



"""simple docstring"""


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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Dict					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				if isinstance(_SCREAMING_SNAKE_CASE					,     torch.Tensor				):
								return image
				elif isinstance(_SCREAMING_SNAKE_CASE					,     PIL.Image.Image				):
								_UpperCAmelCase					=    [image]

				if isinstance(image[0]					,     PIL.Image.Image				):
								_UpperCAmelCase					=    [np.array(i.resize((w, h)					,     resample=PIL_INTERPOLATION['''lanczos''']				)				)[None, :] for i in image]
								_UpperCAmelCase					=    np.concatenate(_SCREAMING_SNAKE_CASE					,     axis=0				)
								_UpperCAmelCase					=    np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
								_UpperCAmelCase					=    image.transpose(0					,     3					,     1					,     2				)
								_UpperCAmelCase					=    2.0 * image - 1.0
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE				)
				elif isinstance(image[0]					,     torch.Tensor				):
								_UpperCAmelCase					=    torch.cat(_SCREAMING_SNAKE_CASE					,     dim=0				)
				return image







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Any=0.9995				):






				'''simple docstring'''



				if not isinstance(_SCREAMING_SNAKE_CASE					,     np.ndarray				):
								_UpperCAmelCase					=    True
								_UpperCAmelCase					=    va.device
								_UpperCAmelCase					=    va.cpu().numpy()
								_UpperCAmelCase					=    va.cpu().numpy()

				_UpperCAmelCase					=    np.sum(va * va / (np.linalg.norm(_SCREAMING_SNAKE_CASE				) * np.linalg.norm(_SCREAMING_SNAKE_CASE				))				)
				if np.abs(_SCREAMING_SNAKE_CASE				) > DOT_THRESHOLD:
								_UpperCAmelCase					=    (1 - t) * va + t * va
				else:
								_UpperCAmelCase					=    np.arccos(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    np.sin(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    theta_a * t
								_UpperCAmelCase					=    np.sin(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    np.sin(theta_a - theta_t				) / sin_theta_a
								_UpperCAmelCase					=    sin_theta_t / sin_theta_a
								_UpperCAmelCase					=    sa * va + sa * va

				if inputs_are_torch:
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE				).to(_SCREAMING_SNAKE_CASE				)

				return va







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    F.normalize(_SCREAMING_SNAKE_CASE					,     dim=-1				)
				_UpperCAmelCase					=    F.normalize(_SCREAMING_SNAKE_CASE					,     dim=-1				)
				return (x - y).norm(dim=-1				).div(2				).arcsin().pow(2				).mul(2				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				for param in model.parameters():
								_UpperCAmelCase					=    value
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    AutoencoderKL				,   __UpperCamelCase     :    CLIPTextModel				,   __UpperCamelCase     :    CLIPModel				,   __UpperCamelCase     :    CLIPTokenizer				,   __UpperCamelCase     :    UNetaDConditionModel				,   __UpperCamelCase     :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]				,   __UpperCamelCase     :    CLIPFeatureExtractor				,   __UpperCamelCase     :    List[Any]=None				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Any=None				,   )->Union[str, Any]:
							super().__init__()
							self.register_modules(
							    vae=__UpperCamelCase				,   text_encoder=__UpperCamelCase				,   clip_model=__UpperCamelCase				,   tokenizer=__UpperCamelCase				,   unet=__UpperCamelCase				,   scheduler=__UpperCamelCase				,   feature_extractor=__UpperCamelCase				,   coca_model=__UpperCamelCase				,   coca_tokenizer=__UpperCamelCase				,   coca_transform=__UpperCamelCase				,   )
							_UpperCAmelCase					=    (
							    feature_extractor.size
							    if isinstance(feature_extractor.size				,   __UpperCamelCase   )
							    else feature_extractor.size['''shortest_edge''']
							)
							_UpperCAmelCase					=    transforms.Normalize(mean=feature_extractor.image_mean				,   std=feature_extractor.image_std   )
							set_requires_grad(self.text_encoder				,   __UpperCamelCase   )
							set_requires_grad(self.clip_model				,   __UpperCamelCase   )



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



			def  lowercase__						(		self     :    str   )->str:
							self.enable_attention_slicing(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]   )->Optional[int]:
							set_requires_grad(self.vae				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    int   )->List[Any]:
							set_requires_grad(self.vae				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Optional[int]   )->List[Any]:
							set_requires_grad(self.unet				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]   )->List[Any]:
							set_requires_grad(self.unet				,   __UpperCamelCase   )



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Dict				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Tuple   )->Tuple:
							# get the original timestep using init_timestep
							_UpperCAmelCase					=    min(int(num_inference_steps * strength   )				,   __UpperCamelCase   )

							_UpperCAmelCase					=    max(num_inference_steps - init_timestep				,   0   )
							_UpperCAmelCase					=    self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



			def  lowercase__						(		self     :    Dict				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Optional[Any]=None   )->Tuple:
							if not isinstance(__UpperCamelCase				,   torch.Tensor   ):
											raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__UpperCamelCase   )}'   )

							_UpperCAmelCase					=    image.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   )

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

							# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							_UpperCAmelCase					=    0.1_8_2_1_5 * init_latents
							_UpperCAmelCase					=    init_latents.repeat_interleave(__UpperCamelCase				,   dim=0   )

							_UpperCAmelCase					=    randn_tensor(init_latents.shape				,   generator=__UpperCamelCase				,   device=__UpperCamelCase				,   dtype=__UpperCamelCase   )

							# get latents
							_UpperCAmelCase					=    self.scheduler.add_noise(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    init_latents

							return latents



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



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    str   )->Any:
							_UpperCAmelCase					=    self.feature_extractor.preprocess(__UpperCamelCase   )
							_UpperCAmelCase					=    torch.from_numpy(clip_image_input['''pixel_values'''][0]   ).unsqueeze(0   ).to(self.device   ).half()
							_UpperCAmelCase					=    self.clip_model.get_image_features(__UpperCamelCase   )
							_UpperCAmelCase					=    image_embeddings_clip / image_embeddings_clip.norm(p=2				,   dim=-1				,   keepdim=__UpperCamelCase   )
							_UpperCAmelCase					=    image_embeddings_clip.repeat_interleave(__UpperCamelCase				,   dim=0   )
							return image_embeddings_clip



			@torch.enable_grad()
			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any				,   __UpperCamelCase     :    List[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   )->str:
							_UpperCAmelCase					=    latents.detach().requires_grad_()

							_UpperCAmelCase					=    self.scheduler.scale_model_input(__UpperCamelCase				,   __UpperCamelCase   )

							# predict the noise residual
							_UpperCAmelCase					=    self.unet(__UpperCamelCase				,   __UpperCamelCase				,   encoder_hidden_states=__UpperCamelCase   ).sample

							if isinstance(self.scheduler				,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
											_UpperCAmelCase					=    self.scheduler.alphas_cumprod[timestep]
											_UpperCAmelCase					=    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
											_UpperCAmelCase					=    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

											_UpperCAmelCase					=    torch.sqrt(__UpperCamelCase   )
											_UpperCAmelCase					=    pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    self.scheduler.sigmas[index]
											_UpperCAmelCase					=    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
							_UpperCAmelCase					=    1 / 0.1_8_2_1_5 * sample
							_UpperCAmelCase					=    self.vae.decode(__UpperCamelCase   ).sample
							_UpperCAmelCase					=    (image / 2 + 0.5).clamp(0				,   1   )

							_UpperCAmelCase					=    transforms.Resize(self.feature_extractor_size   )(__UpperCamelCase   )
							_UpperCAmelCase					=    self.normalize(__UpperCamelCase   ).to(latents.dtype   )

							_UpperCAmelCase					=    self.clip_model.get_image_features(__UpperCamelCase   )
							_UpperCAmelCase					=    image_embeddings_clip / image_embeddings_clip.norm(p=2				,   dim=-1				,   keepdim=__UpperCamelCase   )

							_UpperCAmelCase					=    spherical_dist_loss(__UpperCamelCase				,   __UpperCamelCase   ).mean() * clip_guidance_scale

							_UpperCAmelCase					=    -torch.autograd.grad(__UpperCamelCase				,   __UpperCamelCase   )[0]

							if isinstance(self.scheduler				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    latents.detach() + grads * (sigma**2)
											_UpperCAmelCase					=    noise_pred_original
							else:
											_UpperCAmelCase					=    noise_pred_original - torch.sqrt(__UpperCamelCase   ) * grads
							return noise_pred, latents



			@torch.no_grad()
			def __call__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Union[torch.FloatTensor, PIL.Image.Image]				,   __UpperCamelCase     :    Union[torch.FloatTensor, PIL.Image.Image]				,   __UpperCamelCase     :    Optional[str] = None				,   __UpperCamelCase     :    Optional[str] = None				,   __UpperCamelCase     :    Optional[int] = 5_1_2				,   __UpperCamelCase     :    Optional[int] = 5_1_2				,   __UpperCamelCase     :    float = 0.6				,   __UpperCamelCase     :    Optional[int] = 5_0				,   __UpperCamelCase     :    Optional[float] = 7.5				,   __UpperCamelCase     :    Optional[int] = 1				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    Optional[float] = 1_0_0				,   __UpperCamelCase     :    Optional[torch.Generator] = None				,   __UpperCamelCase     :    Optional[str] = "pil"				,   __UpperCamelCase     :    bool = True				,   __UpperCamelCase     :    float = 0.8				,   __UpperCamelCase     :    float = 0.1				,   __UpperCamelCase     :    float = 0.1				,   )->str:
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) and len(__UpperCamelCase   ) != batch_size:
											raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__UpperCamelCase   )} 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(__UpperCamelCase				,   torch.Generator   ) and batch_size > 1:
											_UpperCAmelCase					=    [generator] + [None] * (batch_size - 1)

							_UpperCAmelCase					=    [
							    ('''model''', self.coca_model is None),
							    ('''tokenizer''', self.coca_tokenizer is None),
							    ('''transform''', self.coca_transform is None),
							]
							_UpperCAmelCase					=    [x[0] for x in coca_is_none if x[1]]
							_UpperCAmelCase					=    ''', '''.join(__UpperCamelCase   )
							# generate prompts with coca model if prompt is None
							if content_prompt is None:
											if len(__UpperCamelCase   ):
															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.'   )
											_UpperCAmelCase					=    self.get_image_description(__UpperCamelCase   )
							if style_prompt is None:
											if len(__UpperCamelCase   ):
															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.'   )
											_UpperCAmelCase					=    self.get_image_description(__UpperCamelCase   )

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

							_UpperCAmelCase					=    self.tokenizer(
							    __UpperCamelCase				,   padding='''max_length'''				,   max_length=self.tokenizer.model_max_length				,   truncation=__UpperCamelCase				,   return_tensors='''pt'''				,   )
							_UpperCAmelCase					=    self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

							_UpperCAmelCase					=    slerp(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

							# duplicate text embeddings for each generation per prompt
							_UpperCAmelCase					=    text_embeddings.repeat_interleave(__UpperCamelCase				,   dim=0   )

							# set timesteps
							_UpperCAmelCase					=    '''offset''' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
							_UpperCAmelCase					=    {}
							if accepts_offset:
											_UpperCAmelCase					=    1

							self.scheduler.set_timesteps(__UpperCamelCase				,   **__UpperCamelCase   )
							# 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   )

							_UpperCAmelCase		,						_UpperCAmelCase					=    self.get_timesteps(__UpperCamelCase				,   __UpperCamelCase				,   self.device   )
							_UpperCAmelCase					=    timesteps[:1].repeat(__UpperCamelCase   )

							# Preprocess image
							_UpperCAmelCase					=    preprocess(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    self.prepare_latents(
							    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   text_embeddings.dtype				,   self.device				,   __UpperCamelCase   )

							_UpperCAmelCase					=    preprocess(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )
							_UpperCAmelCase					=    self.prepare_latents(
							    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   text_embeddings.dtype				,   self.device				,   __UpperCamelCase   )

							_UpperCAmelCase					=    slerp(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

							if clip_guidance_scale > 0:
											_UpperCAmelCase					=    self.get_clip_image_embeddings(__UpperCamelCase				,   __UpperCamelCase   )
											_UpperCAmelCase					=    self.get_clip_image_embeddings(__UpperCamelCase				,   __UpperCamelCase   )
											_UpperCAmelCase					=    slerp(
											    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase   )

							# 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					=    guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
											_UpperCAmelCase					=    content_text_input.input_ids.shape[-1]
											_UpperCAmelCase					=    self.tokenizer(['''''']				,   padding='''max_length'''				,   max_length=__UpperCamelCase				,   return_tensors='''pt'''   )
											_UpperCAmelCase					=    self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
											# duplicate unconditional embeddings for each generation per prompt
											_UpperCAmelCase					=    uncond_embeddings.repeat_interleave(__UpperCamelCase				,   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
											_UpperCAmelCase					=    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`.
							_UpperCAmelCase					=    (batch_size, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCAmelCase					=    text_embeddings.dtype
							if latents is None:
											if self.device.type == "mps":
															# randn does not work reproducibly on mps
															_UpperCAmelCase					=    torch.randn(__UpperCamelCase				,   generator=__UpperCamelCase				,   device='''cpu'''				,   dtype=__UpperCamelCase   ).to(
															    self.device   )
											else:
															_UpperCAmelCase					=    torch.randn(__UpperCamelCase				,   generator=__UpperCamelCase				,   device=self.device				,   dtype=__UpperCamelCase   )
							else:
											if latents.shape != latents_shape:
															raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
											_UpperCAmelCase					=    latents.to(self.device   )

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCAmelCase					=    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]
							_UpperCAmelCase					=    '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
							_UpperCAmelCase					=    {}
							if accepts_eta:
											_UpperCAmelCase					=    eta

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

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

															# predict the noise residual
															_UpperCAmelCase					=    self.unet(__UpperCamelCase				,   __UpperCamelCase				,   encoder_hidden_states=__UpperCamelCase   ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																			_UpperCAmelCase		,						_UpperCAmelCase					=    noise_pred.chunk(2   )
																			_UpperCAmelCase					=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																			_UpperCAmelCase					=    (
																			    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																			)
																			_UpperCAmelCase		,						_UpperCAmelCase					=    self.cond_fn(
																			    __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   )

															# compute the previous noisy sample x_t -> x_t-1
															_UpperCAmelCase					=    self.scheduler.step(__UpperCamelCase				,   __UpperCamelCase				,   __UpperCamelCase				,   **__UpperCamelCase   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
							_UpperCAmelCase					=    1 / 0.1_8_2_1_5 * latents
							_UpperCAmelCase					=    self.vae.decode(__UpperCamelCase   ).sample

							_UpperCAmelCase					=    (image / 2 + 0.5).clamp(0				,   1   )
							_UpperCAmelCase					=    image.cpu().permute(0				,   2				,   3				,   1   ).numpy()

							if output_type == "pil":
											_UpperCAmelCase					=    self.numpy_to_pil(__UpperCamelCase   )

							if not return_dict:
											return (image, None)

							return StableDiffusionPipelineOutput(images=__UpperCamelCase				,   nsfw_content_detected=__UpperCamelCase   )








"""simple docstring"""


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    [self.constructed_objects[key_node] for key_node, _ in node.value]
							_UpperCAmelCase					=    [tuple(__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else key for key in keys]
							_UpperCAmelCase					=    Counter(__UpperCamelCase   )
							_UpperCAmelCase					=    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
											raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    list(readme_content.splitlines()				)
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								_UpperCAmelCase					=    full_content[1:].index('''---'''				) + 1
								_UpperCAmelCase					=    '''\n'''.join(full_content[1:sep_idx]				)
								return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

				return None, "\n".join(_SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(readme_file.read()   )
							if yaml_string is not None:
											return cls.from_yaml_string(__UpperCamelCase   )
							else:
											return cls()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							if path.exists():
											with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
															_UpperCAmelCase					=    readme_file.read()
							else:
											_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self._to_readme(__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as readme_file:
											readme_file.write(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							if readme_content is not None:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(__UpperCamelCase   )
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
							else:
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
							return full_content



			@classmethod
			def  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_UpperCAmelCase					=    yaml.load(__UpperCamelCase				,   Loader=_NoDuplicateSafeLoader   ) or {}

							# Convert the YAML keys to DatasetMetadata fields
							_UpperCAmelCase					=    {
							    (key.replace('''-'''				,   '''_'''   ) if key.replace('''-'''				,   '''_'''   ) in cls._FIELDS_WITH_DASHES else key): value
							    for key, value in metadata_dict.items()
							}
							return cls(**__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->str:
							return yaml.safe_dump(
							    {
							        (key.replace('''_'''				,   '''-'''   ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    }				,   sort_keys=__UpperCamelCase				,   allow_unicode=__UpperCamelCase				,   encoding='''utf-8'''				,   ).decode('''utf-8'''   )



__A   :							str										=   {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
					from argparse import ArgumentParser

					__A   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



