



"""simple docstring"""







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


lowerCAmelCase_   						=   logging.get_logger(__name__)  # pylint: disable=invalid-name
class        __A       (	A_    ):







		'''simple docstring'''





		def __init__(	self						:  Union[str, Any]			,_snake_case						:  WhisperForConditionalGeneration			,_snake_case						:  WhisperProcessor			,_snake_case						:  AutoencoderKL			,_snake_case						:  CLIPTextModel			,_snake_case						:  CLIPTokenizer			,_snake_case						:  UNetaDConditionModel			,_snake_case						:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,_snake_case						:  StableDiffusionSafetyChecker			,_snake_case						:  CLIPImageProcessor			,)					->		int:






				"""simple docstring"""



				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=__SCREAMING_SNAKE_CASE			,speech_processor=__SCREAMING_SNAKE_CASE			,vae=__SCREAMING_SNAKE_CASE			,text_encoder=__SCREAMING_SNAKE_CASE			,tokenizer=__SCREAMING_SNAKE_CASE			,unet=__SCREAMING_SNAKE_CASE			,scheduler=__SCREAMING_SNAKE_CASE			,feature_extractor=__SCREAMING_SNAKE_CASE			,)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Union[str, int]] = "auto"						)					->		int:






				"""simple docstring"""



				if slice_size == "auto":
						lowercase__   :    int				=			self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__SCREAMING_SNAKE_CASE						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Optional[Any]:






				"""simple docstring"""



				self.enable_attention_slicing(__SCREAMING_SNAKE_CASE						)







		@torch.no_grad()
		def __call__(	self						:  Optional[int]			,_snake_case						:  Any			,_snake_case						:  Tuple=16_000			,_snake_case						:  int = 512			,_snake_case						:  int = 512			,_snake_case						:  int = 50			,_snake_case						:  float = 7.5			,_snake_case						:  Optional[Union[str, List[str]]] = None			,_snake_case						:  Optional[int] = 1			,_snake_case						:  float = 0.0			,_snake_case						:  Optional[torch.Generator] = None			,_snake_case						:  Optional[torch.FloatTensor] = None			,_snake_case						:  Optional[str] = "pil"			,_snake_case						:  bool = True			,_snake_case						:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,_snake_case						:  int = 1			,**_snake_case						:  str			,)					->		Optional[Any]:






				"""simple docstring"""



				lowercase__   :    Any				=			self.speech_processor.feature_extractor(
				    __SCREAMING_SNAKE_CASE			,return_tensors='''pt'''			,sampling_rate=__SCREAMING_SNAKE_CASE						).input_features.to(self.device						)
				lowercase__   :    Dict				=			self.speech_model.generate(__SCREAMING_SNAKE_CASE			,max_length=480_000						)

				lowercase__   :    Optional[Any]				=			self.speech_processor.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			,skip_special_tokens=__SCREAMING_SNAKE_CASE			,normalize=__SCREAMING_SNAKE_CASE						)[
				    0
				]

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

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

				# get prompt text embeddings
				lowercase__   :    Optional[int]				=			self.tokenizer(
				    __SCREAMING_SNAKE_CASE			,padding='''max_length'''			,max_length=self.tokenizer.model_max_length			,return_tensors='''pt'''			,)
				lowercase__   :    Optional[Any]				=			text_inputs.input_ids

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

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

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

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

				# set timesteps
				self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE						)

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

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

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

						# predict the noise residual
						lowercase__   :    Optional[int]				=			self.unet(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,encoder_hidden_states=__SCREAMING_SNAKE_CASE						).sample

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

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

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

				lowercase__   :    str				=			1 / 0.1_8215 * latents
				lowercase__   :    List[Any]				=			self.vae.decode(__SCREAMING_SNAKE_CASE						).sample

				lowercase__   :    Union[str, Any]				=			(image / 2 + 0.5).clamp(0			,1						)

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

				if output_type == "pil":
						lowercase__   :    List[str]				=			self.numpy_to_pil(__SCREAMING_SNAKE_CASE						)

				if not return_dict:
						return image

				return StableDiffusionPipelineOutput(images=__SCREAMING_SNAKE_CASE			,nsfw_content_detected=__SCREAMING_SNAKE_CASE						)



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






