

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


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






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







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

				"""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=a      ,      speech_processor=a      ,      vae=a      ,      text_encoder=a      ,      tokenizer=a      ,      unet=a      ,      scheduler=a      ,      feature_extractor=a      ,      )
			def 							__UpperCamelCase (			self							: str      ,      a							: Optional[Union[str, int]] = "auto"	)							->       List[str]:

				"""simple docstring"""

				if slice_size == "auto":
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(a	)
			def 							__UpperCamelCase (			self							: str	)							->       Optional[Any]:

				"""simple docstring"""

				self.enable_attention_slicing(a	)






			@torch.no_grad()
			def __call__(			self							: Optional[Any]      ,      a							: List[str]      ,      a							: List[Any]=1_6000      ,      a							: int = 512      ,      a							: int = 512      ,      a							: int = 50      ,      a							: float = 7.5      ,      a							: Optional[Union[str, List[str]]] = None      ,      a							: Optional[int] = 1      ,      a							: float = 0.0      ,      a							: Optional[torch.Generator] = None      ,      a							: Optional[torch.FloatTensor] = None      ,      a							: Optional[str] = "pil"      ,      a							: bool = True      ,      a							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,      a							: int = 1      ,      **a							: int      ,      )							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.speech_processor.feature_extractor(
				    a      ,      return_tensors="pt"      ,      sampling_rate=a	).input_features.to(self.device	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.speech_model.generate(a      ,      max_length=48_0000	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.speech_processor.tokenizer.batch_decode(a      ,      skip_special_tokens=a      ,      normalize=a	)[
				    0
				]

				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       1
				elif isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       len(a	)
				else:
					raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(a	)}"	)

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

				# get prompt text embeddings
				SCREAMING_SNAKE_CASE   :      str				     =       self.tokenizer(
				    a      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      return_tensors="pt"      ,      )
				SCREAMING_SNAKE_CASE   :      str				     =       text_inputs.input_ids

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

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

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

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

				# set timesteps
				self.scheduler.set_timesteps(a	)

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

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

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

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

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

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

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

				SCREAMING_SNAKE_CASE   :      Any				     =       1 / 0.1_8215 * latents
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.vae.decode(a	).sample

				SCREAMING_SNAKE_CASE   :      str				     =       (image / 2 + 0.5).clamp(0      ,      1	)

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

				if output_type == "pil":
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.numpy_to_pil(a	)

				if not return_dict:
					return image

				return StableDiffusionPipelineOutput(images=a      ,      nsfw_content_detected=a	)









import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()