



import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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





class 		__SCREAMING_SNAKE_CASE      (				nn.Module      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=1.0      ):






					"""simple docstring"""

					super().__init__()

					_snake_case					:							Dict					     =   cut_size
					_snake_case					:							List[Any]					     =   cut_power







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							Dict					     =   pixel_values.shape[2:4]
					_snake_case					:							Tuple					     =   min(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   min(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	self.cut_size      )
					_snake_case					:							int					     =   []
					for _ in range(SCREAMING_SNAKE_CASE__      ):
						_snake_case					:							List[str]					     =   int(torch.rand([]      ) ** self.cut_power * (max_size - min_size) + min_size      )
						_snake_case					:							List[str]					     =   torch.randint(0						,	sideX - size + 1						,	()      )
						_snake_case					:							Union[str, Any]					     =   torch.randint(0						,	sideY - size + 1						,	()      )
						_snake_case					:							int					     =   pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
						cutouts.append(F.adaptive_avg_poolad(SCREAMING_SNAKE_CASE__						,	self.cut_size      )      )
					return torch.cat(SCREAMING_SNAKE_CASE__      )
def   UpperCAmelCase							(		A__				,  A__     )							->					str:
	_snake_case					:							str					     =   F.normalize(A__				,  dim=-1     )
	_snake_case					:							Tuple					     =   F.normalize(A__				,  dim=-1     )
	return (x - y).norm(dim=-1     ).div(2     ).arcsin().pow(2     ).mul(2     )
def   UpperCAmelCase							(		A__				,  A__     )							->					int:
	for param in model.parameters():
		_snake_case					:							Union[str, Any]					     =   value





class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					super().__init__()
					self.register_modules(
					    vae=SCREAMING_SNAKE_CASE__						,	text_encoder=SCREAMING_SNAKE_CASE__						,	clip_model=SCREAMING_SNAKE_CASE__						,	tokenizer=SCREAMING_SNAKE_CASE__						,	unet=SCREAMING_SNAKE_CASE__						,	scheduler=SCREAMING_SNAKE_CASE__						,	feature_extractor=SCREAMING_SNAKE_CASE__						,	)

					_snake_case					:							str					     =   transforms.Normalize(mean=feature_extractor.image_mean						,	std=feature_extractor.image_std      )
					_snake_case					:							Optional[Any]					     =   (
					    feature_extractor.size
					    if isinstance(feature_extractor.size						,	SCREAMING_SNAKE_CASE__      )
					    else feature_extractor.size["""shortest_edge"""]
					)
					_snake_case					:							Dict					     =   MakeCutouts(self.cut_out_size      )

					set_requires_grad(self.text_encoder						,	SCREAMING_SNAKE_CASE__      )
					set_requires_grad(self.clip_model						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = "auto"      ):






					"""simple docstring"""

					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						_snake_case					:							Tuple					     =   self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.enable_attention_slicing(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					set_requires_grad(self.vae						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					set_requires_grad(self.vae						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					set_requires_grad(self.unet						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					set_requires_grad(self.unet						,	SCREAMING_SNAKE_CASE__      )







				@torch.enable_grad()
				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=True						,	):






					"""simple docstring"""

					_snake_case					:							int					     =   latents.detach().requires_grad_()

					_snake_case					:							Optional[Any]					     =   self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# predict the noise residual
					_snake_case					:							List[Any]					     =   self.unet(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	encoder_hidden_states=SCREAMING_SNAKE_CASE__      ).sample

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

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

					_snake_case					:							int					     =   1 / self.vae.config.scaling_factor * sample
					_snake_case					:							Union[str, Any]					     =   self.vae.decode(SCREAMING_SNAKE_CASE__      ).sample
					_snake_case					:							Dict					     =   (image / 2 + 0.5).clamp(0						,	1      )

					if use_cutouts:
						_snake_case					:							str					     =   self.make_cutouts(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Optional[int]					     =   transforms.Resize(self.cut_out_size      )(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.normalize(SCREAMING_SNAKE_CASE__      ).to(latents.dtype      )

					_snake_case					:							Optional[Any]					     =   self.clip_model.get_image_features(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Dict					     =   image_embeddings_clip / image_embeddings_clip.norm(p=2						,	dim=-1						,	keepdim=SCREAMING_SNAKE_CASE__      )

					if use_cutouts:
						_snake_case					:							Union[str, Any]					     =   spherical_dist_loss(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Optional[Any]					     =   dists.view([num_cutouts, sample.shape[0], -1]      )
						_snake_case					:							Any					     =   dists.sum(2      ).mean(0      ).sum() * clip_guidance_scale
					else:
						_snake_case					:							List[str]					     =   spherical_dist_loss(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ).mean() * clip_guidance_scale

					_snake_case					:							Union[str, Any]					     =   -torch.autograd.grad(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )[0]

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







				@torch.no_grad()
				def __call__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = 5_12						,	SCREAMING_SNAKE_CASE__ = 5_12						,	SCREAMING_SNAKE_CASE__ = 50						,	SCREAMING_SNAKE_CASE__ = 7.5						,	SCREAMING_SNAKE_CASE__ = 1						,	SCREAMING_SNAKE_CASE__ = 0.0						,	SCREAMING_SNAKE_CASE__ = 1_00						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 4						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = "pil"						,	SCREAMING_SNAKE_CASE__ = True						,	):






					"""simple docstring"""

					if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
						_snake_case					:							Any					     =   1
					elif isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):
						_snake_case					:							Union[str, Any]					     =   len(SCREAMING_SNAKE_CASE__      )
					else:
						raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE__      )}'''      )

					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}.'''      )

					# get prompt text embeddings
					_snake_case					:							Optional[Any]					     =   self.tokenizer(
					    SCREAMING_SNAKE_CASE__						,	padding="""max_length"""						,	max_length=self.tokenizer.model_max_length						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="""pt"""						,	)
					_snake_case					:							Union[str, Any]					     =   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]
					# duplicate text embeddings for each generation per prompt
					_snake_case					:							List[str]					     =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE__						,	dim=0      )

					if clip_guidance_scale > 0:
						if clip_prompt is not None:
							_snake_case					:							str					     =   self.tokenizer(
							    SCREAMING_SNAKE_CASE__						,	padding="""max_length"""						,	max_length=self.tokenizer.model_max_length						,	truncation=SCREAMING_SNAKE_CASE__						,	return_tensors="""pt"""						,	).input_ids.to(self.device      )
						else:
							_snake_case					:							Optional[Any]					     =   text_input.input_ids.to(self.device      )
						_snake_case					:							Dict					     =   self.clip_model.get_text_features(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							List[str]					     =   text_embeddings_clip / text_embeddings_clip.norm(p=2						,	dim=-1						,	keepdim=SCREAMING_SNAKE_CASE__      )
						# duplicate text embeddings clip for each generation per prompt
						_snake_case					:							Union[str, Any]					     =   text_embeddings_clip.repeat_interleave(SCREAMING_SNAKE_CASE__						,	dim=0      )

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

					# set timesteps
					_snake_case					:							str					     =   """offset""" in set(inspect.signature(self.scheduler.set_timesteps      ).parameters.keys()      )
					_snake_case					:							Optional[int]					     =   {}
					if accepts_offset:
						_snake_case					:							List[Any]					     =   1

					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      )

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					_snake_case					:							str					     =   self.scheduler.timesteps.to(self.device      )

					# scale the initial noise by the standard deviation required by the scheduler
					_snake_case					:							Dict					     =   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]
					_snake_case					:							Optional[Any]					     =   """eta""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
					_snake_case					:							Any					     =   {}
					if accepts_eta:
						_snake_case					:							Optional[int]					     =   eta

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

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

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

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

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

						# compute the previous noisy sample x_t -> x_t-1
						_snake_case					:							List[str]					     =   self.scheduler.step(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__      ).prev_sample

					# scale and decode the image latents with vae
					_snake_case					:							Optional[int]					     =   1 / self.vae.config.scaling_factor * latents
					_snake_case					:							int					     =   self.vae.decode(SCREAMING_SNAKE_CASE__      ).sample

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

					if output_type == "pil":
						_snake_case					:							Any					     =   self.numpy_to_pil(SCREAMING_SNAKE_CASE__      )

					if not return_dict:
						return (image, None)

					return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE__						,	nsfw_content_detected=SCREAMING_SNAKE_CASE__      )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						UpperCAmelCase_			      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





