


from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE       :      Optional[int]												= "\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n"


SCREAMING_SNAKE_CASE       :      str												= "\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\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 (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {'recall': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {'recall': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {'recall': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric('recall')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'recall': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'recall': array([1., 0., 0.])}\n"


SCREAMING_SNAKE_CASE       :      List[str]												= "\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={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.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION   )
class _lowerCamelCase(						datasets.Metric   ):
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,      citation=_CITATION,      inputs_description=_KWARGS_DESCRIPTION,      features=datasets.Features(
                   {
                       'predictions': datasets.Sequence(datasets.Value('int32')),
                       'references': datasets.Sequence(datasets.Value('int32')),
                   }
                   if self.config_name == 'multilabel'
                   else {
                       'predictions': datasets.Value('int32'),
                       'references': datasets.Value('int32'),
                   }),      reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html'],      )




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=1,      lowerCamelCase="binary",      lowerCamelCase=None,      lowerCamelCase="warn",      )       ->   str:




           """simple docstring"""

           _lowercase       :							Tuple      		=		recall_score(
               lowerCamelCase,      lowerCamelCase,      labels=lowerCamelCase,      pos_label=lowerCamelCase,      average=lowerCamelCase,      sample_weight=lowerCamelCase,      zero_division=lowerCamelCase,      )
           return {"recall": float(lowerCamelCase) if score.size == 1 else score}






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    if isinstance(lowerCamelCase_							, torch.Tensor ):
        return image
    elif isinstance(lowerCamelCase_							, PIL.Image.Image ):
        _lowercase       :							List[Any]      		=		[image]

    if isinstance(image[0]							, PIL.Image.Image ):
        _lowercase       :							Tuple      		=		[np.array(i.resize((w, h)							, resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
        _lowercase       :							str      		=		np.concatenate(lowerCamelCase_							, axis=0 )
        _lowercase       :							Dict      		=		np.array(lowerCamelCase_ ).astype(np.floataa ) / 2_55.0
        _lowercase       :							Optional[int]      		=		image.transpose(0							, 3							, 1							, 2 )
        _lowercase       :							str      		=		2.0 * image - 1.0
        _lowercase       :							Tuple      		=		torch.from_numpy(lowerCamelCase_ )
    elif isinstance(image[0]							, torch.Tensor ):
        _lowercase       :							Any      		=		torch.cat(lowerCamelCase_							, dim=0 )
    return image



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=0.99_95 )    ->						Tuple:
    if not isinstance(lowerCamelCase_							, np.ndarray ):
        _lowercase       :							List[Any]      		=		True
        _lowercase       :							Any      		=		va.device
        _lowercase       :							Union[str, Any]      		=		va.cpu().numpy()
        _lowercase       :							int      		=		va.cpu().numpy()

    _lowercase       :							int      		=		np.sum(va * va / (np.linalg.norm(lowerCamelCase_ ) * np.linalg.norm(lowerCamelCase_ )) )
    if np.abs(lowerCamelCase_ ) > DOT_THRESHOLD:
        _lowercase       :							Any      		=		(1 - t) * va + t * va
    else:
        _lowercase       :							Dict      		=		np.arccos(lowerCamelCase_ )
        _lowercase       :							str      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							int      		=		theta_a * t
        _lowercase       :							Dict      		=		np.sin(lowerCamelCase_ )
        _lowercase       :							Any      		=		np.sin(theta_a - theta_t ) / sin_theta_a
        _lowercase       :							List[Any]      		=		sin_theta_t / sin_theta_a
        _lowercase       :							Dict      		=		sa * va + sa * va

    if inputs_are_torch:
        _lowercase       :							Optional[Any]      		=		torch.from_numpy(lowerCamelCase_ ).to(lowerCamelCase_ )

    return va



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						List[Any]:
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    _lowercase       :							Tuple      		=		F.normalize(lowerCamelCase_							, dim=-1 )
    return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_ )    ->						Optional[int]:
    for param in model.parameters():
        _lowercase       :							Any      		=		value






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None,      lowerCamelCase=None,      lowerCamelCase=None,      )       ->   Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(
               vae=lowerCamelCase,      text_encoder=lowerCamelCase,      clip_model=lowerCamelCase,      tokenizer=lowerCamelCase,      unet=lowerCamelCase,      scheduler=lowerCamelCase,      feature_extractor=lowerCamelCase,      coca_model=lowerCamelCase,      coca_tokenizer=lowerCamelCase,      coca_transform=lowerCamelCase,      )
           _lowercase       :							Tuple      		=		(
               feature_extractor.size
               if isinstance(feature_extractor.size,      lowerCamelCase)
               else feature_extractor.size['shortest_edge']
           )
           _lowercase       :							Union[str, Any]      		=		transforms.Normalize(mean=feature_extractor.image_mean,      std=feature_extractor.image_std)
           set_requires_grad(self.text_encoder,      lowerCamelCase)
           set_requires_grad(self.clip_model,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase = "auto")       ->   Any:




           """simple docstring"""

           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               _lowercase       :							Optional[Any]      		=		self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           self.enable_attention_slicing(lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           set_requires_grad(self.vae,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           set_requires_grad(self.unet,      lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		min(int(num_inference_steps * strength),      lowerCamelCase)

           _lowercase       :							List[Any]      		=		max(num_inference_steps - init_timestep,      0)
           _lowercase       :							int      		=		self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase=None)       ->   Optional[Any]:




           """simple docstring"""

           if not isinstance(lowerCamelCase,      torch.Tensor):
               raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase)}''')

           _lowercase       :							Any      		=		image.to(device=lowerCamelCase,      dtype=lowerCamelCase)

           if isinstance(lowerCamelCase,      lowerCamelCase):
               _lowercase       :							Dict      		=		[
                   self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(lowerCamelCase)
               ]
               _lowercase       :							int      		=		torch.cat(lowerCamelCase,      dim=0)
           else:
               _lowercase       :							int      		=		self.vae.encode(lowerCamelCase).latent_dist.sample(lowerCamelCase)

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							str      		=		0.1_8_2_1_5 * init_latents
           _lowercase       :							List[str]      		=		init_latents.repeat_interleave(lowerCamelCase,      dim=0)

           _lowercase       :							List[str]      		=		randn_tensor(init_latents.shape,      generator=lowerCamelCase,      device=lowerCamelCase,      dtype=lowerCamelCase)

           # get latents
           _lowercase       :							Any      		=		self.scheduler.add_noise(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							str      		=		init_latents

           return latents
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							str      		=		self.coca_transform(lowerCamelCase).unsqueeze(0)
           with torch.no_grad(), torch.cuda.amp.autocast():
               _lowercase       :							List[str]      		=		self.coca_model.generate(transformed_image.to(device=self.device,      dtype=self.coca_model.dtype))
           _lowercase       :							int      		=		self.coca_tokenizer.decode(generated[0].cpu().numpy())
           return generated.split('<end_of_text>')[0].replace('<start_of_text>',      '').rstrip(' .,')
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Tuple      		=		self.feature_extractor.preprocess(lowerCamelCase)
           _lowercase       :							List[str]      		=		torch.from_numpy(clip_image_input['pixel_values'][0]).unsqueeze(0).to(self.device).half()
           _lowercase       :							int      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							Dict      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)
           _lowercase       :							int      		=		image_embeddings_clip.repeat_interleave(lowerCamelCase,      dim=0)
           return image_embeddings_clip
       @torch.enable_grad()
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[str]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		latents.detach().requires_grad_()

           _lowercase       :							Union[str, Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

           # predict the noise residual
           _lowercase       :							Tuple      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

           if isinstance(self.scheduler,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
               _lowercase       :							Any      		=		self.scheduler.alphas_cumprod[timestep]
               _lowercase       :							Any      		=		1 - alpha_prod_t
               # compute predicted original sample from predicted noise also called
               # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
               _lowercase       :							List[Any]      		=		(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

               _lowercase       :							List[str]      		=		torch.sqrt(lowerCamelCase)
               _lowercase       :							Dict      		=		pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Dict      		=		self.scheduler.sigmas[index]
               _lowercase       :							List[Any]      		=		latents - sigma * noise_pred
           else:
               raise ValueError(F'''scheduler type {type(self.scheduler)} not supported''')

           # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Dict      		=		1 / 0.1_8_2_1_5 * sample
           _lowercase       :							Optional[Any]      		=		self.vae.decode(lowerCamelCase).sample
           _lowercase       :							int      		=		(image / 2 + 0.5).clamp(0,      1)

           _lowercase       :							Any      		=		transforms.Resize(self.feature_extractor_size)(lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.normalize(lowerCamelCase).to(latents.dtype)

           _lowercase       :							List[str]      		=		self.clip_model.get_image_features(lowerCamelCase)
           _lowercase       :							List[Any]      		=		image_embeddings_clip / image_embeddings_clip.norm(p=2,      dim=-1,      keepdim=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		spherical_dist_loss(lowerCamelCase,      lowerCamelCase).mean() * clip_guidance_scale

           _lowercase       :							str      		=		-torch.autograd.grad(lowerCamelCase,      lowerCamelCase)[0]

           if isinstance(self.scheduler,      lowerCamelCase):
               _lowercase       :							Union[str, Any]      		=		latents.detach() + grads * (sigma**2)
               _lowercase       :							List[str]      		=		noise_pred_original
           else:
               _lowercase       :							List[Any]      		=		noise_pred_original - torch.sqrt(lowerCamelCase) * grads
           return noise_pred, latents




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = 5_12,      lowerCamelCase = 5_12,      lowerCamelCase = 0.6,      lowerCamelCase = 50,      lowerCamelCase = 7.5,      lowerCamelCase = 1,      lowerCamelCase = 0.0,      lowerCamelCase = 1_00,      lowerCamelCase = None,      lowerCamelCase = "pil",      lowerCamelCase = True,      lowerCamelCase = 0.8,      lowerCamelCase = 0.1,      lowerCamelCase = 0.1,      )       ->   int:




           """simple docstring"""

           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase)} generators.''')

           if height % 8 != 0 or width % 8 != 0:
               raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

           if isinstance(lowerCamelCase,      torch.Generator) and batch_size > 1:
               _lowercase       :							Dict      		=		[generator] + [None] * (batch_size - 1)

           _lowercase       :							Optional[int]      		=		[
               ('model', self.coca_model is None),
               ('tokenizer', self.coca_tokenizer is None),
               ('transform', self.coca_transform is None),
           ]
           _lowercase       :							Optional[int]      		=		[x[0] for x in coca_is_none if x[1]]
           _lowercase       :							str      		=		', '.join(lowerCamelCase)
           # generate prompts with coca model if prompt is None
           if content_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							List[Any]      		=		self.get_image_description(lowerCamelCase)
           if style_prompt is None:
               if len(lowerCamelCase):
                   raise ValueError(
                       F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                       F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''')
               _lowercase       :							Dict      		=		self.get_image_description(lowerCamelCase)

           # get prompt text embeddings for content and style
           _lowercase       :							Optional[int]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							Optional[int]      		=		self.text_encoder(content_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Union[str, Any]      		=		self.tokenizer(
               lowerCamelCase,      padding='max_length',      max_length=self.tokenizer.model_max_length,      truncation=lowerCamelCase,      return_tensors='pt',      )
           _lowercase       :							List[Any]      		=		self.text_encoder(style_text_input.input_ids.to(self.device))[0]

           _lowercase       :							Any      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # duplicate text embeddings for each generation per prompt
           _lowercase       :							Dict      		=		text_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

           # set timesteps
           _lowercase       :							Dict      		=		'offset' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_offset:
               _lowercase       :							Any      		=		1

           self.scheduler.set_timesteps(lowerCamelCase,      **lowerCamelCase)
           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           self.scheduler.timesteps.to(self.device)

           _lowercase		,				_lowercase       :							List[Any]      		=		self.get_timesteps(lowerCamelCase,      lowerCamelCase,      self.device)
           _lowercase       :							str      		=		timesteps[:1].repeat(lowerCamelCase)

           # Preprocess image
           _lowercase       :							str      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							int      		=		preprocess(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							List[str]      		=		self.prepare_latents(
               lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      text_embeddings.dtype,      self.device,      lowerCamelCase)

           _lowercase       :							Optional[int]      		=		slerp(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           if clip_guidance_scale > 0:
               _lowercase       :							Optional[int]      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Dict      		=		self.get_clip_image_embeddings(lowerCamelCase,      lowerCamelCase)
               _lowercase       :							Optional[int]      		=		slerp(
                   lowerCamelCase,      lowerCamelCase,      lowerCamelCase)

           # 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.
           _lowercase       :							Dict      		=		guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
               _lowercase       :							Tuple      		=		content_text_input.input_ids.shape[-1]
               _lowercase       :							Union[str, Any]      		=		self.tokenizer([''],      padding='max_length',      max_length=lowerCamelCase,      return_tensors='pt')
               _lowercase       :							int      		=		self.text_encoder(uncond_input.input_ids.to(self.device))[0]
               # duplicate unconditional embeddings for each generation per prompt
               _lowercase       :							Union[str, Any]      		=		uncond_embeddings.repeat_interleave(lowerCamelCase,      dim=0)

               # 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
               _lowercase       :							Optional[Any]      		=		torch.cat([uncond_embeddings, text_embeddings])

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           _lowercase       :							Tuple      		=		(batch_size, self.unet.config.in_channels, height // 8, width // 8)
           _lowercase       :							Optional[int]      		=		text_embeddings.dtype
           if latents is None:
               if self.device.type == "mps":
                   # randn does not work reproducibly on mps
                   _lowercase       :							List[Any]      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device='cpu',      dtype=lowerCamelCase).to(
                       self.device)
               else:
                   _lowercase       :							Any      		=		torch.randn(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=lowerCamelCase)
           else:
               if latents.shape != latents_shape:
                   raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
               _lowercase       :							Tuple      		=		latents.to(self.device)

           # scale the initial noise by the standard deviation required by the scheduler
           _lowercase       :							List[Any]      		=		latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           _lowercase       :							Dict      		=		'eta' in set(inspect.signature(self.scheduler.step).parameters.keys())
           _lowercase       :							Optional[Any]      		=		{}
           if accepts_eta:
               _lowercase       :							List[Any]      		=		eta

           # check if the scheduler accepts generator
           _lowercase       :							Dict      		=		'generator' in set(inspect.signature(self.scheduler.step).parameters.keys())
           if accepts_generator:
               _lowercase       :							str      		=		generator

           with self.progress_bar(total=lowerCamelCase):
               for i, t in enumerate(lowerCamelCase):
                   # expand the latents if we are doing classifier free guidance
                   _lowercase       :							List[str]      		=		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                   _lowercase       :							List[Any]      		=		self.scheduler.scale_model_input(lowerCamelCase,      lowerCamelCase)

                   # predict the noise residual
                   _lowercase       :							Dict      		=		self.unet(lowerCamelCase,      lowerCamelCase,      encoder_hidden_states=lowerCamelCase).sample

                   # perform classifier free guidance
                   if do_classifier_free_guidance:
                       _lowercase		,				_lowercase       :							Optional[Any]      		=		noise_pred.chunk(2)
                       _lowercase       :							Optional[Any]      		=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                   # perform clip guidance
                   if clip_guidance_scale > 0:
                       _lowercase       :							Tuple      		=		(
                           text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                       )
                       _lowercase		,				_lowercase       :							List[Any]      		=		self.cond_fn(
                           lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )

                   # compute the previous noisy sample x_t -> x_t-1
                   _lowercase       :							Optional[Any]      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      **lowerCamelCase).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
           _lowercase       :							Any      		=		1 / 0.1_8_2_1_5 * latents
           _lowercase       :							List[str]      		=		self.vae.decode(lowerCamelCase).sample

           _lowercase       :							Tuple      		=		(image / 2 + 0.5).clamp(0,      1)
           _lowercase       :							List[Any]      		=		image.cpu().permute(0,      2,      3,      1).numpy()

           if output_type == "pil":
               _lowercase       :							List[Any]      		=		self.numpy_to_pil(lowerCamelCase)

           if not return_dict:
               return (image, None)

           return StableDiffusionPipelineOutput(images=lowerCamelCase,      nsfw_content_detected=lowerCamelCase)


