


"""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 snake_case					(     nn.Module):







		def __init__(    self					:				Any			,      a__					:				List[Any]			,      a__					:				Any=1.0						)				->  Dict:





						'''simple docstring'''







						super().__init__()

						_A         =	cut_size
						_A         =	cut_power







		def        a_       (    self					:				Optional[Any]			,      a__					:				Optional[Any]			,      a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







						_A     ,						_A         =	pixel_values.shape[2:4]
						_A         =	min(a__			,      a__						)
						_A         =	min(a__			,      a__			,      self.cut_size						)
						_A         =	[]
						for _ in range(a__						):
										_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(a__			,      self.cut_size						)						)
						return torch.cat(a__						)






def    a__       ( __lowercase   ,	__lowercase      )		->		int:
				_A         =	F.normalize(__lowercase   ,	dim=-1      )
				_A         =	F.normalize(__lowercase   ,	dim=-1      )
				return (x - y).norm(dim=-1      ).div(2      ).arcsin().pow(2      ).mul(2      )






def    a__       ( __lowercase   ,	__lowercase      )		->		List[Any]:
				for param in model.parameters():
								_A         =	value




class snake_case					(     _UpperCamelCase):







		def __init__(    self					:				str			,      a__					:				AutoencoderKL			,      a__					:				CLIPTextModel			,      a__					:				CLIPModel			,      a__					:				CLIPTokenizer			,      a__					:				UNetaDConditionModel			,      a__					:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]			,      a__					:				CLIPImageProcessor			,      )				->  Optional[int]:





						'''simple docstring'''







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

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

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







		def        a_       (    self					:				Union[str, Any]			,      a__					:				Optional[Union[str, int]] = "auto"						)				->  Dict:





						'''simple docstring'''







						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(a__						)







		def        a_       (    self					:				List[Any]						)				->  int:





						'''simple docstring'''







						self.enable_attention_slicing(a__						)







		def        a_       (    self					:				str						)				->  int:





						'''simple docstring'''







						set_requires_grad(self.vae			,      a__						)







		def        a_       (    self					:				Dict						)				->  Dict:





						'''simple docstring'''







						set_requires_grad(self.vae			,      a__						)







		def        a_       (    self					:				str						)				->  List[Any]:





						'''simple docstring'''







						set_requires_grad(self.unet			,      a__						)







		def        a_       (    self					:				Any						)				->  List[Any]:





						'''simple docstring'''







						set_requires_grad(self.unet			,      a__						)







		@torch.enable_grad()
		def        a_       (    self					:				Optional[int]			,      a__					:				List[Any]			,      a__					:				Optional[Any]			,      a__					:				Any			,      a__					:				Tuple			,      a__					:				Optional[int]			,      a__					:				List[str]			,      a__					:				List[Any]			,      a__					:				Union[str, Any]			,      a__					:				Optional[int]=True			,      )				->  Optional[Any]:





						'''simple docstring'''







						_A         =	latents.detach().requires_grad_()

						_A         =	self.scheduler.scale_model_input(a__			,      a__						)

						# predict the noise residual
						_A         =	self.unet(a__			,      a__			,      encoder_hidden_states=a__						).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(a__						)
										_A         =	pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler			,      a__						):
										_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(a__						).sample
						_A         =	(image / 2 + 0.5).clamp(0			,      1						)

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

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

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

						_A         =	-torch.autograd.grad(a__			,      a__						)[0]

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







		@torch.no_grad()
		def __call__(    self					:				Tuple			,      a__					:				Union[str, List[str]]			,      a__					:				Optional[int] = 5_12			,      a__					:				Optional[int] = 5_12			,      a__					:				Optional[int] = 50			,      a__					:				Optional[float] = 7.5			,      a__					:				Optional[int] = 1			,      a__					:				float = 0.0			,      a__					:				Optional[float] = 1_00			,      a__					:				Optional[Union[str, List[str]]] = None			,      a__					:				Optional[int] = 4			,      a__					:				Optional[bool] = True			,      a__					:				Optional[torch.Generator] = None			,      a__					:				Optional[torch.FloatTensor] = None			,      a__					:				Optional[str] = "pil"			,      a__					:				bool = True			,      )				->  List[Any]:





						'''simple docstring'''







						if isinstance(a__			,      a__						):
										_A         =	1
						elif isinstance(a__			,      a__						):
										_A         =	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
						_A         =	self.tokenizer(
						    a__			,      padding="max_length"			,      max_length=self.tokenizer.model_max_length			,      truncation=a__			,      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(a__			,      dim=0						)

						if clip_guidance_scale > 0:
										if clip_prompt is not None:
														_A         =	self.tokenizer(
														    a__			,      padding="max_length"			,      max_length=self.tokenizer.model_max_length			,      truncation=a__			,      return_tensors="pt"			,      ).input_ids.to(self.device						)
										else:
														_A         =	text_input.input_ids.to(self.device						)
										_A         =	self.clip_model.get_text_features(a__						)
										_A         =	text_embeddings_clip / text_embeddings_clip.norm(p=2			,      dim=-1			,      keepdim=a__						)
										# duplicate text embeddings clip for each generation per prompt
										_A         =	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.
						_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=a__			,      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(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
										_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(a__			,      generator=a__			,      device="cpu"			,      dtype=a__						).to(
														    self.device						)
										else:
														_A         =	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}"""						)
										_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(a__			,      **a__						)

						# 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(a__						)						):
										# 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(a__			,      a__						)

										# predict the noise residual
										_A         =	self.unet(a__			,      a__			,      encoder_hidden_states=a__						).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(
														    a__			,      a__			,      a__			,      a__			,      a__			,      a__			,      a__			,      a__			,      a__			,      )

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

						# scale and decode the image latents with vae
						_A         =	1 / self.vae.config.scaling_factor * latents
						_A         =	self.vae.decode(a__						).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(a__						)

						if not return_dict:
										return (image, None)

						return StableDiffusionPipelineOutput(images=a__			,      nsfw_content_detected=a__						)



"""simple docstring"""


import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


a_											=			get_logger(__name__)


if TYPE_CHECKING:
		from .builder import DatasetBuilder






def    a__       ( __lowercase   ,	__lowercase = None      )		->		Optional[Any]:
				_A         =	importlib.import_module(__lowercase      )

				# TODO(QL): always update the module to add subsequent new authentication
				if hasattr(__lowercase   ,	"_patched_for_streaming"      ) and module._patched_for_streaming:
								return

				def wrap_auth(__lowercase      ):
								@wraps(__lowercase      )
								def wrapper(*__lowercase   ,	**__lowercase      ):
												return function(*__lowercase   ,	use_auth_token=__lowercase   ,	**__lowercase      )

								_A         =	"wrap_auth"
								return wrapper

				# open files in a streaming fashion
				patch_submodule(__lowercase   ,	"open"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"os.listdir"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"os.walk"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"glob.glob"   ,	wrap_auth(__lowercase      )      ).start()
				# allow to navigate in remote zip files
				patch_submodule(__lowercase   ,	"os.path.join"   ,	__lowercase      ).start()
				patch_submodule(__lowercase   ,	"os.path.dirname"   ,	__lowercase      ).start()
				patch_submodule(__lowercase   ,	"os.path.basename"   ,	__lowercase      ).start()
				patch_submodule(__lowercase   ,	"os.path.relpath"   ,	__lowercase      ).start()
				patch_submodule(__lowercase   ,	"os.path.split"   ,	__lowercase      ).start()
				patch_submodule(__lowercase   ,	"os.path.splitext"   ,	__lowercase      ).start()
				# allow checks on paths
				patch_submodule(__lowercase   ,	"os.path.exists"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"os.path.isdir"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"os.path.isfile"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"os.path.getsize"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"pathlib.Path"   ,	__lowercase      ).start()
				# file readers
				patch_submodule(__lowercase   ,	"gzip.open"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"numpy.load"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"pandas.read_csv"   ,	wrap_auth(__lowercase      )   ,	attrs=["__version__"]      ).start()
				patch_submodule(__lowercase   ,	"pandas.read_excel"   ,	wrap_auth(__lowercase      )   ,	attrs=["__version__"]      ).start()
				patch_submodule(__lowercase   ,	"scipy.io.loadmat"   ,	wrap_auth(__lowercase      )   ,	attrs=["__version__"]      ).start()
				patch_submodule(__lowercase   ,	"xml.etree.ElementTree.parse"   ,	wrap_auth(__lowercase      )      ).start()
				patch_submodule(__lowercase   ,	"xml.dom.minidom.parse"   ,	wrap_auth(__lowercase      )      ).start()
				_A         =	True






def    a__       ( __lowercase      )		->		str:
				extend_module_for_streaming(builder.__module__   ,	use_auth_token=builder.use_auth_token      )
				# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
				if not builder.__module__.startswith("datasets."      ):  # check that it's not a packaged builder like csv
								for imports in get_imports(inspect.getfile(builder.__class__      )      ):
												if imports[0] == "internal":
																_A         =	imports[1]
																_A         =	".".join(builder.__module__.split("."      )[:-1] + [internal_import_name]      )
																extend_module_for_streaming(__lowercase   ,	use_auth_token=builder.use_auth_token      )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
				from .builder import DatasetBuilder

				_A         =	[
				    cls.__module__
				    for cls in type(__lowercase      ).__mro__[1:]  # make sure it's not the same module we've already patched
				    if issubclass(cls   ,	__lowercase      ) and cls.__module__ != DatasetBuilder.__module__
				]  # check it's not a standard builder from datasets.builder
				for module in parent_builder_modules:
								extend_module_for_streaming(__lowercase   ,	use_auth_token=builder.use_auth_token      )