"""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							):




					"""simple docstring"""

					def __init__(							self  :      Optional[int]       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      List[str]=1.0     ):
									super().__init__()

									UpperCAmelCase__			 =				cut_size
									UpperCAmelCase__			 =				cut_power







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






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



def     a_		(   lowerCamelCase   ,     lowerCamelCase			):

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







class 							snake_case			(					A_							):




					"""simple docstring"""

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

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

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







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







					def 						__lowerCAmelCase		(							self  :      Any     ):
									self.enable_attention_slicing(_A     )







					def 						__lowerCAmelCase		(							self  :      str     ):
									set_requires_grad(self.vae       ,_A     )







					def 						__lowerCAmelCase		(							self  :      Tuple     ):
									set_requires_grad(self.vae       ,_A     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									set_requires_grad(self.unet       ,_A     )







					def 						__lowerCAmelCase		(							self  :      Union[str, Any]     ):
									set_requires_grad(self.unet       ,_A     )







					@torch.enable_grad()
					def 						__lowerCAmelCase		(							self  :      Tuple       ,lowerCamelCase__  :      Optional[Any]       ,lowerCamelCase__  :      List[Any]       ,lowerCamelCase__  :      Union[str, Any]       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      Tuple       ,lowerCamelCase__  :      List[str]       ,lowerCamelCase__  :      List[str]       ,lowerCamelCase__  :      List[str]       ,lowerCamelCase__  :      int=True       ,):
									UpperCAmelCase__			 =				latents.detach().requires_grad_()

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

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

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

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

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

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

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

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

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

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







					@torch.no_grad()
					def __call__(							self  :      Any       ,lowerCamelCase__  :      Union[str, List[str]]       ,lowerCamelCase__  :      Optional[int] = 512       ,lowerCamelCase__  :      Optional[int] = 512       ,lowerCamelCase__  :      Optional[int] = 50       ,lowerCamelCase__  :      Optional[float] = 7.5       ,lowerCamelCase__  :      Optional[int] = 1       ,lowerCamelCase__  :      float = 0.0       ,lowerCamelCase__  :      Optional[float] = 100       ,lowerCamelCase__  :      Optional[Union[str, List[str]]] = None       ,lowerCamelCase__  :      Optional[int] = 4       ,lowerCamelCase__  :      Optional[bool] = True       ,lowerCamelCase__  :      Optional[torch.Generator] = None       ,lowerCamelCase__  :      Optional[torch.FloatTensor] = None       ,lowerCamelCase__  :      Optional[str] = "pil"       ,lowerCamelCase__  :      bool = True       ,):
									if isinstance(_A       ,_A     ):
													UpperCAmelCase__			 =				1
									elif isinstance(_A       ,_A     ):
													UpperCAmelCase__			 =				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
									UpperCAmelCase__			 =				self.tokenizer(
									    _A       ,padding='max_length'       ,max_length=self.tokenizer.model_max_length       ,truncation=_A       ,return_tensors='pt'       ,)
									UpperCAmelCase__			 =				self.text_encoder(text_input.input_ids.to(self.device     )     )[0]
									# duplicate text embeddings for each generation per prompt
									UpperCAmelCase__			 =				text_embeddings.repeat_interleave(_A       ,dim=0     )

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

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

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

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

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

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

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

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

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

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

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

									if output_type == "pil":
													UpperCAmelCase__			 =				self.numpy_to_pil(_A     )

									if not return_dict:
													return (image, None)

									return StableDiffusionPipelineOutput(images=_A       ,nsfw_content_detected=_A     )





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A       =					{
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					["SplinterTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A       =					[
       "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SplinterForQuestionAnswering",
       "SplinterForPreTraining",
       "SplinterLayer",
       "SplinterModel",
       "SplinterPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_splinter_fast import SplinterTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_splinter import (
          SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SplinterForPreTraining,
          SplinterForQuestionAnswering,
          SplinterLayer,
          SplinterModel,
          SplinterPreTrainedModel,
      )


else:
   import sys

   _A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

