import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


SCREAMING_SNAKE_CASE		:     Optional[int]         =	logging.get_logger(__name__)

class        UpperCamelCase						(				__a				):
						a__		:Optional[Any]      =			CLIPConfig

						a__		:int      =			['''CLIPEncoderLayer''']





						def __init__(self			,     __UpperCamelCase						)				->				Union[str, Any]:
												super().__init__(__UpperCamelCase						)

												UpperCamelCase_		:							int  =		CLIPVisionModelWithProjection(config.vision_config						)

												UpperCamelCase_		:							Any  =		nn.Linear(config.vision_config.projection_dim			,     1						)
												UpperCamelCase_		:							int  =		nn.Linear(config.vision_config.projection_dim			,     1						)





						@torch.no_grad()
						def        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase=0.5			,     __UpperCamelCase=0.5						)				->				Any:
												UpperCamelCase_		:							Dict  =		self.vision_model(__UpperCamelCase						)[0]

												UpperCamelCase_		:							int  =		self.p_head(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		nsfw_detected.flatten()
												UpperCamelCase_		:							Tuple  =		nsfw_detected > p_threshold
												UpperCamelCase_		:							int  =		nsfw_detected.tolist()

												if any(__UpperCamelCase						):
																		logger.warning(
																		    """Potential NSFW content was detected in one or more images. A black image will be returned instead."""
																		    """ Try again with a different prompt and/or seed."""						)

												for idx, nsfw_detected_ in enumerate(__UpperCamelCase						):
																		if nsfw_detected_:
																								UpperCamelCase_		:							str  =		np.zeros(images[idx].shape						)

												UpperCamelCase_		:							List[str]  =		self.w_head(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		watermark_detected.flatten()
												UpperCamelCase_		:							Dict  =		watermark_detected > w_threshold
												UpperCamelCase_		:							Any  =		watermark_detected.tolist()

												if any(__UpperCamelCase						):
																		logger.warning(
																		    """Potential watermarked content was detected in one or more images. A black image will be returned instead."""
																		    """ Try again with a different prompt and/or seed."""						)

												for idx, watermark_detected_ in enumerate(__UpperCamelCase						):
																		if watermark_detected_:
																								UpperCamelCase_		:							Tuple  =		np.zeros(images[idx].shape						)

												return images, nsfw_detected, watermark_detected







import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


SCREAMING_SNAKE_CASE		:     List[str]         =	logging.get_logger(__name__)  # pylint: disable=invalid-name

class        UpperCamelCase						(				__a				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     __UpperCamelCase			,     )				->				Tuple:
												super().__init__()

												if safety_checker is None:
																		logger.warning(
																		    f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
																		    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
																		    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
																		    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
																		    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
																		    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""						)

												self.register_modules(
												    speech_model=__UpperCamelCase			,     speech_processor=__UpperCamelCase			,     vae=__UpperCamelCase			,     text_encoder=__UpperCamelCase			,     tokenizer=__UpperCamelCase			,     unet=__UpperCamelCase			,     scheduler=__UpperCamelCase			,     feature_extractor=__UpperCamelCase			,     )





						def        A_							(self			,     __UpperCamelCase = "auto"						)				->				List[str]:
												if slice_size == "auto":
																		UpperCamelCase_		:							Any  =		self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(__UpperCamelCase						)





						def        A_							(self						)				->				Any:
												self.enable_attention_slicing(__UpperCamelCase						)





						@torch.no_grad()
						def __call__(self			,     __UpperCamelCase			,     __UpperCamelCase=16_000			,     __UpperCamelCase = 512			,     __UpperCamelCase = 512			,     __UpperCamelCase = 50			,     __UpperCamelCase = 7.5			,     __UpperCamelCase = None			,     __UpperCamelCase = 1			,     __UpperCamelCase = 0.0			,     __UpperCamelCase = None			,     __UpperCamelCase = None			,     __UpperCamelCase = "pil"			,     __UpperCamelCase = True			,     __UpperCamelCase = None			,     __UpperCamelCase = 1			,     **__UpperCamelCase			,     )				->				Optional[int]:
												UpperCamelCase_		:							str  =		self.speech_processor.feature_extractor(
												    __UpperCamelCase			,     return_tensors="""pt"""			,     sampling_rate=__UpperCamelCase						).input_features.to(self.device						)
												UpperCamelCase_		:							List[Any]  =		self.speech_model.generate(__UpperCamelCase			,     max_length=480_000						)

												UpperCamelCase_		:							List[Any]  =		self.speech_processor.tokenizer.batch_decode(__UpperCamelCase			,     skip_special_tokens=__UpperCamelCase			,     normalize=__UpperCamelCase						)[
												    0
												]

												if isinstance(__UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							List[Any]  =		1
												elif isinstance(__UpperCamelCase			,     __UpperCamelCase						):
																		UpperCamelCase_		:							Optional[int]  =		len(__UpperCamelCase						)
												else:
																		raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase						)}'''						)

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

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

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		UpperCamelCase_		:							List[str]  =		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}'''						)
																		UpperCamelCase_		:							Optional[int]  =		text_input_ids[:, : self.tokenizer.model_max_length]
												UpperCamelCase_		:							Tuple  =		self.text_encoder(text_input_ids.to(self.device						)						)[0]

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

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

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		UpperCamelCase_		:							List[str]  =		uncond_embeddings.shape[1]
																		UpperCamelCase_		:							List[str]  =		uncond_embeddings.repeat(1			,     __UpperCamelCase			,     1						)
																		UpperCamelCase_		:							Tuple  =		uncond_embeddings.view(batch_size * num_images_per_prompt			,     __UpperCamelCase			,     -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
																		UpperCamelCase_		:							str  =		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`.
												UpperCamelCase_		:							Union[str, Any]  =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												UpperCamelCase_		:							Optional[int]  =		text_embeddings.dtype
												if latents is None:
																		if self.device.type == "mps":
																								# randn does not exist on mps
																								UpperCamelCase_		:							str  =		torch.randn(__UpperCamelCase			,     generator=__UpperCamelCase			,     device="""cpu"""			,     dtype=__UpperCamelCase						).to(
																								    self.device						)
																		else:
																								UpperCamelCase_		:							Optional[Any]  =		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}'''						)
																		UpperCamelCase_		:							Optional[int]  =		latents.to(self.device						)

												# set timesteps
												self.scheduler.set_timesteps(__UpperCamelCase						)

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

												# scale the initial noise by the standard deviation required by the scheduler
												UpperCamelCase_		:							List[str]  =		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]
												UpperCamelCase_		:							Optional[Any]  =		"""eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
												UpperCamelCase_		:							Union[str, Any]  =		{}
												if accepts_eta:
																		UpperCamelCase_		:							Any  =		eta

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

																		# predict the noise residual
																		UpperCamelCase_		:							Tuple  =		self.unet(__UpperCamelCase			,     __UpperCamelCase			,     encoder_hidden_states=__UpperCamelCase						).sample

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

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

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

												UpperCamelCase_		:							List[str]  =		1 / 0.18_215 * latents
												UpperCamelCase_		:							List[Any]  =		self.vae.decode(__UpperCamelCase						).sample

												UpperCamelCase_		:							Dict  =		(image / 2 + 0.5).clamp(0			,     1						)

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

												if output_type == "pil":
																		UpperCamelCase_		:							List[Any]  =		self.numpy_to_pil(__UpperCamelCase						)

												if not return_dict:
																		return image

												return StableDiffusionPipelineOutput(images=__UpperCamelCase			,     nsfw_content_detected=__UpperCamelCase						)






