



"""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_						,				a_						,				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_						,				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_						,				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__ = False	)			->      bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
				raise ValueError(
				    "Warning: upper bound of deterministic test is exceeded. "
				    "Pass allow_probable=True to allow probabilistic test. "
				    "A return value of True indicates a probable prime."	)
		# array bounds provided by analysis
		a_     :      Optional[Any] 						=     [
		    2_047,
		    1_373_653,
		    25_326_001,
		    3_215_031_751,
		    2_152_302_898_747,
		    3_474_749_660_383,
		    341_550_071_728_321,
		    1,
		    3_825_123_056_546_413_051,
		    1,
		    1,
		    318_665_857_834_031_151_167_461,
		    3_317_044_064_679_887_385_961_981,
		]

		a_     :      Optional[int] 						=     [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1	):
				if n < _p:
						# then we have our last prime to check
						a_     :      List[Any] 						=     primes[:idx]
						break
		a_						,				a_     :      Tuple 						=     n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				a_     :      str 						=     False
				for r in range(SCREAMING_SNAKE_CASE__	):
						a_     :      List[str] 						=     pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__	)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								a_     :      List[Any] 						=     True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True



def        lowerCAmelCase_						(							)			->      None:
		assert not miller_rabin(561	)
		assert miller_rabin(563	)
		# 2047

		assert not miller_rabin(838_201	)
		assert miller_rabin(838_207	)
		# 1_373_653

		assert not miller_rabin(17_316_001	)
		assert miller_rabin(17_316_017	)
		# 25_326_001

		assert not miller_rabin(3_078_386_641	)
		assert miller_rabin(3_078_386_653	)
		# 3_215_031_751

		assert not miller_rabin(1_713_045_574_801	)
		assert miller_rabin(1_713_045_574_819	)
		# 2_152_302_898_747

		assert not miller_rabin(2_779_799_728_307	)
		assert miller_rabin(2_779_799_728_327	)
		# 3_474_749_660_383

		assert not miller_rabin(113_850_023_909_441	)
		assert miller_rabin(113_850_023_909_527	)
		# 341_550_071_728_321

		assert not miller_rabin(1_275_041_018_848_804_351	)
		assert miller_rabin(1_275_041_018_848_804_391	)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(79_666_464_458_507_787_791_867	)
		assert miller_rabin(79_666_464_458_507_787_791_951	)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(552_840_677_446_647_897_660_333	)
		assert miller_rabin(552_840_677_446_647_897_660_359	)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()