



'''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 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     List[Any]:
							if isinstance(_lowerCAmelCase     ,   torch.Tensor						):
														return image
							elif isinstance(_lowerCAmelCase     ,   PIL.Image.Image						):
														UpperCAmelCase				: List[str]											=	[image]

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


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		Optional[int]=0.9_9_9_5						)   ->     Optional[int]:
							if not isinstance(_lowerCAmelCase     ,   np.ndarray						):
														UpperCAmelCase				: Dict											=	True
														UpperCAmelCase				: List[Any]											=	va.device
														UpperCAmelCase				: Any											=	va.cpu().numpy()
														UpperCAmelCase				: Optional[Any]											=	va.cpu().numpy()

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

							if inputs_are_torch:
														UpperCAmelCase				: List[str]											=	torch.from_numpy(_lowerCAmelCase						).to(_lowerCAmelCase						)

							return va


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Any						)   ->     Tuple:
							UpperCAmelCase				: str											=	F.normalize(_lowerCAmelCase     ,   dim=-1						)
							UpperCAmelCase				: int											=	F.normalize(_lowerCAmelCase     ,   dim=-1						)
							return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		int						)   ->     int:
							for param in model.parameters():
														UpperCAmelCase				: List[str]											=	value




class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







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







	def   A			(		self    :  List[str]	)    ->	int:
								self.enable_attention_slicing(__snake_case	)







	def   A			(		self    :  Any	)    ->	Optional[int]:
								set_requires_grad(self.vae	,    __snake_case	)







	def   A			(		self    :  List[str]	)    ->	int:
								set_requires_grad(self.vae	,    __snake_case	)







	def   A			(		self    :  List[Any]	)    ->	int:
								set_requires_grad(self.unet	,    __snake_case	)







	def   A			(		self    :  str	)    ->	Any:
								set_requires_grad(self.unet	,    __snake_case	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str	,    __snake_case    :  str	,    __snake_case    :  List[str]	)    ->	List[str]:
								# get the original timestep using init_timestep
								UpperCAmelCase				: Dict											=	min(int(num_inference_steps * strength	)	,    __snake_case	)

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

								return timesteps, num_inference_steps - t_start







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

								UpperCAmelCase				: List[str]											=	image.to(device=__snake_case	,    dtype=__snake_case	)

								if isinstance(__snake_case	,    __snake_case	):
															UpperCAmelCase				: Optional[int]											=	[
															    self.vae.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(__snake_case	)
															]
															UpperCAmelCase				: Union[str, Any]											=	torch.cat(__snake_case	,    dim=0	)
								else:
															UpperCAmelCase				: Any											=	self.vae.encode(__snake_case	).latent_dist.sample(__snake_case	)

								# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								UpperCAmelCase				: Dict											=	0.1_82_15 * init_latents
								UpperCAmelCase				: Optional[Any]											=	init_latents.repeat_interleave(__snake_case	,    dim=0	)

								UpperCAmelCase				: Tuple											=	randn_tensor(init_latents.shape	,    generator=__snake_case	,    device=__snake_case	,    dtype=__snake_case	)

								# get latents
								UpperCAmelCase				: Optional[Any]											=	self.scheduler.add_noise(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: List[str]											=	init_latents

								return latents







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







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







	@torch.enable_grad()
	def   A			(		self    :  Optional[int]	,    __snake_case    :  Tuple	,    __snake_case    :  List[str]	,    __snake_case    :  List[Any]	,    __snake_case    :  List[str]	,    __snake_case    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  int	,    )    ->	Union[str, Any]:
								UpperCAmelCase				: List[str]											=	latents.detach().requires_grad_()

								UpperCAmelCase				: Any											=	self.scheduler.scale_model_input(__snake_case	,    __snake_case	)

								# predict the noise residual
								UpperCAmelCase				: List[str]											=	self.unet(__snake_case	,    __snake_case	,    encoder_hidden_states=__snake_case	).sample

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

															UpperCAmelCase				: Dict											=	torch.sqrt(__snake_case	)
															UpperCAmelCase				: Tuple											=	pred_original_sample * (fac) + latents * (1 - fac)
								elif isinstance(self.scheduler	,    __snake_case	):
															UpperCAmelCase				: Tuple											=	self.scheduler.sigmas[index]
															UpperCAmelCase				: 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
								UpperCAmelCase				: List[str]											=	1 / 0.1_82_15 * sample
								UpperCAmelCase				: int											=	self.vae.decode(__snake_case	).sample
								UpperCAmelCase				: Any											=	(image / 2 + 0.5).clamp(0	,    1	)

								UpperCAmelCase				: List[str]											=	transforms.Resize(self.feature_extractor_size	)(__snake_case	)
								UpperCAmelCase				: str											=	self.normalize(__snake_case	).to(latents.dtype	)

								UpperCAmelCase				: List[Any]											=	self.clip_model.get_image_features(__snake_case	)
								UpperCAmelCase				: int											=	image_embeddings_clip / image_embeddings_clip.norm(p=2	,    dim=-1	,    keepdim=__snake_case	)

								UpperCAmelCase				: str											=	spherical_dist_loss(__snake_case	,    __snake_case	).mean() * clip_guidance_scale

								UpperCAmelCase				: Any											=	-torch.autograd.grad(__snake_case	,    __snake_case	)[0]

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







	@torch.no_grad()
	def __call__(		self    :  Optional[Any]	,    __snake_case    :  Union[torch.FloatTensor, PIL.Image.Image]	,    __snake_case    :  Union[torch.FloatTensor, PIL.Image.Image]	,    __snake_case    :  Optional[str] = None	,    __snake_case    :  Optional[str] = None	,    __snake_case    :  Optional[int] = 512	,    __snake_case    :  Optional[int] = 512	,    __snake_case    :  float = 0.6	,    __snake_case    :  Optional[int] = 50	,    __snake_case    :  Optional[float] = 7.5	,    __snake_case    :  Optional[int] = 1	,    __snake_case    :  float = 0.0	,    __snake_case    :  Optional[float] = 100	,    __snake_case    :  Optional[torch.Generator] = None	,    __snake_case    :  Optional[str] = "pil"	,    __snake_case    :  bool = True	,    __snake_case    :  float = 0.8	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    )    ->	List[str]:
								if isinstance(__snake_case	,    __snake_case	) and len(__snake_case	) != batch_size:
															raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(__snake_case	)} 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(__snake_case	,    torch.Generator	) and batch_size > 1:
															UpperCAmelCase				: Union[str, Any]											=	[generator] + [None] * (batch_size - 1)

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

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

								UpperCAmelCase				: int											=	self.tokenizer(
								    __snake_case	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=__snake_case	,    return_tensors='''pt'''	,    )
								UpperCAmelCase				: Optional[Any]											=	self.text_encoder(style_text_input.input_ids.to(self.device	)	)[0]

								UpperCAmelCase				: Dict											=	slerp(__snake_case	,    __snake_case	,    __snake_case	)

								# duplicate text embeddings for each generation per prompt
								UpperCAmelCase				: List[Any]											=	text_embeddings.repeat_interleave(__snake_case	,    dim=0	)

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

								self.scheduler.set_timesteps(__snake_case	,    **__snake_case	)
								# 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				: str											=	self.get_timesteps(__snake_case	,    __snake_case	,    self.device	)
								UpperCAmelCase				: Union[str, Any]											=	timesteps[:1].repeat(__snake_case	)

								# Preprocess image
								UpperCAmelCase				: str											=	preprocess(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Tuple											=	self.prepare_latents(
								    __snake_case	,    __snake_case	,    __snake_case	,    text_embeddings.dtype	,    self.device	,    __snake_case	)

								UpperCAmelCase				: Dict											=	preprocess(__snake_case	,    __snake_case	,    __snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	self.prepare_latents(
								    __snake_case	,    __snake_case	,    __snake_case	,    text_embeddings.dtype	,    self.device	,    __snake_case	)

								UpperCAmelCase				: int											=	slerp(__snake_case	,    __snake_case	,    __snake_case	)

								if clip_guidance_scale > 0:
															UpperCAmelCase				: int											=	self.get_clip_image_embeddings(__snake_case	,    __snake_case	)
															UpperCAmelCase				: str											=	self.get_clip_image_embeddings(__snake_case	,    __snake_case	)
															UpperCAmelCase				: str											=	slerp(
															    __snake_case	,    __snake_case	,    __snake_case	)

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

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase				: Optional[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]
								UpperCAmelCase				: Tuple											=	'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
								UpperCAmelCase				: Union[str, Any]											=	{}
								if accepts_eta:
															UpperCAmelCase				: Dict											=	eta

								# check if the scheduler accepts generator
								UpperCAmelCase				: Optional[Any]											=	'''generator''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
								if accepts_generator:
															UpperCAmelCase				: Optional[Any]											=	generator

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

																						# predict the noise residual
																						UpperCAmelCase				: Optional[int]											=	self.unet(__snake_case	,    __snake_case	,    encoder_hidden_states=__snake_case	).sample

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

																						# perform clip guidance
																						if clip_guidance_scale > 0:
																													UpperCAmelCase				: Union[str, Any]											=	(
																													    text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
																													)
																													UpperCAmelCase				: Tuple											=	self.cond_fn(
																													    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
								UpperCAmelCase				: Any											=	1 / 0.1_82_15 * latents
								UpperCAmelCase				: Optional[int]											=	self.vae.decode(__snake_case	).sample

								UpperCAmelCase				: int											=	(image / 2 + 0.5).clamp(0	,    1	)
								UpperCAmelCase				: Dict											=	image.cpu().permute(0	,    2	,    3	,    1	).numpy()

								if output_type == "pil":
															UpperCAmelCase				: Any											=	self.numpy_to_pil(__snake_case	)

								if not return_dict:
															return (image, None)

								return StableDiffusionPipelineOutput(images=__snake_case	,    nsfw_content_detected=__snake_case	)










'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

UpperCamelCase__:     int										=    {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """megatron-bert"""







	def __init__(		self    :  int	,    __snake_case    :  List[Any]=29056	,    __snake_case    :  Dict=1024	,    __snake_case    :  Tuple=24	,    __snake_case    :  Any=16	,    __snake_case    :  Tuple=4096	,    __snake_case    :  int="gelu"	,    __snake_case    :  Dict=0.1	,    __snake_case    :  str=0.1	,    __snake_case    :  Optional[Any]=512	,    __snake_case    :  Tuple=2	,    __snake_case    :  Tuple=0.02	,    __snake_case    :  int=1E-12	,    __snake_case    :  Union[str, Any]=0	,    __snake_case    :  int="absolute"	,    __snake_case    :  str=True	,    **__snake_case    :  Tuple	,    )    ->	Union[str, Any]:
								super().__init__(pad_token_id=__snake_case	,    **__snake_case	)

								UpperCAmelCase				: Any											=	vocab_size
								UpperCAmelCase				: Tuple											=	hidden_size
								UpperCAmelCase				: List[Any]											=	num_hidden_layers
								UpperCAmelCase				: str											=	num_attention_heads
								UpperCAmelCase				: Optional[int]											=	hidden_act
								UpperCAmelCase				: Optional[int]											=	intermediate_size
								UpperCAmelCase				: Dict											=	hidden_dropout_prob
								UpperCAmelCase				: List[Any]											=	attention_probs_dropout_prob
								UpperCAmelCase				: Dict											=	max_position_embeddings
								UpperCAmelCase				: Any											=	type_vocab_size
								UpperCAmelCase				: str											=	initializer_range
								UpperCAmelCase				: str											=	layer_norm_eps
								UpperCAmelCase				: Optional[Any]											=	position_embedding_type
								UpperCAmelCase				: str											=	use_cache





