



"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


SCREAMING_SNAKE_CASE_				= logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			snake_case_						(						a_				):






							def __init__(   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_			,     ):
								super().__init__()
								self.register_modules(
								    vae=a_			,     text_encoder=a_			,     tokenizer=a_			,     unet=a_			,     scheduler=a_			,     safety_checker=a_			,     feature_extractor=a_			,     )






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






							def snake_case_  (   self      ):
								self.enable_attention_slicing(a_      )






							@torch.no_grad()
							def __call__(   self			,     a_			,     a_ = 5_1_2			,     a_ = 5_1_2			,     a_ = 5_0			,     a_ = 7.5			,     a_ = None			,     a_ = 1			,     a_ = 0.0			,     a_ = None			,     a_ = None			,     a_ = "pil"			,     a_ = True			,     a_ = None			,     a_ = 1			,     a_ = None			,     **a_			,     ):

								if isinstance(a_			,     a_      ):
									a_     :      Union[str, Any] 						=     1
								elif isinstance(a_			,     a_      ):
									a_     :      Any 						=     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}."""      )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(a_			,     a_      ) or callback_steps <= 0)
								):
									raise ValueError(
									    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
									    F""" {type(a_      )}."""      )

								# get prompt text embeddings
								a_     :      Optional[Any] 						=     self.tokenizer(
								    a_			,     padding="max_length"			,     max_length=self.tokenizer.model_max_length			,     return_tensors="pt"			,     )
								a_     :      Union[str, Any] 						=     text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									a_     :      Optional[Any] 						=     self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]      )
									logger.warning(
									    "The following part of your input was truncated because CLIP can only handle sequences up to"
									    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""      )
									a_     :      List[Any] 						=     text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
									a_     :      str 						=     self.text_encoder(text_input_ids.to(self.device      )      )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								a_     :      List[str] 						=     text_embeddings.shape
								a_     :      int 						=     text_embeddings.repeat(1			,     a_			,     1      )
								a_     :      List[str] 						=     text_embeddings.view(bs_embed * num_images_per_prompt			,     a_			,     -1      )

								# 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_     :      Dict 						=     guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									a_     :      List[str]
									if negative_prompt is None:
										a_     :      List[str] 						=     [""]
									elif type(a_      ) is not type(a_      ):
										raise TypeError(
										    F"""`negative_prompt` should be the same type to `prompt`, but got {type(a_      )} !="""
										    F""" {type(a_      )}."""      )
									elif isinstance(a_			,     a_      ):
										a_     :      Any 						=     [negative_prompt]
									elif batch_size != len(a_      ):
										raise ValueError(
										    F"""`negative_prompt`: {negative_prompt} has batch size {len(a_      )}, but `prompt`:"""
										    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
										    " the batch size of `prompt`."      )
									else:
										a_     :      Any 						=     negative_prompt

									a_     :      Any 						=     text_input_ids.shape[-1]
									a_     :      Tuple 						=     self.tokenizer(
									    a_			,     padding="max_length"			,     max_length=a_			,     truncation=a_			,     return_tensors="pt"			,     )
									a_     :      Union[str, Any] 						=     self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									a_     :      int 						=     uncond_embeddings.shape[1]
									a_     :      List[str] 						=     uncond_embeddings.repeat(a_			,     a_			,     1      )
									a_     :      Tuple 						=     uncond_embeddings.view(batch_size * num_images_per_prompt			,     a_			,     -1      )

									# 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_     :      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`.
								a_     :      Optional[Any] 						=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								a_     :      Optional[int] 						=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
								a_     :      Tuple 						=     text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not exist on mps
										a_     :      Any 						=     torch.randn(
										    a_			,     generator=a_			,     device="cpu"			,     dtype=a_      ).to(self.device      )
										a_     :      Optional[int] 						=     torch.randn(a_			,     generator=a_			,     device="cpu"			,     dtype=a_      ).to(
										    self.device      )
									else:
										a_     :      Optional[Any] 						=     torch.randn(
										    a_			,     generator=a_			,     device=self.device			,     dtype=a_      )
										a_     :      Dict 						=     torch.randn(a_			,     generator=a_			,     device=self.device			,     dtype=a_      )
								else:
									if latents_reference.shape != latents_shape:
										raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""      )
									a_     :      Tuple 						=     latents_reference.to(self.device      )
									a_     :      Dict 						=     latents.to(self.device      )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								a_     :      List[Any] 						=     (latents_shape[3] - latents_shape_reference[3]) // 2
								a_     :      Any 						=     (latents_shape[2] - latents_shape_reference[2]) // 2
								a_     :      Union[str, Any] 						=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								a_     :      Optional[int] 						=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								a_     :      Dict 						=     0 if dx < 0 else dx
								a_     :      Tuple 						=     0 if dy < 0 else dy
								a_     :      Optional[Any] 						=     max(-dx			,     0      )
								a_     :      List[str] 						=     max(-dy			,     0      )
								# import pdb
								# pdb.set_trace()
								a_     :      Tuple 						=     latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(a_      )

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

								# scale the initial noise by the standard deviation required by the scheduler
								a_     :      Union[str, Any] 						=     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_     :      Any 						=     "eta" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
								a_     :      List[Any] 						=     {}
								if accepts_eta:
									a_     :      int 						=     eta

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

									# predict the noise residual
									a_     :      int 						=     self.unet(a_			,     a_			,     encoder_hidden_states=a_      ).sample

									# perform guidance
									if do_classifier_free_guidance:
										a_     :      Any 						=     noise_pred.chunk(2      )
										a_     :      Tuple 						=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
										callback(a_			,     a_			,     a_      )

								a_     :      Dict 						=     1 / 0.18_215 * latents
								a_     :      int 						=     self.vae.decode(a_      ).sample

								a_     :      Tuple 						=     (image / 2 + 0.5).clamp(0			,     1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a_     :      Union[str, Any] 						=     image.cpu().permute(0			,     2			,     3			,     1      ).float().numpy()

								if self.safety_checker is not None:
									a_     :      Optional[int] 						=     self.feature_extractor(self.numpy_to_pil(a_      )			,     return_tensors="pt"      ).to(
									    self.device      )
									a_     :      Optional[int] 						=     self.safety_checker(
									    images=a_			,     clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
								else:
									a_     :      Optional[int] 						=     None

								if output_type == "pil":
									a_     :      List[str] 						=     self.numpy_to_pil(a_      )

								if not return_dict:
									return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=a_			,     nsfw_content_detected=a_      )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()