int			     =  self.tokenizer(
  					    _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     truncation=_a		,     return_tensors="pt"		,     )
  					__magic_name__	:	Any			     =  text_inputs.input_ids
  					__magic_name__	:	Optional[int]			     =  self.tokenizer(_a		,     padding="longest"		,     return_tensors="pt"	).input_ids
  
  					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
  					    _a		,     _a	):
  						__magic_name__	:	List[Any]			     =  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
  					__magic_name__	:	Tuple			     =  self.text_encoder(
  					    input_ids=text_input_ids.int().to(_a	)		,     )
  					__magic_name__	:	int			     =  prompt_embeds[0]
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  prompt_embeds.shape
  				# duplicate text embeddings for each generation per prompt, using mps friendly method
  				__magic_name__	:	List[Any]			     =  prompt_embeds.repeat(1		,     _a	)
  				__magic_name__	:	Any			     =  prompt_embeds.reshape(bs_embed * num_images_per_prompt		,     _a		,     -1	)
  
  				# get unconditional embeddings for classifier free guidance
  				if do_classifier_free_guidance and negative_prompt_embeds is None:
  					__magic_name__	:	List[str]
  					if negative_prompt is None:
  						__magic_name__	:	int			     =  [""] * batch_size
  					elif type(_a	) is not type(_a	):
  						raise TypeError(
  						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_a	)} !='''
  						    f''' {type(_a	)}.'''	)
  					elif isinstance(_a		,     _a	):
  						__magic_name__	:	Optional[int]			     =  [negative_prompt]
  					elif batch_size != len(_a	):
  						raise ValueError(
  						    f'''`negative_prompt`: {negative_prompt} has batch size {len(_a	)}, but `prompt`:'''
  						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
  						    " the batch size of `prompt`."	)
  					else:
  						__magic_name__	:	Union[str, Any]			     =  negative_prompt
  
  					__magic_name__	:	str			     =  text_input_ids.shape[-1]
  					__magic_name__	:	Union[str, Any]			     =  self.tokenizer(
  					    _a		,     padding="max_length"		,     max_length=_a		,     truncation=_a		,     return_tensors="pt"		,     )
  
  					# if hasattr(uncond_input, "attention_mask"):
  					#     attention_mask = uncond_input.attention_mask.to(device)
  					# else:
  					#     attention_mask = None
  
  					__magic_name__	:	Optional[int]			     =  self.text_encoder(
  					    input_ids=uncond_input.input_ids.int().to(_a	)		,     )
  					__magic_name__	:	int			     =  uncond_embeddings[0]
  
  				if do_classifier_free_guidance:
  					__magic_name__	:	Optional[Any]			     =  uncond_embeddings.shape[1]
  					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  					__magic_name__	:	Optional[Any]			     =  uncond_embeddings.repeat(1		,     _a	)
  					__magic_name__	:	Optional[Any]			     =  uncond_embeddings.reshape(batch_size * num_images_per_prompt		,     _a		,     -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
  					__magic_name__	:	str			     =  np.concatenate([uncond_embeddings, prompt_embeds]	)
  
  				return prompt_embeds
  
style_context: 
  from sklearn.metrics import roc_auc_score
  
  import datasets
  
  
  snake_case				:	List[Any]							   =						"\nThis metric computes the area under the curve (AUC) for the Receiver Operating Characteristic Curve (ROC). The return values represent how well the model used is predicting the correct classes, based on the input data. A score of `0.5` means that the model is predicting exactly at chance, i.e. the model's predictions are correct at the same rate as if the predictions were being decided by the flip of a fair coin or the roll of a fair die. A score above `0.5` indicates that the model is doing better than chance, while a score below `0.5` indicates that the model is doing worse than chance.\n\nThis metric has three separate use cases:\n    - binary: The case in which there are only two different label classes, and each example gets only one label. This is the default implementation.\n    - multiclass: The case in which there can be more than two different label classes, but each example still gets only one label.\n    - multilabel: The case in which there can be more than two different label classes, and each example can have more than one label.\n"
  
  snake_case				:	Any							   =						"\nArgs:\n- references (array-like of shape (n_samples,) or (n_samples, n_classes)): Ground truth labels. Expects different input based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples,)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- prediction_scores (array-like of shape (n_samples,) or (n_samples, n_classes)): Model predictions. Expects different inputs based on use case:\n    - binary: expects an array-like of shape (n_samples,)\n    - multiclass: expects an array-like of shape (n_samples, n_classes)\n    - multilabel: expects an array-like of shape (n_samples, n_classes)\n- average (`str`): Type of average, and is ignored in the binary use case. Defaults to 'macro'. Options are:\n    - `'micro'`: Calculates metrics globally by considering each element of the label indicator matrix as a label. Only works with the multilabel use case.\n    - `'macro'`: Calculate metrics for each label, and find their unweighted mean.  This does not take label imbalance into account.\n    - `'weighted'`: Calculate metrics for each label, and find their average, weighted by support (i.e. the number of true instances for each label).\n    - `'samples'`: Calculate metrics for each instance, and find their average. Only works with the multilabel use case.\n    - `None`:  No average is calculated, and scores for each class are returned. Only works with the multilabels use case.\n- sample_weight (array-like of shape (n_samples,)): Sample weights. Defaults to None.\n- max_fpr (`float`): If not None, the standardized partial AUC over the range [0, `max_fpr`] is returned. Must be greater than `0` and less than or equal to `1`. Defaults to `None`. Note: For the multiclass use case, `max_fpr` should be either `None` or `1.0` as ROC AUC partial computation is not currently supported for `multiclass`.\n- multi_class (`str`): Only used for multiclass targets, where it is required. Determines the type of configuration to use. Options are:\n    - `'ovr'`: Stands for One-vs-rest. Computes the AUC of each class against the rest. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when `average == 'macro'`, because class imbalance affects the composition of each of the 'rest' groupings.\n    - `'ovo'`: Stands for One-vs-one. Computes the average AUC of all possible pairwise combinations of classes. Insensitive to class imbalance when `average == 'macro'`.\n- labels (array-like of shape (n_classes,)): Only used for multiclass targets. List of labels that index the classes in\n    `prediction_scores`. If `None`, the numerical or lexicographical order of the labels in\n    `prediction_scores` is used. Defaults to `None`.\nReturns:\n    roc_auc (`float` or array-like of shape (n_classes,)): Returns array if in multilabel use case and `average='None'`. Otherwise, returns `float`.\nExamples:\n    Example 1:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\")\n        >>> refs = [1, 0, 1, 1, 0, 0]\n        >>> pred_scores = [0.5, 0.2, 0.99, 0.3, 0.1, 0.7]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores)\n        >>> print(round(results['roc_auc'], 2))\n        0.78\n\n    Example 2:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multiclass\")\n        >>> refs = [1, 0, 1, 2, 2, 0]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, multi_class='ovr')\n        >>> print(round(results['roc_auc'], 2))\n        0.85\n\n    Example 3:\n        >>> roc_auc_score = datasets.load_metric(\"roc_auc\", \"multilabel\")\n        >>> refs = [[1, 1, 0],\n        ...         [1, 1, 0],\n        ...         [0, 1, 0],\n        ...         [0, 0, 1],\n        ...         [0, 1, 1],\n        ...         [1, 0, 1]]\n        >>> pred_scores = [[0.3, 0.5, 0.2],\n        ...                 [0.7, 0.2, 0.1],\n        ...                 [0.005, 0.99, 0.005],\n        ...                 [0.2, 0.3, 0.5],\n        ...                 [0.1, 0.1, 0.8],\n        ...                 [0.1, 0.7, 0.2]]\n        >>> results = roc_auc_score.compute(references=refs, prediction_scores=pred_scores, average=None)\n        >>> print([round(res, 2) for res in results['roc_auc']])\n        [0.83, 0.38, 0.94]\n"
  
  snake_case				:	List[Any]							   =						"\\n@article{doi:10.1177/0272989X8900900307,\nauthor = {Donna Katzman McClish},\ntitle ={Analyzing a Portion of the ROC Curve},\njournal = {Medical Decision Making},\nvolume = {9},\nnumber = {3},\npages = {190-195},\nyear = {1989},\ndoi = {10.1177/0272989X8900900307},\n    note ={PMID: 2668680},\nURL = {https://doi.org/10.1177/0272989X8900900307},\neprint = {https://doi.org/10.1177/0272989X8900900307}\n}\n\n\n@article{10.1023/A:1010920819831,\nauthor = {Hand, David J. and Till, Robert J.},\ntitle = {A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems},\nyear = {2001},\nissue_date = {November 2001},\npublisher = {Kluwer Academic Publishers},\naddress = {USA},\nvolume = {45},\nnumber = {2},\nissn = {0885-6125},\nurl = {https://doi.org/10.1023/A:1010920819831},\ndoi = {10.1023/A:1010920819831},\njournal = {Mach. Learn.},\nmonth = {oct},\npages = {171–186},\nnumpages = {16},\nkeywords = {Gini index, AUC, error rate, ROC curve, receiver operating characteristic}\n}\n\n\n@article{scikit-learn,\ntitle={Scikit-learn: Machine Learning in {P}ython},\nauthor={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\njournal={Journal of Machine Learning Research},\nvolume={12},\npages={2825--2830},\nyear={2011}\n}\n"
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,		_KWARGS_DESCRIPTION       )
  class    _snake_case      (  datasets.Metric       ):
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return datasets.MetricInfo(
  				    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
  				        {
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				            "references": datasets.Value("int32"	),
  				        }
  				        if self.config_name == "multiclass"
  				        else {
  				            "references": datasets.Sequence(datasets.Value("int32"	)	),
  				            "prediction_scores": datasets.Sequence(datasets.Value("float"	)	),
  				        }
  				        if self.config_name == "multilabel"
  				        else {
  				            "references": datasets.Value("int32"	),
  				            "prediction_scores": datasets.Value("float"	),
  				        }	)		,     reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html"]		,     )
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a="macro"		,     _a=None		,     _a=None		,     _a="raise"		,     _a=None		,     ):
  				return {
  				    "roc_auc": roc_auc_score(
  				        _a		,     _a		,     average=_a		,     sample_weight=_a		,     max_fpr=_a		,     multi_class=_a		,     labels=_a		,     )
  				}
  