






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


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


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
					'''simple docstring'''


					def __init__(				self       :	Optional[Any]					, UpperCAmelCase_       :	AutoencoderKL					, UpperCAmelCase_       :	CLIPTextModel					, UpperCAmelCase_       :	CLIPTokenizer					, UpperCAmelCase_       :	UNetaDConditionModel					, UpperCAmelCase_       :	Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					, UpperCAmelCase_       :	StableDiffusionSafetyChecker					, UpperCAmelCase_       :	CLIPImageProcessor					, ):
							super().__init__()
							self.register_modules(
							    vae=__A					, text_encoder=__A					, tokenizer=__A					, unet=__A					, scheduler=__A					, safety_checker=__A					, feature_extractor=__A					, )


					def 				_A       (				self       :	List[str]					, 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
									SCREAMING_SNAKE_CASE       :      str							=    self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(__A      )


					def 				_A       (				self       :	List[str]      ):
							self.enable_attention_slicing(__A      )






					@torch.no_grad()
					def __call__(				self       :	Dict					, UpperCAmelCase_       :	Union[str, List[str]]					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 50					, UpperCAmelCase_       :	float = 7.5					, UpperCAmelCase_       :	Optional[Union[str, List[str]]] = None					, UpperCAmelCase_       :	Optional[int] = 1					, UpperCAmelCase_       :	float = 0.0					, UpperCAmelCase_       :	Optional[torch.Generator] = None					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, UpperCAmelCase_       :	Optional[str] = "pil"					, UpperCAmelCase_       :	bool = True					, UpperCAmelCase_       :	Optional[Callable[[int, int, torch.FloatTensor], None]] = None					, UpperCAmelCase_       :	int = 1					, UpperCAmelCase_       :	Optional[torch.FloatTensor] = None					, **UpperCAmelCase_       :	List[str]					, ):

							if isinstance(__A					, __A      ):
									SCREAMING_SNAKE_CASE       :      str							=    1
							elif isinstance(__A					, __A      ):
									SCREAMING_SNAKE_CASE       :      Tuple							=    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
							SCREAMING_SNAKE_CASE       :      List[str]							=    self.tokenizer(
							    __A					, padding="max_length"					, max_length=self.tokenizer.model_max_length					, return_tensors="pt"					, )
							SCREAMING_SNAKE_CASE       :      Tuple							=    text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									SCREAMING_SNAKE_CASE       :      Tuple							=    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}'''      )
									SCREAMING_SNAKE_CASE       :      Any							=    text_input_ids[:, : self.tokenizer.model_max_length]

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

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    text_embeddings.shape
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    text_embeddings.repeat(1					, __A					, 1      )
							SCREAMING_SNAKE_CASE       :      Any							=    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.
							SCREAMING_SNAKE_CASE       :      List[str]							=    guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE       :      Optional[int]							=    42
									if negative_prompt is None:
											SCREAMING_SNAKE_CASE       :      Optional[int]							=    [""]
									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      ):
											SCREAMING_SNAKE_CASE       :      List[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:
											SCREAMING_SNAKE_CASE       :      Dict							=    negative_prompt

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

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									SCREAMING_SNAKE_CASE       :      Optional[int]							=    uncond_embeddings.shape[1]
									SCREAMING_SNAKE_CASE       :      Dict							=    uncond_embeddings.repeat(__A					, __A					, 1      )
									SCREAMING_SNAKE_CASE       :      List[Any]							=    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
									SCREAMING_SNAKE_CASE       :      Dict							=    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       :      List[str]							=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							SCREAMING_SNAKE_CASE       :      List[str]							=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							SCREAMING_SNAKE_CASE       :      List[Any]							=    text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not exist on mps
											SCREAMING_SNAKE_CASE       :      Any							=    torch.randn(
											    __A					, generator=__A					, device="cpu"					, dtype=__A      ).to(self.device      )
											SCREAMING_SNAKE_CASE       :      int							=    torch.randn(__A					, generator=__A					, device="cpu"					, dtype=__A      ).to(
											    self.device      )
									else:
											SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.randn(
											    __A					, generator=__A					, device=self.device					, dtype=__A      )
											SCREAMING_SNAKE_CASE       :      int							=    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}'''      )
									SCREAMING_SNAKE_CASE       :      str							=    latents_reference.to(self.device      )
									SCREAMING_SNAKE_CASE       :      List[Any]							=    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
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (latents_shape[3] - latents_shape_reference[3]) // 2
							SCREAMING_SNAKE_CASE       :      int							=    (latents_shape[2] - latents_shape_reference[2]) // 2
							SCREAMING_SNAKE_CASE       :      str							=    latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							SCREAMING_SNAKE_CASE       :      Dict							=    latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    0 if dx < 0 else dx
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    0 if dy < 0 else dy
							SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    max(-dx					, 0      )
							SCREAMING_SNAKE_CASE       :      Any							=    max(-dy					, 0      )
							# import pdb
							# pdb.set_trace()
							SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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
							SCREAMING_SNAKE_CASE       :      str							=    self.scheduler.timesteps.to(self.device      )

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

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

									# predict the noise residual
									SCREAMING_SNAKE_CASE       :      Any							=    self.unet(__A					, __A					, encoder_hidden_states=__A      ).sample

									# perform guidance
									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    noise_pred.chunk(2      )
											SCREAMING_SNAKE_CASE       :      List[Any]							=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									SCREAMING_SNAKE_CASE       :      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      )

							SCREAMING_SNAKE_CASE       :      List[Any]							=    1 / 0.18_215 * latents
							SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.vae.decode(__A      ).sample

							SCREAMING_SNAKE_CASE       :      Dict							=    (image / 2 + 0.5).clamp(0					, 1      )

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

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

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

							if not return_dict:
									return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=__A					, nsfw_content_detected=__A      )














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






