





from __future__ import annotations

from collections.abc import Iterator


class     __magic_name__     :





     """simple docstring"""



     def __init__(							self				:Optional[Any]	,					snake_case				:str     ):





        '''simple docstring'''




        A_    : Tuple								=	value
        A_    : Node | None								=	None
        A_    : Node | None								=	None




class     __magic_name__     :





     """simple docstring"""



     def __init__(							self				:Optional[int]	,					snake_case				:Union[str, Any]     ):





        '''simple docstring'''




        A_    : List[Any]								=	tree



     def   SCREAMING_SNAKE_CASE						(							self				:Any	,					snake_case				:Optional[int]     ):





        '''simple docstring'''




        if node is None:
           return 0
        return node.value + (
            self.depth_first_search(node.left     ) + self.depth_first_search(node.right     )
        )





     def __iter__(							self				:Tuple     ):





        '''simple docstring'''




        yield self.depth_first_search(self.tree     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()





'''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


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







class 	_lowerCAmelCase							(			__UpperCAmelCase ):


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


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


	def     _a	(self							):
				self.enable_attention_slicing(lowercase							)


	@torch.no_grad()
	def __call__(self       ,      lowercase       ,      lowercase = 512       ,      lowercase = 512       ,      lowercase = 50       ,      lowercase = 7.5       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = 0.0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = "pil"       ,      lowercase = True       ,      lowercase = None       ,      lowercase = 1       ,      lowercase = None       ,      **lowercase       ,      ):

				if isinstance(lowercase       ,      lowercase							):
							A_	:						Union[str, Any]	=     1
				elif isinstance(lowercase       ,      lowercase							):
							A_	:						Any	=     len(lowercase							)
				else:
							raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase							)}'							)

				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(lowercase       ,      lowercase							) or callback_steps <= 0)
				):
							raise ValueError(
							    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    F' {type(lowercase							)}.'							)

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

				if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A_	:						Union[str, 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_	:						Dict	=     text_input_ids[:, : self.tokenizer.model_max_length]

				if text_embeddings is None:
							A_	:						Any	=     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_	:						Tuple	=     text_embeddings.shape
				A_	:						Optional[Any]	=     text_embeddings.repeat(1       ,      lowercase       ,      1							)
				A_	:						Any	=     text_embeddings.view(bs_embed * num_images_per_prompt       ,      lowercase       ,      -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_	:						List[str]	=     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_	:						Optional[int]	=     [""""""]
							elif type(lowercase							) is not type(lowercase							):
										raise TypeError(
										    F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase							)} !='
										    F' {type(lowercase							)}.'							)
							elif isinstance(lowercase       ,      lowercase							):
										A_	:						Dict	=     [negative_prompt]
							elif batch_size != len(lowercase							):
										raise ValueError(
										    F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase							)}, but `prompt`:'
										    F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
										    """ the batch size of `prompt`."""							)
							else:
										A_	:						Dict	=     negative_prompt

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

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A_	:						Optional[Any]	=     uncond_embeddings.shape[1]
							A_	:						str	=     uncond_embeddings.repeat(lowercase       ,      lowercase       ,      1							)
							A_	:						List[str]	=     uncond_embeddings.view(batch_size * num_images_per_prompt       ,      lowercase       ,      -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_	:						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`.
				A_	:						int	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				A_	:						Dict	=     (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
				A_	:						Dict	=     text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not exist on mps
										A_	:						Tuple	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(self.device							)
										A_	:						int	=     torch.randn(lowercase       ,      generator=lowercase       ,      device="""cpu"""       ,      dtype=lowercase							).to(
										    self.device							)
							else:
										A_	:						int	=     torch.randn(
										    lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
										A_	:						str	=     torch.randn(lowercase       ,      generator=lowercase       ,      device=self.device       ,      dtype=lowercase							)
				else:
							if latents_reference.shape != latents_shape:
										raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
							A_	:						str	=     latents_reference.to(self.device							)
							A_	:						Tuple	=     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_	:						Optional[int]	=     (latents_shape[3] - latents_shape_reference[3]) // 2
				A_	:						Optional[int]	=     (latents_shape[2] - latents_shape_reference[2]) // 2
				A_	:						Optional[int]	=     latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
				A_	:						int	=     latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
				A_	:						Optional[Any]	=     0 if dx < 0 else dx
				A_	:						Optional[Any]	=     0 if dy < 0 else dy
				A_	:						Optional[int]	=     max(-dx       ,      0							)
				A_	:						List[str]	=     max(-dy       ,      0							)
				# import pdb
				# pdb.set_trace()
				A_	:						str	=     latents_reference[:, :, dy : dy + h, dx : dx + w]

				# set timesteps
				self.scheduler.set_timesteps(lowercase							)

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

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

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

							# predict the noise residual
							A_	:						List[str]	=     self.unet(lowercase       ,      lowercase       ,      encoder_hidden_states=lowercase							).sample

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

							# compute the previous noisy sample x_t -> x_t-1
							A_	:						List[str]	=     self.scheduler.step(lowercase       ,      lowercase       ,      lowercase       ,      **lowercase							).prev_sample

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

				A_	:						List[str]	=     1 / 0.1_82_15 * latents
				A_	:						List[str]	=     self.vae.decode(lowercase							).sample

				A_	:						Optional[int]	=     (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_	:						Optional[int]	=     image.cpu().permute(0       ,      2       ,      3       ,      1							).float().numpy()

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

				if output_type == "pil":
							A_	:						Tuple	=     self.numpy_to_pil(lowercase							)

				if not return_dict:
							return (image, has_nsfw_concept)

				return StableDiffusionPipelineOutput(images=lowercase       ,      nsfw_content_detected=lowercase							)