



"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


A:     Any										=	"CompVis/stable-diffusion-v1-1"
A:     int										=	"CompVis/stable-diffusion-v1-2"
A:     Any										=	"CompVis/stable-diffusion-v1-3"
A:     Union[str, Any]										=	"CompVis/stable-diffusion-v1-4"



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):

			def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = True   ,  )						->   Optional[int]:





				'''simple docstring'''




				super()._init_()

				UpperCAmelCase			:      Union[str, Any]			=			StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		)
				UpperCAmelCase			:      Union[str, Any]			=			StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		)
				UpperCAmelCase			:      List[str]			=			StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE		)
				UpperCAmelCase			:      Any			=			StableDiffusionPipeline(
				    vae=_SCREAMING_SNAKE_CASE   ,  text_encoder=_SCREAMING_SNAKE_CASE   ,  tokenizer=_SCREAMING_SNAKE_CASE   ,  unet=_SCREAMING_SNAKE_CASE   ,  scheduler=_SCREAMING_SNAKE_CASE   ,  safety_checker=_SCREAMING_SNAKE_CASE   ,  feature_extractor=_SCREAMING_SNAKE_CASE   ,  requires_safety_checker=_SCREAMING_SNAKE_CASE   ,  )

				self.register_modules(pipelinea=self.pipea   ,  pipelinea=self.pipea   ,  pipelinea=self.pipea   ,  pipelinea=self.pipea		)

			@property
			def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict[str, Any]:





				'''simple docstring'''




				return {k: getattr(self   ,  _SCREAMING_SNAKE_CASE		) for k in self.config.keys() if not k.startswith("""_"""		)}

			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = "auto"		)						->   List[str]:





				'''simple docstring'''




				if slice_size == "auto":
					# half the attention head size is usually a good trade-off between
					# speed and memory
					UpperCAmelCase			:      List[Any]			=			self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(_SCREAMING_SNAKE_CASE		)

			def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





				'''simple docstring'''




				self.enable_attention_slicing(_SCREAMING_SNAKE_CASE		)

			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = 7.5   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   str:





				'''simple docstring'''




				return self.pipea(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = 7.5   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





				'''simple docstring'''




				return self.pipea(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = 7.5   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Any:





				'''simple docstring'''




				return self.pipea(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = 7.5   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[Any]:





				'''simple docstring'''




				return self.pipea(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )




			@torch.no_grad()
			def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 512   ,  _SCREAMING_SNAKE_CASE = 50   ,  _SCREAMING_SNAKE_CASE = 7.5   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  _SCREAMING_SNAKE_CASE = 0.0   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "pil"   ,  _SCREAMING_SNAKE_CASE = True   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = 1   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Tuple:





				'''simple docstring'''




				UpperCAmelCase			:      int			=			"""cuda""" if torch.cuda.is_available() else """cpu"""
				self.to(_SCREAMING_SNAKE_CASE		)

				# Checks if the height and width are divisible by 8 or not
				if height % 8 != 0 or width % 8 != 0:
					raise ValueError(F"`height` and `width` must be divisible by 8 but are {height} and {width}."		)

				# Get first result from Stable Diffusion Checkpoint v1.1
				UpperCAmelCase			:      Optional[int]			=			self.textaimg_sda_a(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

				# Get first result from Stable Diffusion Checkpoint v1.2
				UpperCAmelCase			:      Any			=			self.textaimg_sda_a(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

				# Get first result from Stable Diffusion Checkpoint v1.3
				UpperCAmelCase			:      Optional[Any]			=			self.textaimg_sda_a(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

				# Get first result from Stable Diffusion Checkpoint v1.4
				UpperCAmelCase			:      Union[str, Any]			=			self.textaimg_sda_a(
				    prompt=_SCREAMING_SNAKE_CASE   ,  height=_SCREAMING_SNAKE_CASE   ,  width=_SCREAMING_SNAKE_CASE   ,  num_inference_steps=_SCREAMING_SNAKE_CASE   ,  guidance_scale=_SCREAMING_SNAKE_CASE   ,  negative_prompt=_SCREAMING_SNAKE_CASE   ,  num_images_per_prompt=_SCREAMING_SNAKE_CASE   ,  eta=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE   ,  latents=_SCREAMING_SNAKE_CASE   ,  output_type=_SCREAMING_SNAKE_CASE   ,  return_dict=_SCREAMING_SNAKE_CASE   ,  callback=_SCREAMING_SNAKE_CASE   ,  callback_steps=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

				# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
				return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]		)





"""simple docstring"""
import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


A:     List[Any]										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		'AutoTokenizer'
   __lowerCAmelCase						:     str		=		['tokenizer']

   __lowerCAmelCase						:     Any		=		{
       'semantic_prompt': 1,
       'coarse_prompt': 2,
       'fine_prompt': 2,
   }

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=None		)						->   int:





    '''simple docstring'''




    super().__init__(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			speaker_embeddings

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  **_SCREAMING_SNAKE_CASE		)						->   List[Any]:





    '''simple docstring'''




    if speaker_embeddings_dict_path is not None:
     UpperCAmelCase			:      Any			=			get_file_from_repo(
         _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if speaker_embeddings_path is None:
      logger.warning(
          F"`{os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."		)
      UpperCAmelCase			:      Optional[int]			=			None
     else:
      with open(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
       UpperCAmelCase			:      List[str]			=			json.load(_SCREAMING_SNAKE_CASE		)
    else:
     UpperCAmelCase			:      List[str]			=			None

    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

    return cls(tokenizer=_SCREAMING_SNAKE_CASE   ,  speaker_embeddings=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json"   ,  _SCREAMING_SNAKE_CASE="speaker_embeddings"   ,  _SCREAMING_SNAKE_CASE = False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Union[str, Any]:





    '''simple docstring'''




    if self.speaker_embeddings is not None:
     os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  """v2"""		)   ,  exist_ok=_SCREAMING_SNAKE_CASE		)

     UpperCAmelCase			:      str			=			{}

     UpperCAmelCase			:      Union[str, Any]			=			save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       UpperCAmelCase			:      Optional[Any]			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

       UpperCAmelCase			:      Any			=			{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["""repo_or_path"""]   ,  _SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}"		)   ,  voice_preset[key]   ,  allow_pickle=_SCREAMING_SNAKE_CASE   ,  )
        UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  F"{prompt_key}_{key}.npy"		)

       UpperCAmelCase			:      Tuple			=			tmp_dict

     with open(os.path.join(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)   ,  """w"""		) as fp:
      json.dump(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    super().save_pretrained(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None   ,  **_SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.speaker_embeddings[voice_preset]

    UpperCAmelCase			:      List[Any]			=			{}
    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset_paths:
      raise ValueError(
          F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."		)

     UpperCAmelCase			:      List[str]			=			get_file_from_repo(
         self.speaker_embeddings.get("""repo_or_path"""   ,  """/"""		)   ,  voice_preset_paths[key]   ,  subfolder=kwargs.pop("""subfolder"""   ,  _SCREAMING_SNAKE_CASE		)   ,  cache_dir=kwargs.pop("""cache_dir"""   ,  _SCREAMING_SNAKE_CASE		)   ,  force_download=kwargs.pop("""force_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  proxies=kwargs.pop("""proxies"""   ,  _SCREAMING_SNAKE_CASE		)   ,  resume_download=kwargs.pop("""resume_download"""   ,  _SCREAMING_SNAKE_CASE		)   ,  local_files_only=kwargs.pop("""local_files_only"""   ,  _SCREAMING_SNAKE_CASE		)   ,  use_auth_token=kwargs.pop("""use_auth_token"""   ,  _SCREAMING_SNAKE_CASE		)   ,  revision=kwargs.pop("""revision"""   ,  _SCREAMING_SNAKE_CASE		)   ,  )
     if path is None:
      raise ValueError(
          F"`{os.path.join(self.speaker_embeddings.get('repo_or_path'   ,  '/'		)   ,  voice_preset_paths[key]		)}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."		)

     UpperCAmelCase			:      List[str]			=			np.load(_SCREAMING_SNAKE_CASE		)

    return voice_preset_dict

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE = None		)						->   List[str]:





    '''simple docstring'''




    for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
     if key not in voice_preset:
      raise ValueError(F"Voice preset unrecognized, missing {key} as a key."		)

     if not isinstance(voice_preset[key]   ,  np.ndarray		):
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)

     if len(voice_preset[key].shape		) != self.preset_shape[key]:
      raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key]		)}D ndarray."		)




   def __call__(     self   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE=None   ,  _SCREAMING_SNAKE_CASE="pt"   ,  _SCREAMING_SNAKE_CASE=256   ,  _SCREAMING_SNAKE_CASE=False   ,  _SCREAMING_SNAKE_CASE=True   ,  _SCREAMING_SNAKE_CASE=False   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   Optional[int]:





    '''simple docstring'''




    if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		):
     if (
         isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      UpperCAmelCase			:      Dict			=			self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

     else:
      if isinstance(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		) and not voice_preset.endswith(""".npz"""		):
       UpperCAmelCase			:      Tuple			=			voice_preset + """.npz"""

      UpperCAmelCase			:      Union[str, Any]			=			np.load(_SCREAMING_SNAKE_CASE		)

    if voice_preset is not None:
     self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      List[Any]			=			BatchFeature(data=_SCREAMING_SNAKE_CASE   ,  tensor_type=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[str]			=			self.tokenizer(
        _SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding="""max_length"""   ,  max_length=_SCREAMING_SNAKE_CASE   ,  return_attention_mask=_SCREAMING_SNAKE_CASE   ,  return_token_type_ids=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )

    if voice_preset is not None:
     UpperCAmelCase			:      List[Any]			=			voice_preset

    return encoded_text
