



'''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


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






class 						a_		(     snake_case    ):


							def __init__(				self   :						Optional[Any]      ,						a_   :						AutoencoderKL      ,						a_   :						CLIPTextModel      ,						a_   :						CLIPTokenizer      ,						a_   :						UNetaDConditionModel      ,						a_   :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,						a_   :						StableDiffusionSafetyChecker      ,						a_   :						CLIPImageProcessor      ,						)			->					Optional[int]:
										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   :						Dict      ,						a_   :						Optional[Union[str, int]] = "auto"			)			->					Any:
										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													snake_case:    str      							=self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Any:
										self.enable_attention_slicing(a_			)




							@torch.no_grad()
							def __call__(				self   :						Dict      ,						a_   :						Union[str, List[str]]      ,						a_   :						int = 5_1_2      ,						a_   :						int = 5_1_2      ,						a_   :						int = 5_0      ,						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_   :						List[Any]      ,						)			->					Optional[int]:

										if isinstance(a_      ,						a_			):
													snake_case:    str      							=1
										elif isinstance(a_      ,						a_			):
													snake_case:    int      							=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
										snake_case:    Optional[Any]      							=self.tokenizer(
										    a_      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						return_tensors='pt'      ,						)
										snake_case:    Optional[Any]      							=text_inputs.input_ids

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

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

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

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

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													snake_case:    Union[str, Any]      							=uncond_embeddings.shape[1]
													snake_case:    str      							=uncond_embeddings.repeat(a_      ,						a_      ,						1			)
													snake_case:    str      							=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
													snake_case:    Optional[int]      							=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`.
										snake_case:    List[str]      							=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										snake_case:    Optional[int]      							=(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
										snake_case:    List[str]      							=text_embeddings.dtype
										if latents is None:
													if self.device.type == "mps":
																# randn does not exist on mps
																snake_case:    List[Any]      							=torch.randn(
																    a_      ,						generator=a_      ,						device='cpu'      ,						dtype=a_			).to(self.device			)
																snake_case:    int      							=torch.randn(a_      ,						generator=a_      ,						device='cpu'      ,						dtype=a_			).to(
																    self.device			)
													else:
																snake_case:    Dict      							=torch.randn(
																    a_      ,						generator=a_      ,						device=self.device      ,						dtype=a_			)
																snake_case:    List[str]      							=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}'''			)
													snake_case:    Optional[Any]      							=latents_reference.to(self.device			)
													snake_case:    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
										snake_case:    Dict      							=(latents_shape[3] - latents_shape_reference[3]) // 2
										snake_case:    Optional[int]      							=(latents_shape[2] - latents_shape_reference[2]) // 2
										snake_case:    Optional[int]      							=latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										snake_case:    Dict      							=latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										snake_case:    Union[str, Any]      							=0 if dx < 0 else dx
										snake_case:    List[Any]      							=0 if dy < 0 else dy
										snake_case:    Any      							=max(-dx      ,						0			)
										snake_case:    List[Any]      							=max(-dy      ,						0			)
										# import pdb
										# pdb.set_trace()
										snake_case:    Dict      							=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
										snake_case:    Any      							=self.scheduler.timesteps.to(self.device			)

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

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

													# predict the noise residual
													snake_case:    List[Any]      							=self.unet(a_      ,						a_      ,						encoder_hidden_states=a_			).sample

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

													# compute the previous noisy sample x_t -> x_t-1
													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_			)

										snake_case:    List[str]      							=1 / 0.1_8_2_1_5 * latents
										snake_case:    List[str]      							=self.vae.decode(a_			).sample

										snake_case:    str      							=(image / 2 + 0.5).clamp(0      ,						1			)

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

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

										if output_type == "pil":
													snake_case:    Union[str, Any]      							=self.numpy_to_pil(a_			)

										if not return_dict:
													return (image, has_nsfw_concept)

										return StableDiffusionPipelineOutput(images=a_      ,						nsfw_content_detected=a_			)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    int      							=0
			if start < end:
						snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
						snake_case:    Optional[Any]      							=a[end]
						snake_case:    List[Any]      							=a[pivot]
						snake_case:    int      							=temp

						snake_case							,  snake_case:    Optional[int]      							=_in_place_partition(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							__UpperCAmelCase				,							p - 1					)
						count += _in_place_quick_sort(__UpperCAmelCase				,							p + 1				,							__UpperCAmelCase					)
			return count







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=0
			snake_case:    Tuple      							=randint(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[Any]      							=a[end]
			snake_case:    List[str]      							=a[pivot]
			snake_case:    Dict      							=temp
			snake_case:    Union[str, Any]      							=start - 1
			for index in range(__UpperCAmelCase				,							__UpperCAmelCase					):
						count += 1
						if a[index] < a[end]:  # check if current val is less than pivot value
									snake_case:    Optional[Any]      							=new_pivot_index + 1
									snake_case:    Dict      							=a[new_pivot_index]
									snake_case:    Union[str, Any]      							=a[index]
									snake_case:    List[Any]      							=temp

			snake_case:    Union[str, Any]      							=a[new_pivot_index + 1]
			snake_case:    Tuple      							=a[end]
			snake_case:    Union[str, Any]      							=temp
			return new_pivot_index + 1, count


a							   =				TemporaryFile()
a							   =				100  # 1000 elements are to be sorted


a   ,      a							   =				0, 1  # mean and standard deviation
a							   =				np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
a							   =				np.load(outfile)
a							   =				len(M) - 1
a							   =				_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)







print(z)




