



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_		:     Optional[int]        =     logging.get_logger(__name__)



class 		_lowercase		(       _UpperCamelCase	):
   _UpperCAmelCase     	=			'''AutoTokenizer'''
   _UpperCAmelCase     	=			['''tokenizer''']

   _UpperCAmelCase     	=			{
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }

   def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Dict=None							)       ->				List[str]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							)

    a						=		speaker_embeddings

   @classmethod
   def 		A							(      cls			:       Optional[int]							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Tuple="speaker_embeddings_path.json"							,   **__lowerCAmelCase			:       Dict							)       ->				Dict:


    """simple docstring"""






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

    a						=		AutoTokenizer.from_pretrained(__lowerCAmelCase							,   **__lowerCAmelCase							)

    return cls(tokenizer=__lowerCAmelCase							,   speaker_embeddings=__lowerCAmelCase							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       List[Any]="speaker_embeddings_path.json"							,   __lowerCAmelCase			:       str="speaker_embeddings"							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Tuple							,   )       ->				Union[str, Any]:


    """simple docstring"""






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

     a						=		{}

     a						=		save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       a						=		self._load_voice_preset(__lowerCAmelCase							)

       a						=		{}
       for key in self.speaker_embeddings[prompt_key]:
        np.save(
            os.path.join(
                embeddings_dict["repo_or_path"]							,   __lowerCAmelCase							,   f"""{prompt_key}_{key}"""							)							,   voice_preset[key]							,   allow_pickle=__lowerCAmelCase							,   )
        a						=		os.path.join(__lowerCAmelCase							,   f"""{prompt_key}_{key}.npy"""							)

       a						=		tmp_dict

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

    super().save_pretrained(__lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       str = None							,   **__lowerCAmelCase			:       int							)       ->				Optional[Any]:


    """simple docstring"""






    a						=		self.speaker_embeddings[voice_preset]

    a						=		{}
    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}]."""							)

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

     a						=		np.load(__lowerCAmelCase							)

    return voice_preset_dict

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Optional[dict] = None							)       ->				List[Any]:


    """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			:       Any							,   __lowerCAmelCase			:       int=None							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Dict="pt"							,   __lowerCAmelCase			:       str=256							,   __lowerCAmelCase			:       Optional[Any]=False							,   __lowerCAmelCase			:       List[Any]=True							,   __lowerCAmelCase			:       List[str]=False							,   **__lowerCAmelCase			:       List[str]							,   )       ->				Tuple:


    """simple docstring"""






    if voice_preset is not None and not isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
     if (
         isinstance(__lowerCAmelCase							,   __lowerCAmelCase							)
         and self.speaker_embeddings is not None
         and voice_preset in self.speaker_embeddings
     ):
      a						=		self._load_voice_preset(__lowerCAmelCase							)

     else:
      if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) and not voice_preset.endswith(".npz"							):
       a						=		voice_preset + ".npz"

      a						=		np.load(__lowerCAmelCase							)

    if voice_preset is not None:
     self._validate_voice_preset_dict(__lowerCAmelCase							,   **__lowerCAmelCase							)
     a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)

    a						=		self.tokenizer(
        __lowerCAmelCase							,   return_tensors=__lowerCAmelCase							,   padding="max_length"							,   max_length=__lowerCAmelCase							,   return_attention_mask=__lowerCAmelCase							,   return_token_type_ids=__lowerCAmelCase							,   add_special_tokens=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    if voice_preset is not None:
     a						=		voice_preset

    return encoded_text






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       UNetaDModel							,   __lowerCAmelCase			:       ScoreSdeVeScheduler							)       ->				str:


    """simple docstring"""






    super().__init__()
    self.register_modules(unet=__lowerCAmelCase							,   scheduler=__lowerCAmelCase							)




   @torch.no_grad()
   def __call__(      self			:       int							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 2000							,   __lowerCAmelCase			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   __lowerCAmelCase			:       Optional[str] = "pil"							,   __lowerCAmelCase			:       bool = True							,   **__lowerCAmelCase			:       Any							,   )       ->				Union[ImagePipelineOutput, Tuple]:


    """simple docstring"""






    a						=		self.unet.config.sample_size
    a						=		(batch_size, 3, img_size, img_size)

    a						=		self.unet

    a						=		randn_tensor(__lowerCAmelCase							,   generator=__lowerCAmelCase							) * self.scheduler.init_noise_sigma
    a						=		sample.to(self.device							)

    self.scheduler.set_timesteps(__lowerCAmelCase							)
    self.scheduler.set_sigmas(__lowerCAmelCase							)

    for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
     a						=		self.scheduler.sigmas[i] * torch.ones(shape[0]							,   device=self.device							)

     # correction step
     for _ in range(self.scheduler.config.correct_steps							):
      a						=		self.unet(__lowerCAmelCase							,   __lowerCAmelCase							).sample
      a						=		self.scheduler.step_correct(__lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							).prev_sample

     # prediction step
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							).sample
     a						=		self.scheduler.step_pred(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)

     a		,  a						=		output.prev_sample, output.prev_sample_mean

    a						=		sample_mean.clamp(0							,   1							)
    a						=		sample.cpu().permute(0							,   2							,   3							,   1							).numpy()
    if output_type == "pil":
     a						=		self.numpy_to_pil(__lowerCAmelCase							)

    if not return_dict:
     return (sample,)

    return ImagePipelineOutput(images=__lowerCAmelCase							)

