


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


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







class 		_snake_case  (      _snake_case							):




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  ):
       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase = "auto"    ):
       if slice_size == "auto":
             a						:List[str] 							=       self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.enable_attention_slicing(_lowerCamelCase    )




 @torch.no_grad()
 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase=1_6000					,  _lowerCamelCase = 512					,  _lowerCamelCase = 512					,  _lowerCamelCase = 50					,  _lowerCamelCase = 7.5					,  _lowerCamelCase = None					,  _lowerCamelCase = 1					,  _lowerCamelCase = 0.0					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = "pil"					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  _lowerCamelCase = 1					,  **_lowerCamelCase					,  ):
       a						:Union[str, Any] 							=       self.speech_processor.feature_extractor(
           _lowerCamelCase					,  return_tensors='''pt'''					,  sampling_rate=_lowerCamelCase    ).input_features.to(self.device    )
       a						:Optional[Any] 							=       self.speech_model.generate(_lowerCamelCase					,  max_length=48_0000    )

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

       if isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Any 							=       1
       elif isinstance(_lowerCamelCase					,  _lowerCamelCase    ):
             a						:Tuple 							=       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
       a						:Tuple 							=       self.tokenizer(
           _lowerCamelCase					,  padding='''max_length'''					,  max_length=self.tokenizer.model_max_length					,  return_tensors='''pt'''					,  )
       a						:str 							=       text_inputs.input_ids

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       a							,				a							,				a						:List[Any] 							=       text_embeddings.shape
       a						:int 							=       text_embeddings.repeat(1					,  _lowerCamelCase					,  1    )
       a						:Dict 							=       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.
       a						:List[Any] 							=       guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
             a						:List[str]
             if negative_prompt is None:
                   a						:int 							=       [''''''] * 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    ):
                   a						:str 							=       [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:
                   a						:str 							=       negative_prompt

             a						:List[Any] 							=       text_input_ids.shape[-1]
             a						:Optional[Any] 							=       self.tokenizer(
                 _lowerCamelCase					,  padding='''max_length'''					,  max_length=_lowerCamelCase					,  truncation=_lowerCamelCase					,  return_tensors='''pt'''					,  )
             a						: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
             a						:List[str] 							=       uncond_embeddings.shape[1]
             a						:Any 							=       uncond_embeddings.repeat(1					,  _lowerCamelCase					,  1    )
             a						:Optional[int] 							=       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
             a						:Optional[int] 							=       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						:List[str] 							=       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       a						:Any 							=       text_embeddings.dtype
       if latents is None:
             if self.device.type == "mps":
                   # randn does not exist on mps
                   a						:int 							=       torch.randn(_lowerCamelCase					,  generator=_lowerCamelCase					,  device='''cpu'''					,  dtype=_lowerCamelCase    ).to(
                       self.device    )
             else:
                   a						:Optional[Any] 							=       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}'''    )
             a						:Optional[Any] 							=       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
       a						:int 							=       self.scheduler.timesteps.to(self.device    )

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

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

             # predict the noise residual
             a						:List[str] 							=       self.unet(_lowerCamelCase					,  _lowerCamelCase					,  encoder_hidden_states=_lowerCamelCase    ).sample

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

             # compute the previous noisy sample x_t -> x_t-1
             a						:int 							=       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    )

       a						:Any 							=       1 / 0.1_8215 * latents
       a						:Tuple 							=       self.vae.decode(_lowerCamelCase    ).sample

       a						:Tuple 							=       (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						:Tuple 							=       image.cpu().permute(0					,  2					,  3					,  1    ).float().numpy()

       if output_type == "pil":
             a						:Dict 							=       self.numpy_to_pil(_lowerCamelCase    )

       if not return_dict:
             return image

       return StableDiffusionPipelineOutput(images=_lowerCamelCase					,  nsfw_content_detected=_lowerCamelCase    )












"""simple docstring"""
from typing import Any



def      __lowerCamelCase   (							a_     :		list							)			->      list[Any]:
      if not input_list:
            return []
      __SCREAMING_SNAKE_CASE   :int						=  [input_list.count(a_							) for value in input_list]
      __SCREAMING_SNAKE_CASE   :str						=  max(a_							)  # Gets the maximum count in the input list.
      # Gets values of modes
      return sorted({input_list[i] for i, value in enumerate(a_							) if value == y}							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()