
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				:Optional[int]									=							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





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






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					Optional[Union[str, int]] = "auto"					):
					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 							UpperCamelCase_    (			self   :					Tuple					):
					self.enable_attention_slicing(A					)







	@torch.no_grad()
	def __call__(			self   :					Dict				,A   :					Union[str, List[str]]				,A   :					int = 5_12				,A   :					int = 5_12				,A   :					int = 50				,A   :					float = 7.5				,A   :					Optional[Union[str, List[str]]] = None				,A   :					Optional[int] = 1				,A   :					float = 0.0				,A   :					Optional[torch.Generator] = None				,A   :					Optional[torch.FloatTensor] = None				,A   :					Optional[str] = "pil"				,A   :					bool = True				,A   :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None				,A   :					int = 1				,A   :					Optional[torch.FloatTensor] = None				,**A   :					Optional[Any]				,):

					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}.'''					)

					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       =					self.tokenizer(
					    A				,padding="max_length"				,max_length=self.tokenizer.model_max_length				,return_tensors="pt"				,)
					__A       =					text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									__A       =					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       =					text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
									__A       =					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       =					text_embeddings.shape
					__A       =					text_embeddings.repeat(1				,A				,1					)
					__A       =					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       =					guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
									__A       =					42
									if negative_prompt is None:
													__A       =					[""]
									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       =					[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       =					negative_prompt

									__A       =					text_input_ids.shape[-1]
									__A       =					self.tokenizer(
									    A				,padding="max_length"				,max_length=A				,truncation=A				,return_tensors="pt"				,)
									__A       =					self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

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

					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 guidance
									if do_classifier_free_guidance:
													__A     ,				__A       =					noise_pred.chunk(2					)
													__A       =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									__A       =					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       =					1 / 0.1_82_15 * latents
					__A       =					self.vae.decode(A					).sample

					__A       =					(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       =					image.cpu().permute(0				,2				,3				,1					).float().numpy()

					if self.safety_checker is not None:
									__A       =					self.feature_extractor(self.numpy_to_pil(A					)				,return_tensors="pt"					).to(
									    self.device					)
									__A     ,				__A       =					self.safety_checker(
									    images=A				,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype					)					)
					else:
									__A       =					None

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

					if not return_dict:
									return (image, has_nsfw_concept)

					return StableDiffusionPipelineOutput(images=A				,nsfw_content_detected=A					)







def   __snake_case    (       _lowerCAmelCase			:			int = 1					,					_lowerCAmelCase			:			int = 1000						)			->    int:
   A_       :					Optional[int]        =      1
   A_       :					int        =      0

   for divide_by_number in range(_lowerCAmelCase					,					digit + 1						):
      A_       :					list[int]        =      []
      A_       :					Union[str, Any]        =      numerator
      for _ in range(1					,					digit + 1						):
         if now_divide in has_been_divided:
            if longest_list_length < len(_lowerCAmelCase						):
               A_       :					Optional[Any]        =      len(_lowerCAmelCase						)
               A_       :					Union[str, Any]        =      divide_by_number
         else:
            has_been_divided.append(_lowerCAmelCase						)
            A_       :					Dict        =      now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()
