





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






				"""simple docstring"""







				def __init__(    self	:   Tuple   ,							__a	:   Dict   ,							__a	:   str   ,							__a	:   List[str]   ,							__a	:   Optional[int]   ,							__a	:   Optional[Any]   ,							__a	:   List[str]   ,							__a	:   List[Any]   ,							__a	:   Tuple   ,							__a	:   Dict   ,							):
								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	:   Tuple   ,							__a	:   str = "auto"   ):







								if slice_size == "auto":
												_a				      =					self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_A   )




				def     UpperCamelCase__  (    self	:   List[Any]   ):
								self.enable_attention_slicing(_A   )




				@torch.no_grad()
				def __call__(    self	:   List[str]   ,							__a	:   Optional[Any]   ,							__a	:   str=1_60_00   ,							__a	:   Optional[Any] = 5_12   ,							__a	:   List[str] = 5_12   ,							__a	:   Tuple = 50   ,							__a	:   Optional[Any] = 7.5   ,							__a	:   str = None   ,							__a	:   int = 1   ,							__a	:   str = 0.0   ,							__a	:   Optional[Any] = None   ,							__a	:   Tuple = None   ,							__a	:   str = "pil"   ,							__a	:   Tuple = True   ,							__a	:   List[Any] = None   ,							__a	:   Optional[Any] = 1   ,							**__a	:   Tuple   ,							):
								_a				      =					self.speech_processor.feature_extractor(
								    _A   ,							return_tensors="pt"   ,							sampling_rate=_A   ).input_features.to(self.device   )
								_a				      =					self.speech_model.generate(_A   ,							max_length=48_00_00   )

								_a				      =					self.speech_processor.tokenizer.batch_decode(_A   ,							skip_special_tokens=_A   ,							normalize=_A   )[
								    0
								]

								if isinstance(_A   ,							_A   ):
												_a				      =					1
								elif isinstance(_A   ,							_A   ):
												_a				      =					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
								_a				      =					self.tokenizer(
								    _A   ,							padding="max_length"   ,							max_length=self.tokenizer.model_max_length   ,							return_tensors="pt"   ,							)
								_a				      =					text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												_a				      =					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}'   )
												_a				      =					text_input_ids[:, : self.tokenizer.model_max_length]
								_a				      =					self.text_encoder(text_input_ids.to(self.device   )   )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								_a				      =					text_embeddings.shape
								_a				      =					text_embeddings.repeat(1   ,							_A   ,							1   )
								_a				      =					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.
								_a				      =					guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												_a				      =					42
												if negative_prompt is None:
																_a				      =					[""""""] * 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   ):
																_a				      =					[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:
																_a				      =					negative_prompt

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

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												_a				      =					uncond_embeddings.shape[1]
												_a				      =					uncond_embeddings.repeat(1   ,							_A   ,							1   )
												_a				      =					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
												_a				      =					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`.
								_a				      =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								_a				      =					text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																_a				      =					torch.randn(_A   ,							generator=_A   ,							device="cpu"   ,							dtype=_A   ).to(
																    self.device   )
												else:
																_a				      =					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}'   )
												_a				      =					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
								_a				      =					self.scheduler.timesteps.to(self.device   )

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

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

												# predict the noise residual
												_a				      =					self.unet(_A   ,							_A   ,							encoder_hidden_states=_A   ).sample

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

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

								_a				      =					1 / 0.18215 * latents
								_a				      =					self.vae.decode(_A   ).sample

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

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

								if output_type == "pil":
												_a				      =					self.numpy_to_pil(_A   )

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=_A   ,							nsfw_content_detected=_A   )







"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return math.pow(SCREAMING_SNAKE_CASE							,     2     ) - a

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return 2 * x

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			2.0

						while start <= a:
												UpperCamelCase				:		Optional[Any]				   =			math.pow(SCREAMING_SNAKE_CASE							,     2     )

						return start

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = 9999							,     SCREAMING_SNAKE_CASE = 0.00_00_00_00_00_00_01     ):

						if a < 0:
												raise ValueError("""math domain error"""     )

						UpperCamelCase				:		Optional[Any]				   =			get_initial_point(SCREAMING_SNAKE_CASE     )

						for _ in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			value
												UpperCamelCase				:		Tuple				   =			value - fx(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / fx_derivative(SCREAMING_SNAKE_CASE     )
												if abs(prev_value - value     ) < tolerance:
																		return value

						return value


if __name__ == "__main__":
		from doctest import testmod

		testmod()



