


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						:Union[str, Any]			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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




     """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=_lowerCAmelCase     ,					speech_processor=_lowerCAmelCase     ,					vae=_lowerCAmelCase     ,					text_encoder=_lowerCAmelCase     ,					tokenizer=_lowerCAmelCase     ,					unet=_lowerCAmelCase     ,					scheduler=_lowerCAmelCase     ,					feature_extractor=_lowerCAmelCase     ,					)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Optional[Union[str, int]] = "auto"	) ->			Union[str, Any]:




     """simple docstring"""
     if slice_size == "auto":
        snake_case_            =       self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Any:




     """simple docstring"""
     self.enable_attention_slicing(_lowerCAmelCase	)



  @torch.no_grad()
  def __call__(  self							:							Optional[int]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							str=1_6_0_0_0     ,					_lowerCAmelCase							:							int = 5_1_2     ,					_lowerCAmelCase							:							int = 5_1_2     ,					_lowerCAmelCase							:							int = 5_0     ,					_lowerCAmelCase							:							float = 7.5     ,					_lowerCAmelCase							:							Optional[Union[str, List[str]]] = None     ,					_lowerCAmelCase							:							Optional[int] = 1     ,					_lowerCAmelCase							:							float = 0.0     ,					_lowerCAmelCase							:							Optional[torch.Generator] = None     ,					_lowerCAmelCase							:							Optional[torch.FloatTensor] = None     ,					_lowerCAmelCase							:							Optional[str] = "pil"     ,					_lowerCAmelCase							:							bool = True     ,					_lowerCAmelCase							:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,					_lowerCAmelCase							:							int = 1     ,					**_lowerCAmelCase							:							int     ,					) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       self.speech_processor.feature_extractor(
         _lowerCAmelCase     ,					return_tensors="pt"     ,					sampling_rate=_lowerCAmelCase	).input_features.to(self.device	)
     snake_case_            =       self.speech_model.generate(_lowerCAmelCase     ,					max_length=4_8_0_0_0_0	)

     snake_case_            =       self.speech_processor.tokenizer.batch_decode(_lowerCAmelCase     ,					skip_special_tokens=_lowerCAmelCase     ,					normalize=_lowerCAmelCase	)[
         0
     ]

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

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

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

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

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

     # 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
     snake_case_            =       self.scheduler.timesteps.to(self.device	)

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

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

        # predict the noise residual
        snake_case_            =       self.unet(_lowerCAmelCase     ,					_lowerCAmelCase     ,					encoder_hidden_states=_lowerCAmelCase	).sample

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

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

     snake_case_            =       1 / 0.18_215 * latents
     snake_case_            =       self.vae.decode(_lowerCAmelCase	).sample

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

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

     if output_type == "pil":
        snake_case_            =       self.numpy_to_pil(_lowerCAmelCase	)

     if not return_dict:
        return image

     return StableDiffusionPipelineOutput(images=_lowerCAmelCase     ,					nsfw_content_detected=_lowerCAmelCase	)








from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


SCREAMING_SNAKE_CASE						:Union[str, Any]			=  logging.get_logger(__name__)


class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							42
  _SCREAMING_SNAKE_CASE		     =							None



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Any:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Dict     ,					_lowerCAmelCase							:							List[str]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Optional[int]:




     """simple docstring"""
     raise NotImplementedError



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     if not self.is_available():
        raise RuntimeError(
            F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''	)



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict	) ->			Tuple:




     """simple docstring"""
     return F'''`pip install {cls.pip_package or cls.name}`'''






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'optuna'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_optuna_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""
     return run_hp_search_optuna(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							int	) ->			str:




     """simple docstring"""
     return default_hp_space_optuna(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'ray'
  _SCREAMING_SNAKE_CASE		     =							'\'ray[tune]\''



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			Optional[Any]:




     """simple docstring"""
     return is_ray_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							List[Any]	) ->			List[str]:




     """simple docstring"""
     return run_hp_search_ray(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			Any:




     """simple docstring"""
     return default_hp_space_ray(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'sigopt'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[str]:




     """simple docstring"""
     return is_sigopt_available()



  def        lowerCAmelCase__	(  self							:							str     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Tuple	) ->			str:




     """simple docstring"""
     return run_hp_search_sigopt(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							Tuple	) ->			Union[str, Any]:




     """simple docstring"""
     return default_hp_space_sigopt(_lowerCAmelCase	)






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'wandb'



  @staticmethod
  def        lowerCAmelCase__	(  ) ->			List[Any]:




     """simple docstring"""
     return is_wandb_available()



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Tuple     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							str     ,					**_lowerCAmelCase							:							Any	) ->			Dict:




     """simple docstring"""
     return run_hp_search_wandb(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[str]	) ->			Any:




     """simple docstring"""
     return default_hp_space_wandb(_lowerCAmelCase	)






SCREAMING_SNAKE_CASE						:Union[str, Any]			=  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def       _lowerCAmelCase    (				)->str:
   '''simple docstring'''






   snake_case_            =       [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
   if len(lowerCAmelCase_				) > 0:
      snake_case_            =       available_backends[0].name
      if len(lowerCAmelCase_				) > 1:
         logger.info(
             F'''{len(lowerCAmelCase_				)} hyperparameter search backends available. Using {name} as the default.'''				)
      return name
   raise RuntimeError(
       "No hyperparameter search backend available.\n"
       + "\n".join(
           F''' - To install {backend.name} run {backend.pip_install()}'''
           for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)




