




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






class 						a				(   _lowerCamelCase     ):


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


				def   A_ (		self    :					List[str]					):
									self.enable_attention_slicing(lowercase_					)


				@torch.no_grad()
				def __call__(		self    :					int       ,				lowercase_    :					Union[str, List[str]]       ,				lowercase_    :					int = 512       ,				lowercase_    :					int = 512       ,				lowercase_    :					int = 50       ,				lowercase_    :					float = 7.5       ,				lowercase_    :					Optional[Union[str, List[str]]] = None       ,				lowercase_    :					Optional[int] = 1       ,				lowercase_    :					float = 0.0       ,				lowercase_    :					Optional[torch.Generator] = None       ,				lowercase_    :					Optional[torch.FloatTensor] = None       ,				lowercase_    :					Optional[str] = "pil"       ,				lowercase_    :					bool = True       ,				lowercase_    :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,				lowercase_    :					int = 1       ,				lowercase_    :					Optional[torch.FloatTensor] = None       ,				**lowercase_    :					Optional[int]       ,				):

									if isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				1
									elif isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				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
									snake_case_									=				self.tokenizer(
									    lowercase_       ,				padding='''max_length'''       ,				max_length=self.tokenizer.model_max_length       ,				return_tensors='''pt'''       ,				)
									snake_case_									=				text_inputs.input_ids

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

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

														snake_case_									=				text_input_ids.shape[-1]
														snake_case_									=				self.tokenizer(
														    lowercase_       ,				padding='''max_length'''       ,				max_length=lowercase_       ,				truncation=lowercase_       ,				return_tensors='''pt'''       ,				)
														snake_case_									=				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_									=				uncond_embeddings.shape[1]
														snake_case_									=				uncond_embeddings.repeat(lowercase_       ,				lowercase_       ,				1					)
														snake_case_									=				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
														snake_case_									=				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_									=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									snake_case_									=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
									snake_case_									=				text_embeddings.dtype
									if latents is None:
														if self.device.type == "mps":
																			# randn does not exist on mps
																			snake_case_									=				torch.randn(
																			    lowercase_       ,				generator=lowercase_       ,				device='''cpu'''       ,				dtype=lowercase_					).to(self.device					)
																			snake_case_									=				torch.randn(lowercase_       ,				generator=lowercase_       ,				device='''cpu'''       ,				dtype=lowercase_					).to(
																			    self.device					)
														else:
																			snake_case_									=				torch.randn(
																			    lowercase_       ,				generator=lowercase_       ,				device=self.device       ,				dtype=lowercase_					)
																			snake_case_									=				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}"					)
														snake_case_									=				latents_reference.to(self.device					)
														snake_case_									=				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_									=				(latents_shape[3] - latents_shape_reference[3]) // 2
									snake_case_									=				(latents_shape[2] - latents_shape_reference[2]) // 2
									snake_case_									=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
									snake_case_									=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
									snake_case_									=				0 if dx < 0 else dx
									snake_case_									=				0 if dy < 0 else dy
									snake_case_									=				max(-dx       ,				0					)
									snake_case_									=				max(-dy       ,				0					)
									# import pdb
									# pdb.set_trace()
									snake_case_									=				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
									snake_case_									=				self.scheduler.timesteps.to(self.device					)

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

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

														# predict the noise residual
														snake_case_									=				self.unet(lowercase_       ,				lowercase_       ,				encoder_hidden_states=lowercase_					).sample

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

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

									snake_case_									=				1 / 0.1_8215 * latents
									snake_case_									=				self.vae.decode(lowercase_					).sample

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

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

									if output_type == "pil":
														snake_case_									=				self.numpy_to_pil(lowercase_					)

									if not return_dict:
														return (image, has_nsfw_concept)

									return StableDiffusionPipelineOutput(images=lowercase_       ,				nsfw_content_detected=lowercase_					)






'''simple docstring'''





from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		CustomTokenizer
    pass
