






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					List[Any]		)  ->   Tuple:

						"""simple docstring"""



						if isinstance(UpperCamelCase_    ,       torch.Tensor		):
												return image
						elif isinstance(UpperCamelCase_    ,       PIL.Image.Image		):
												lowerCAmelCase__    						=						[image]

						if isinstance(image[0]    ,       PIL.Image.Image		):
												lowerCAmelCase__    						=						[np.array(i.resize((w, h)    ,       resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
												lowerCAmelCase__    						=						np.concatenate(UpperCamelCase_    ,       axis=0		)
												lowerCAmelCase__    						=						np.array(UpperCamelCase_		).astype(np.floataa		) / 255.0
												lowerCAmelCase__    						=						image.transpose(0    ,       3    ,       1    ,       2		)
												lowerCAmelCase__    						=						2.0 * image - 1.0
												lowerCAmelCase__    						=						torch.from_numpy(UpperCamelCase_		)
						elif isinstance(image[0]    ,       torch.Tensor		):
												lowerCAmelCase__    						=						torch.cat(UpperCamelCase_    ,       dim=0		)
						return image







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Dict=0.9995		)  ->   Tuple:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_    ,       np.ndarray		):
												lowerCAmelCase__    						=						True
												lowerCAmelCase__    						=						va.device
												lowerCAmelCase__    						=						va.cpu().numpy()
												lowerCAmelCase__    						=						va.cpu().numpy()

						lowerCAmelCase__    						=						np.sum(va * va / (np.linalg.norm(UpperCamelCase_		) * np.linalg.norm(UpperCamelCase_		))		)
						if np.abs(UpperCamelCase_		) > DOT_THRESHOLD:
												lowerCAmelCase__    						=						(1 - t) * va + t * va
						else:
												lowerCAmelCase__    						=						np.arccos(UpperCamelCase_		)
												lowerCAmelCase__    						=						np.sin(UpperCamelCase_		)
												lowerCAmelCase__    						=						theta_a * t
												lowerCAmelCase__    						=						np.sin(UpperCamelCase_		)
												lowerCAmelCase__    						=						np.sin(theta_a - theta_t		) / sin_theta_a
												lowerCAmelCase__    						=						sin_theta_t / sin_theta_a
												lowerCAmelCase__    						=						sa * va + sa * va

						if inputs_are_torch:
												lowerCAmelCase__    						=						torch.from_numpy(UpperCamelCase_		).to(UpperCamelCase_		)

						return va







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						F.normalize(UpperCamelCase_    ,       dim=-1		)
						lowerCAmelCase__    						=						F.normalize(UpperCamelCase_    ,       dim=-1		)
						return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Union[str, Any]		)  ->   int:

						"""simple docstring"""



						for param in model.parameters():
												lowerCAmelCase__    						=						value


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						):






												"""simple docstring"""
												super().__init__()
												self.register_modules(
												    vae=_UpperCamelCase     ,						text_encoder=_UpperCamelCase     ,						clip_model=_UpperCamelCase     ,						tokenizer=_UpperCamelCase     ,						unet=_UpperCamelCase     ,						scheduler=_UpperCamelCase     ,						feature_extractor=_UpperCamelCase     ,						coca_model=_UpperCamelCase     ,						coca_tokenizer=_UpperCamelCase     ,						coca_transform=_UpperCamelCase     ,						)
												lowerCAmelCase__    						=						(
												    feature_extractor.size
												    if isinstance(feature_extractor.size     ,						_UpperCamelCase   )
												    else feature_extractor.size['shortest_edge']
												)
												lowerCAmelCase__    						=						transforms.Normalize(mean=feature_extractor.image_mean     ,						std=feature_extractor.image_std   )
												set_requires_grad(self.text_encoder     ,						_UpperCamelCase   )
												set_requires_grad(self.clip_model     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase = "auto"   ):






												"""simple docstring"""
												if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		lowerCAmelCase__    						=						self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.enable_attention_slicing(_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												set_requires_grad(self.vae     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												set_requires_grad(self.vae     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												set_requires_grad(self.unet     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												set_requires_grad(self.unet     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												# get the original timestep using init_timestep
												lowerCAmelCase__    						=						min(int(num_inference_steps * strength   )     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						max(num_inference_steps - init_timestep     ,						0   )
												lowerCAmelCase__    						=						self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if not isinstance(_UpperCamelCase     ,						torch.Tensor   ):
																		raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(_UpperCamelCase   )}"   )

												lowerCAmelCase__    						=						image.to(device=_UpperCamelCase     ,						dtype=_UpperCamelCase   )

												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						[
																		    self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(_UpperCamelCase   )
																		]
																		lowerCAmelCase__    						=						torch.cat(_UpperCamelCase     ,						dim=0   )
												else:
																		lowerCAmelCase__    						=						self.vae.encode(_UpperCamelCase   ).latent_dist.sample(_UpperCamelCase   )

												# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowerCAmelCase__    						=						0.1_82_15 * init_latents
												lowerCAmelCase__    						=						init_latents.repeat_interleave(_UpperCamelCase     ,						dim=0   )

												lowerCAmelCase__    						=						randn_tensor(init_latents.shape     ,						generator=_UpperCamelCase     ,						device=_UpperCamelCase     ,						dtype=_UpperCamelCase   )

												# get latents
												lowerCAmelCase__    						=						self.scheduler.add_noise(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						init_latents

												return latents






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.coca_transform(_UpperCamelCase   ).unsqueeze(0   )
												with torch.no_grad(), torch.cuda.amp.autocast():
																		lowerCAmelCase__    						=						self.coca_model.generate(transformed_image.to(device=self.device     ,						dtype=self.coca_model.dtype   )   )
												lowerCAmelCase__    						=						self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
												return generated.split('<end_of_text>'   )[0].replace('<start_of_text>'     ,						''   ).rstrip(' .,'   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.feature_extractor.preprocess(_UpperCamelCase   )
												lowerCAmelCase__    						=						torch.from_numpy(clip_image_input['pixel_values'][0]   ).unsqueeze(0   ).to(self.device   ).half()
												lowerCAmelCase__    						=						self.clip_model.get_image_features(_UpperCamelCase   )
												lowerCAmelCase__    						=						image_embeddings_clip / image_embeddings_clip.norm(p=2     ,						dim=-1     ,						keepdim=_UpperCamelCase   )
												lowerCAmelCase__    						=						image_embeddings_clip.repeat_interleave(_UpperCamelCase     ,						dim=0   )
												return image_embeddings_clip






						@torch.enable_grad()
						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						latents.detach().requires_grad_()

												lowerCAmelCase__    						=						self.scheduler.scale_model_input(_UpperCamelCase     ,						_UpperCamelCase   )

												# predict the noise residual
												lowerCAmelCase__    						=						self.unet(_UpperCamelCase     ,						_UpperCamelCase     ,						encoder_hidden_states=_UpperCamelCase   ).sample

												if isinstance(self.scheduler     ,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
																		lowerCAmelCase__    						=						self.scheduler.alphas_cumprod[timestep]
																		lowerCAmelCase__    						=						1 - alpha_prod_t
																		# compute predicted original sample from predicted noise also called
																		# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																		lowerCAmelCase__    						=						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																		lowerCAmelCase__    						=						torch.sqrt(_UpperCamelCase   )
																		lowerCAmelCase__    						=						pred_original_sample * (fac) + latents * (1 - fac)
												elif isinstance(self.scheduler     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						self.scheduler.sigmas[index]
																		lowerCAmelCase__    						=						latents - sigma * noise_pred
												else:
																		raise ValueError(F"scheduler type {type(self.scheduler   )} not supported"   )

												# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowerCAmelCase__    						=						1 / 0.1_82_15 * sample
												lowerCAmelCase__    						=						self.vae.decode(_UpperCamelCase   ).sample
												lowerCAmelCase__    						=						(image / 2 + 0.5).clamp(0     ,						1   )

												lowerCAmelCase__    						=						transforms.Resize(self.feature_extractor_size   )(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.normalize(_UpperCamelCase   ).to(latents.dtype   )

												lowerCAmelCase__    						=						self.clip_model.get_image_features(_UpperCamelCase   )
												lowerCAmelCase__    						=						image_embeddings_clip / image_embeddings_clip.norm(p=2     ,						dim=-1     ,						keepdim=_UpperCamelCase   )

												lowerCAmelCase__    						=						spherical_dist_loss(_UpperCamelCase     ,						_UpperCamelCase   ).mean() * clip_guidance_scale

												lowerCAmelCase__    						=						-torch.autograd.grad(_UpperCamelCase     ,						_UpperCamelCase   )[0]

												if isinstance(self.scheduler     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						latents.detach() + grads * (sigma**2)
																		lowerCAmelCase__    						=						noise_pred_original
												else:
																		lowerCAmelCase__    						=						noise_pred_original - torch.sqrt(_UpperCamelCase   ) * grads
												return noise_pred, latents







						@torch.no_grad()
						def __call__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 5_12     ,						_UpperCamelCase = 0.6     ,						_UpperCamelCase = 50     ,						_UpperCamelCase = 7.5     ,						_UpperCamelCase = 1     ,						_UpperCamelCase = 0.0     ,						_UpperCamelCase = 1_00     ,						_UpperCamelCase = None     ,						_UpperCamelCase = "pil"     ,						_UpperCamelCase = True     ,						_UpperCamelCase = 0.8     ,						_UpperCamelCase = 0.1     ,						_UpperCamelCase = 0.1     ,						):






												"""simple docstring"""
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ) and len(_UpperCamelCase   ) != batch_size:
																		raise ValueError(F"You have passed {batch_size} batch_size, but only {len(_UpperCamelCase   )} generators."   )

												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 isinstance(_UpperCamelCase     ,						torch.Generator   ) and batch_size > 1:
																		lowerCAmelCase__    						=						[generator] + [None] * (batch_size - 1)

												lowerCAmelCase__    						=						[
												    ('model', self.coca_model is None),
												    ('tokenizer', self.coca_tokenizer is None),
												    ('transform', self.coca_transform is None),
												]
												lowerCAmelCase__    						=						[x[0] for x in coca_is_none if x[1]]
												lowerCAmelCase__    						=						', '.join(_UpperCamelCase   )
												# generate prompts with coca model if prompt is None
												if content_prompt is None:
																		if len(_UpperCamelCase   ):
																								raise ValueError(
																								    F"Content prompt is None and CoCa [{coca_is_none_str}] is None."
																								    F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."   )
																		lowerCAmelCase__    						=						self.get_image_description(_UpperCamelCase   )
												if style_prompt is None:
																		if len(_UpperCamelCase   ):
																								raise ValueError(
																								    F"Style prompt is None and CoCa [{coca_is_none_str}] is None."
																								    F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."   )
																		lowerCAmelCase__    						=						self.get_image_description(_UpperCamelCase   )

												# get prompt text embeddings for content and style
												lowerCAmelCase__    						=						self.tokenizer(
												    _UpperCamelCase     ,						padding='max_length'     ,						max_length=self.tokenizer.model_max_length     ,						truncation=_UpperCamelCase     ,						return_tensors='pt'     ,						)
												lowerCAmelCase__    						=						self.text_encoder(content_text_input.input_ids.to(self.device   )   )[0]

												lowerCAmelCase__    						=						self.tokenizer(
												    _UpperCamelCase     ,						padding='max_length'     ,						max_length=self.tokenizer.model_max_length     ,						truncation=_UpperCamelCase     ,						return_tensors='pt'     ,						)
												lowerCAmelCase__    						=						self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

												lowerCAmelCase__    						=						slerp(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

												# duplicate text embeddings for each generation per prompt
												lowerCAmelCase__    						=						text_embeddings.repeat_interleave(_UpperCamelCase     ,						dim=0   )

												# set timesteps
												lowerCAmelCase__    						=						'offset' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
												lowerCAmelCase__    						=						{}
												if accepts_offset:
																		lowerCAmelCase__    						=						1

												self.scheduler.set_timesteps(_UpperCamelCase     ,						**_UpperCamelCase   )
												# Some schedulers like PNDM have timesteps as arrays
												# It's more optimized to move all timesteps to correct device beforehand
												self.scheduler.timesteps.to(self.device   )

												lowerCAmelCase__						,       lowerCAmelCase__    						=						self.get_timesteps(_UpperCamelCase     ,						_UpperCamelCase     ,						self.device   )
												lowerCAmelCase__    						=						timesteps[:1].repeat(_UpperCamelCase   )

												# Preprocess image
												lowerCAmelCase__    						=						preprocess(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						self.prepare_latents(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						text_embeddings.dtype     ,						self.device     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						preprocess(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )
												lowerCAmelCase__    						=						self.prepare_latents(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						text_embeddings.dtype     ,						self.device     ,						_UpperCamelCase   )

												lowerCAmelCase__    						=						slerp(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

												if clip_guidance_scale > 0:
																		lowerCAmelCase__    						=						self.get_clip_image_embeddings(_UpperCamelCase     ,						_UpperCamelCase   )
																		lowerCAmelCase__    						=						self.get_clip_image_embeddings(_UpperCamelCase     ,						_UpperCamelCase   )
																		lowerCAmelCase__    						=						slerp(
																		    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

												# 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.
												lowerCAmelCase__    						=						guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																		lowerCAmelCase__    						=						content_text_input.input_ids.shape[-1]
																		lowerCAmelCase__    						=						self.tokenizer(['']     ,						padding='max_length'     ,						max_length=_UpperCamelCase     ,						return_tensors='pt'   )
																		lowerCAmelCase__    						=						self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
																		# duplicate unconditional embeddings for each generation per prompt
																		lowerCAmelCase__    						=						uncond_embeddings.repeat_interleave(_UpperCamelCase     ,						dim=0   )

																		# 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
																		lowerCAmelCase__    						=						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`.
												lowerCAmelCase__    						=						(batch_size, self.unet.config.in_channels, height // 8, width // 8)
												lowerCAmelCase__    						=						text_embeddings.dtype
												if latents is None:
																		if self.device.type == "mps":
																								# randn does not work reproducibly on mps
																								lowerCAmelCase__    						=						torch.randn(_UpperCamelCase     ,						generator=_UpperCamelCase     ,						device='cpu'     ,						dtype=_UpperCamelCase   ).to(
																								    self.device   )
																		else:
																								lowerCAmelCase__    						=						torch.randn(_UpperCamelCase     ,						generator=_UpperCamelCase     ,						device=self.device     ,						dtype=_UpperCamelCase   )
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
																		lowerCAmelCase__    						=						latents.to(self.device   )

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

												# check if the scheduler accepts generator
												lowerCAmelCase__    						=						'generator' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
												if accepts_generator:
																		lowerCAmelCase__    						=						generator

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

																								# predict the noise residual
																								lowerCAmelCase__    						=						self.unet(_UpperCamelCase     ,						_UpperCamelCase     ,						encoder_hidden_states=_UpperCamelCase   ).sample

																								# perform classifier free guidance
																								if do_classifier_free_guidance:
																														lowerCAmelCase__						,       lowerCAmelCase__    						=						noise_pred.chunk(2   )
																														lowerCAmelCase__    						=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																								# perform clip guidance
																								if clip_guidance_scale > 0:
																														lowerCAmelCase__    						=						(
																														    text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
																														)
																														lowerCAmelCase__						,       lowerCAmelCase__    						=						self.cond_fn(
																														    _UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						)

																								# compute the previous noisy sample x_t -> x_t-1
																								lowerCAmelCase__    						=						self.scheduler.step(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						**_UpperCamelCase   ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowerCAmelCase__    						=						1 / 0.1_82_15 * latents
												lowerCAmelCase__    						=						self.vae.decode(_UpperCamelCase   ).sample

												lowerCAmelCase__    						=						(image / 2 + 0.5).clamp(0     ,						1   )
												lowerCAmelCase__    						=						image.cpu().permute(0     ,						2     ,						3     ,						1   ).numpy()

												if output_type == "pil":
																		lowerCAmelCase__    						=						self.numpy_to_pil(_UpperCamelCase   )

												if not return_dict:
																		return (image, None)

												return StableDiffusionPipelineOutput(images=_UpperCamelCase     ,						nsfw_content_detected=_UpperCamelCase   )













import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
__snake_case    :    str         =     (7_20, 12_80)  # Height, Width
__snake_case    :    Dict         =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
__snake_case    :    Dict         =     1 / 1_00
__snake_case    :    Optional[int]         =     """"""
__snake_case    :    Union[str, Any]         =     """"""
__snake_case    :    List[str]         =     """"""
__snake_case    :    List[str]         =     2_50







def 	_UpperCamelCase   (    )  ->   None:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__    						=						get_dataset(UpperCamelCase_    ,       UpperCamelCase_		)
						for index in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						random.sample(range(len(UpperCamelCase_		)		)    ,       4		)
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						update_image_and_anno(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       filter_scale=UpperCamelCase_    ,       )

												# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
												lowerCAmelCase__    						=						random_chars(32		)
												lowerCAmelCase__    						=						path.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												lowerCAmelCase__    						=						F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
												cva.imwrite(F"{file_root}.jpg"    ,       UpperCamelCase_    ,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
												print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"		)
												lowerCAmelCase__    						=						[]
												for anno in new_annos:
																		lowerCAmelCase__    						=						anno[3] - anno[1]
																		lowerCAmelCase__    						=						anno[4] - anno[2]
																		lowerCAmelCase__    						=						anno[1] + width / 2
																		lowerCAmelCase__    						=						anno[2] + height / 2
																		lowerCAmelCase__    						=						F"{anno[0]} {x_center} {y_center} {width} {height}"
																		annos_list.append(UpperCamelCase_		)
												with open(F"{file_root}.txt"    ,       'w'		) as outfile:
																		outfile.write('\n'.join(line for line in annos_list		)		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					str		)  ->   tuple[list, list]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for label_file in glob.glob(os.path.join(UpperCamelCase_    ,       '*.txt'		)		):
												lowerCAmelCase__    						=						label_file.split(os.sep		)[-1].rsplit('.'    ,       1		)[0]
												with open(UpperCamelCase_		) as in_file:
																		lowerCAmelCase__    						=						in_file.readlines()
												lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       F"{label_name}.jpg"		)

												lowerCAmelCase__    						=						[]
												for obj_list in obj_lists:
																		lowerCAmelCase__    						=						obj_list.rstrip('\n'		).split(' '		)
																		lowerCAmelCase__    						=						float(obj[1]		) - float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) - float(obj[4]		) / 2
																		lowerCAmelCase__    						=						float(obj[1]		) + float(obj[3]		) / 2
																		lowerCAmelCase__    						=						float(obj[2]		) + float(obj[4]		) / 2

																		boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
												if not boxes:
																		continue
												img_paths.append(UpperCamelCase_		)
												labels.append(UpperCamelCase_		)
						return img_paths, labels







def 	_UpperCamelCase   (    UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					tuple[int, int]    ,       UpperCamelCase_       :					tuple[float, float]    ,       UpperCamelCase_       :					float = 0.0    ,       )  ->   tuple[list, list, str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros([output_size[0], output_size[1], 3]    ,       dtype=np.uinta		)
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
						lowerCAmelCase__    						=						int(scale_x * output_size[1]		)
						lowerCAmelCase__    						=						int(scale_y * output_size[0]		)

						lowerCAmelCase__    						=						[]
						lowerCAmelCase__    						=						[]
						for i, index in enumerate(UpperCamelCase_		):
												lowerCAmelCase__    						=						all_img_list[index]
												path_list.append(UpperCamelCase_		)
												lowerCAmelCase__    						=						all_annos[index]
												lowerCAmelCase__    						=						cva.imread(UpperCamelCase_		)
												if i == 0:  # top-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 1:  # top-right
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (output_size[1] - divid_point_x, divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[2] * scale_y
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						bbox[4] * scale_y
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												elif i == 2:  # bottom-left
																		lowerCAmelCase__    						=						cva.resize(UpperCamelCase_    ,       (divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						bbox[1] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						bbox[3] * scale_x
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
												else:  # bottom-right
																		lowerCAmelCase__    						=						cva.resize(
																		    UpperCamelCase_    ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
																		lowerCAmelCase__    						=						img
																		for bbox in img_annos:
																								lowerCAmelCase__    						=						scale_x + bbox[1] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[2] * (1 - scale_y)
																								lowerCAmelCase__    						=						scale_x + bbox[3] * (1 - scale_x)
																								lowerCAmelCase__    						=						scale_y + bbox[4] * (1 - scale_y)
																								new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
						if filter_scale > 0:
												lowerCAmelCase__    						=						[
												    anno
												    for anno in new_anno
												    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
												]

						return output_img, new_anno, path_list[0]







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   str:

						"""simple docstring"""



						assert number_char > 1, "The number of character should greater than 1"
						lowerCAmelCase__    						=						ascii_lowercase + digits
						return "".join(random.choice(UpperCamelCase_		) for _ in range(UpperCamelCase_		)		)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")





