
'''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       lowerCAmelCase			(					nn.Module      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=1.0   ):
								super().__init__()

								_SCREAMING_SNAKE_CASE					=			cut_size
								_SCREAMING_SNAKE_CASE					=			cut_power




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			pixel_values.shape[2:4]
								_SCREAMING_SNAKE_CASE					=			min(__A       ,    __A   )
								_SCREAMING_SNAKE_CASE					=			min(__A       ,    __A       ,    self.cut_size   )
								_SCREAMING_SNAKE_CASE					=			[]
								for _ in range(__A   ):
														_SCREAMING_SNAKE_CASE					=			int(torch.rand([]   ) ** self.cut_power * (max_size - min_size) + min_size   )
														_SCREAMING_SNAKE_CASE					=			torch.randint(0       ,    sideX - size + 1       ,    ()   )
														_SCREAMING_SNAKE_CASE					=			torch.randint(0       ,    sideY - size + 1       ,    ()   )
														_SCREAMING_SNAKE_CASE					=			pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
														cutouts.append(F.adaptive_avg_poolad(__A       ,    self.cut_size   )   )
								return torch.cat(__A   )



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Union[str, Any]	):
						_SCREAMING_SNAKE_CASE					=			F.normalize(lowercase__						,      dim=-1	)
						_SCREAMING_SNAKE_CASE					=			F.normalize(lowercase__						,      dim=-1	)
						return (x - y).norm(dim=-1	).div(2	).arcsin().pow(2	).mul(2	)



def       _a		(						_SCREAMING_SNAKE_CASE		: int						,      _SCREAMING_SNAKE_CASE		: Optional[int]	):





						for param in model.parameters():
												_SCREAMING_SNAKE_CASE					=			value




class       lowerCAmelCase			(					A__      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    ):
								super().__init__()
								self.register_modules(
								    vae=__A       ,    text_encoder=__A       ,    clip_model=__A       ,    tokenizer=__A       ,    unet=__A       ,    scheduler=__A       ,    feature_extractor=__A       ,    )

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

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




		def      lowercase							(					self       ,    UpperCamelCase = "auto"   ):
								if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														_SCREAMING_SNAKE_CASE					=			self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__A   )




		def      lowercase							(					self   ):
								self.enable_attention_slicing(__A   )




		def      lowercase							(					self   ):
								set_requires_grad(self.vae       ,    __A   )




		def      lowercase							(					self   ):
								set_requires_grad(self.vae       ,    __A   )




		def      lowercase							(					self   ):
								set_requires_grad(self.unet       ,    __A   )




		def      lowercase							(					self   ):
								set_requires_grad(self.unet       ,    __A   )




		@torch.enable_grad()
		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=True       ,    ):
								_SCREAMING_SNAKE_CASE					=			latents.detach().requires_grad_()

								_SCREAMING_SNAKE_CASE					=			self.scheduler.scale_model_input(__A       ,    __A   )

								# predict the noise residual
								_SCREAMING_SNAKE_CASE					=			self.unet(__A       ,    __A       ,    encoder_hidden_states=__A   ).sample

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

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

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

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

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

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

								_SCREAMING_SNAKE_CASE					=			-torch.autograd.grad(__A       ,    __A   )[0]

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




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase       ,    UpperCamelCase = 512       ,    UpperCamelCase = 512       ,    UpperCamelCase = 50       ,    UpperCamelCase = 7.5       ,    UpperCamelCase = 1       ,    UpperCamelCase = 0.0       ,    UpperCamelCase = 100       ,    UpperCamelCase = None       ,    UpperCamelCase = 4       ,    UpperCamelCase = True       ,    UpperCamelCase = None       ,    UpperCamelCase = None       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								if isinstance(__A       ,    __A   ):
														_SCREAMING_SNAKE_CASE					=			1
								elif isinstance(__A       ,    __A   ):
														_SCREAMING_SNAKE_CASE					=			len(__A   )
								else:
														raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__A   )}'   )

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

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

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

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

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

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

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

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

														# predict the noise residual
														_SCREAMING_SNAKE_CASE					=			self.unet(__A       ,    __A       ,    encoder_hidden_states=__A   ).sample

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

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

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

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

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

								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(__A   )

								if not return_dict:
														return (image, None)

								return StableDiffusionPipelineOutput(images=__A       ,    nsfw_content_detected=__A   )

"""simple docstring"""






from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Union[str, Any]												=		"MarkupLMFeatureExtractor"
			UpperCAmelCase_    :Optional[Any]												=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
			UpperCAmelCase_    :List[Any]												=		True






			def __call__(   self			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A=None			,  __A = True			,  __A = False			,  __A = None			,  __A = None			,  __A = 0			,  __A = None			,  __A = None			,  __A = None			,  __A = False			,  __A = False			,  __A = False			,  __A = False			,  __A = True			,  __A = None			,  **__A			,  )     ->	BatchEncoding:
						# first, create nodes and xpaths
						if self.parse_html:
									if html_strings is None:
												raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""			)

									if nodes is not None or xpaths is not None or node_labels is not None:
												raise ValueError(
												    """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""			)

									lowerCAmelCase_       :Union[str, Any]     	=    self.feature_extractor(__A			)
									lowerCAmelCase_       :Any     	=    features["""nodes"""]
									lowerCAmelCase_       :Union[str, Any]     	=    features["""xpaths"""]
						else:
									if html_strings is not None:
												raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""			)
									if nodes is None or xpaths is None:
												raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""			)

        # # second, apply the tokenizer
						if questions is not None and self.parse_html:
									if isinstance(__A			,  __A			):
												lowerCAmelCase_       :Dict     	=    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

						lowerCAmelCase_       :Optional[Any]     	=    self.tokenizer(
						    text=questions if questions is not None else nodes			,  text_pair=nodes if questions is not None else None			,  xpaths=__A			,  node_labels=__A			,  add_special_tokens=__A			,  padding=__A			,  truncation=__A			,  max_length=__A			,  stride=__A			,  pad_to_multiple_of=__A			,  return_token_type_ids=__A			,  return_attention_mask=__A			,  return_overflowing_tokens=__A			,  return_special_tokens_mask=__A			,  return_offsets_mapping=__A			,  return_length=__A			,  verbose=__A			,  return_tensors=__A			,  **__A			,  )

						return encoded_inputs






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	Tuple:
						return self.tokenizer.batch_decode(*__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	str:
						return self.tokenizer.decode(*__A			,  **__A			)






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Any:
						lowerCAmelCase_       :Any     	=    self.tokenizer.model_input_names
						return tokenizer_input_names






