


'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "vit_mae"







							def __init__(	self      ,					lowerCamelCase_=7_68      ,					lowerCamelCase_=12      ,					lowerCamelCase_=12      ,					lowerCamelCase_=30_72      ,					lowerCamelCase_="gelu"      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.0      ,					lowerCamelCase_=0.02      ,					lowerCamelCase_=1e-12      ,					lowerCamelCase_=2_24      ,					lowerCamelCase_=16      ,					lowerCamelCase_=3      ,					lowerCamelCase_=True      ,					lowerCamelCase_=16      ,					lowerCamelCase_=5_12      ,					lowerCamelCase_=8      ,					lowerCamelCase_=20_48      ,					lowerCamelCase_=0.75      ,					lowerCamelCase_=False      ,					**lowerCamelCase_      ,					) ->							List[str]:

														'''simple docstring'''





														super().__init__(**lowerCamelCase_     )

														lowerCAmelCase__      =			hidden_size
														lowerCAmelCase__      =			num_hidden_layers
														lowerCAmelCase__      =			num_attention_heads
														lowerCAmelCase__      =			intermediate_size
														lowerCAmelCase__      =			hidden_act
														lowerCAmelCase__      =			hidden_dropout_prob
														lowerCAmelCase__      =			attention_probs_dropout_prob
														lowerCAmelCase__      =			initializer_range
														lowerCAmelCase__      =			layer_norm_eps
														lowerCAmelCase__      =			image_size
														lowerCAmelCase__      =			patch_size
														lowerCAmelCase__      =			num_channels
														lowerCAmelCase__      =			qkv_bias
														lowerCAmelCase__      =			decoder_num_attention_heads
														lowerCAmelCase__      =			decoder_hidden_size
														lowerCAmelCase__      =			decoder_num_hidden_layers
														lowerCAmelCase__      =			decoder_intermediate_size
														lowerCAmelCase__      =			mask_ratio
														lowerCAmelCase__      =			norm_pix_loss



'''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


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




class 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					) ->							Optional[Any]:
														super().__init__()
														self.register_modules(
														    vae=lowerCamelCase_      ,					text_encoder=lowerCamelCase_      ,					tokenizer=lowerCamelCase_      ,					unet=lowerCamelCase_      ,					scheduler=lowerCamelCase_      ,					safety_checker=lowerCamelCase_      ,					feature_extractor=lowerCamelCase_      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_ = "auto"     ) ->							Any:
														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(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														self.enable_attention_slicing(lowerCamelCase_     )







							@torch.no_grad()
							def __call__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = 5_12      ,					lowerCamelCase_ = 5_12      ,					lowerCamelCase_ = 50      ,					lowerCamelCase_ = 7.5      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = 1      ,					lowerCamelCase_ = 0.0      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = "pil"      ,					lowerCamelCase_ = True      ,					lowerCamelCase_ = None      ,					lowerCamelCase_ = 1      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_      ,					) ->							Any:

														if isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			1
														elif isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																					lowerCAmelCase__      =			len(lowerCamelCase_     )
														else:
																					raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_     )}"""     )

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

														# get prompt text embeddings
														lowerCAmelCase__      =			self.tokenizer(
														    lowerCamelCase_      ,					padding='''max_length'''      ,					max_length=self.tokenizer.model_max_length      ,					return_tensors='''pt'''      ,					)
														lowerCAmelCase__      =			text_inputs.input_ids

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																					lowerCAmelCase__      =			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}"""     )
																					lowerCAmelCase__      =			text_input_ids[:, : self.tokenizer.model_max_length]

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

														# duplicate text embeddings for each generation per prompt, using mps friendly method
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			text_embeddings.shape
														lowerCAmelCase__      =			text_embeddings.repeat(1      ,					lowerCamelCase_      ,					1     )
														lowerCAmelCase__      =			text_embeddings.view(bs_embed * num_images_per_prompt      ,					lowerCamelCase_      ,					-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.
														lowerCAmelCase__      =			guidance_scale > 1.0
														# get unconditional embeddings for classifier free guidance
														if do_classifier_free_guidance:
																					lowerCAmelCase__      =			42
																					if negative_prompt is None:
																												lowerCAmelCase__      =			['''''']
																					elif type(lowerCamelCase_     ) is not type(lowerCamelCase_     ):
																												raise TypeError(
																												    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_     )} !="""
																												    F""" {type(lowerCamelCase_     )}."""     )
																					elif isinstance(lowerCamelCase_      ,					lowerCamelCase_     ):
																												lowerCAmelCase__      =			[negative_prompt]
																					elif batch_size != len(lowerCamelCase_     ):
																												raise ValueError(
																												    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_     )}, but `prompt`:"""
																												    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																												    ''' the batch size of `prompt`.'''     )
																					else:
																												lowerCAmelCase__      =			negative_prompt

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

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

														# set timesteps
														self.scheduler.set_timesteps(lowerCamelCase_     )

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

														for i, t in enumerate(self.progress_bar(lowerCamelCase_     )     ):
																					# 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(lowerCamelCase_      ,					lowerCamelCase_     )

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

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

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

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

														lowerCAmelCase__      =			1 / 0.18_215 * latents
														lowerCAmelCase__      =			self.vae.decode(lowerCamelCase_     ).sample

														lowerCAmelCase__      =			(image / 2 + 0.5).clamp(0      ,					1     )

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

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

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

														if not return_dict:
																					return (image, has_nsfw_concept)

														return StableDiffusionPipelineOutput(images=lowerCamelCase_      ,					nsfw_content_detected=lowerCamelCase_     )