





"""simple docstring"""
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  _UpperCAmelCase       (     nn.Module			):


					def __init__( self      :List[str]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Tuple=1.0		):
												super().__init__()

												A		=  cut_size
												A		=  cut_power


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Dict					,	__UpperCamelCase      :List[str]		):
												A,     A		=  pixel_values.shape[2:4]
												A		=  min(__UpperCamelCase					,	__UpperCamelCase		)
												A		=  min(__UpperCamelCase					,	__UpperCamelCase					,	self.cut_size		)
												A		=  []
												for _ in range(__UpperCamelCase		):
																			A		=  int(torch.rand([]		) ** self.cut_power * (max_size - min_size) + min_size		)
																			A		=  torch.randint(0					,	sideX - size + 1					,	()		)
																			A		=  torch.randint(0					,	sideY - size + 1					,	()		)
																			A		=  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
																			cutouts.append(F.adaptive_avg_poolad(__UpperCamelCase					,	self.cut_size		)		)
												return torch.cat(__UpperCamelCase		)







def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  F.normalize(UpperCamelCase ,  dim=-1	)
							A		=  F.normalize(UpperCamelCase ,  dim=-1	)
							return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							for param in model.parameters():
														A		=  value






class  _UpperCAmelCase       (     lowercase_			):


					def __init__( self      :List[str]					,	__UpperCamelCase      :AutoencoderKL					,	__UpperCamelCase      :CLIPTextModel					,	__UpperCamelCase      :CLIPModel					,	__UpperCamelCase      :CLIPTokenizer					,	__UpperCamelCase      :UNetaDConditionModel					,	__UpperCamelCase      :Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]					,	__UpperCamelCase      :CLIPImageProcessor					,	):
												super().__init__()
												self.register_modules(
												    vae=__UpperCamelCase					,	text_encoder=__UpperCamelCase					,	clip_model=__UpperCamelCase					,	tokenizer=__UpperCamelCase					,	unet=__UpperCamelCase					,	scheduler=__UpperCamelCase					,	feature_extractor=__UpperCamelCase					,	)

												A		=  transforms.Normalize(mean=feature_extractor.image_mean					,	std=feature_extractor.image_std		)
												A		=  (
												    feature_extractor.size
												    if isinstance(feature_extractor.size					,	__UpperCamelCase		)
												    else feature_extractor.size["shortest_edge"]
												)
												A		=  MakeCutouts(self.cut_out_size		)

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


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


					def      lowerCamelCase	( self      :Optional[Any]		):
												self.enable_attention_slicing(__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												set_requires_grad(self.vae					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[int]		):
												set_requires_grad(self.vae					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												set_requires_grad(self.unet					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :str		):
												set_requires_grad(self.unet					,	__UpperCamelCase		)


					@torch.enable_grad()
					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :List[str]=True					,	):
												A		=  latents.detach().requires_grad_()

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

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

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

																			A		=  torch.sqrt(__UpperCamelCase		)
																			A		=  pred_original_sample * (fac) + latents * (1 - fac)
												elif isinstance(self.scheduler					,	__UpperCamelCase		):
																			A		=  self.scheduler.sigmas[index]
																			A		=  latents - sigma * noise_pred
												else:
																			raise ValueError(f"scheduler type {type(self.scheduler		)} not supported"		)

												A		=  1 / self.vae.config.scaling_factor * sample
												A		=  self.vae.decode(__UpperCamelCase		).sample
												A		=  (image / 2 + 0.5).clamp(0					,	1		)

												if use_cutouts:
																			A		=  self.make_cutouts(__UpperCamelCase					,	__UpperCamelCase		)
												else:
																			A		=  transforms.Resize(self.cut_out_size		)(__UpperCamelCase		)
												A		=  self.normalize(__UpperCamelCase		).to(latents.dtype		)

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

												if use_cutouts:
																			A		=  spherical_dist_loss(__UpperCamelCase					,	__UpperCamelCase		)
																			A		=  dists.view([num_cutouts, sample.shape[0], -1]		)
																			A		=  dists.sum(2		).mean(0		).sum() * clip_guidance_scale
												else:
																			A		=  spherical_dist_loss(__UpperCamelCase					,	__UpperCamelCase		).mean() * clip_guidance_scale

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

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


					@torch.no_grad()
					def __call__( self      :Any					,	__UpperCamelCase      :Union[str, List[str]]					,	__UpperCamelCase      :Optional[int] = 5_12					,	__UpperCamelCase      :Optional[int] = 5_12					,	__UpperCamelCase      :Optional[int] = 50					,	__UpperCamelCase      :Optional[float] = 7.5					,	__UpperCamelCase      :Optional[int] = 1					,	__UpperCamelCase      :float = 0.0					,	__UpperCamelCase      :Optional[float] = 1_00					,	__UpperCamelCase      :Optional[Union[str, List[str]]] = None					,	__UpperCamelCase      :Optional[int] = 4					,	__UpperCamelCase      :Optional[bool] = True					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :Optional[torch.FloatTensor] = None					,	__UpperCamelCase      :Optional[str] = "pil"					,	__UpperCamelCase      :bool = True					,	):
												if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																			A		=  1
												elif isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																			A		=  len(__UpperCamelCase		)
												else:
																			raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase		)}"		)

												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
												A		=  self.tokenizer(
												    __UpperCamelCase					,	padding="max_length"					,	max_length=self.tokenizer.model_max_length					,	truncation=__UpperCamelCase					,	return_tensors="pt"					,	)
												A		=  self.text_encoder(text_input.input_ids.to(self.device		)		)[0]
												# duplicate text embeddings for each generation per prompt
												A		=  text_embeddings.repeat_interleave(__UpperCamelCase					,	dim=0		)

												if clip_guidance_scale > 0:
																			if clip_prompt is not None:
																										A		=  self.tokenizer(
																										    __UpperCamelCase					,	padding="max_length"					,	max_length=self.tokenizer.model_max_length					,	truncation=__UpperCamelCase					,	return_tensors="pt"					,	).input_ids.to(self.device		)
																			else:
																										A		=  text_input.input_ids.to(self.device		)
																			A		=  self.clip_model.get_text_features(__UpperCamelCase		)
																			A		=  text_embeddings_clip / text_embeddings_clip.norm(p=2					,	dim=-1					,	keepdim=__UpperCamelCase		)
																			# duplicate text embeddings clip for each generation per prompt
																			A		=  text_embeddings_clip.repeat_interleave(__UpperCamelCase					,	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.
												A		=  guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																			A		=  text_input.input_ids.shape[-1]
																			A		=  self.tokenizer([""]					,	padding="max_length"					,	max_length=__UpperCamelCase					,	return_tensors="pt"		)
																			A		=  self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
																			# duplicate unconditional embeddings for each generation per prompt
																			A		=  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
																			A		=  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`.
												A		=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												A		=  text_embeddings.dtype
												if latents is None:
																			if self.device.type == "mps":
																										# randn does not work reproducibly on mps
																										A		=  torch.randn(__UpperCamelCase					,	generator=__UpperCamelCase					,	device="cpu"					,	dtype=__UpperCamelCase		).to(
																										    self.device		)
																			else:
																										A		=  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}"		)
																			A		=  latents.to(self.device		)

												# set timesteps
												A		=  "offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
												A		=  {}
												if accepts_offset:
																			A		=  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
												A		=  self.scheduler.timesteps.to(self.device		)

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

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

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

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

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

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

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

												# scale and decode the image latents with vae
												A		=  1 / self.vae.config.scaling_factor * latents
												A		=  self.vae.decode(__UpperCamelCase		).sample

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

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

												if not return_dict:
																			return (image, None)

												return StableDiffusionPipelineOutput(images=__UpperCamelCase					,	nsfw_content_detected=__UpperCamelCase		)









"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


