



"""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


_UpperCamelCase					      =  logging.get_logger(__name__)







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      '''AutoTokenizer'''
    SCREAMING_SNAKE_CASE		=      ['''tokenizer''']

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






    def __init__(			self     ,A     ,A=None ):
          super().__init__(A )

          UpperCAmelCase          =	speaker_embeddings






    @classmethod
    def     _UpperCamelCase							(			cls     ,A     ,A="speaker_embeddings_path.json"     ,**A ):

          if speaker_embeddings_dict_path is not None:
                UpperCAmelCase          =	get_file_from_repo(
                    A     ,A     ,subfolder=kwargs.pop("""subfolder"""     ,A )     ,cache_dir=kwargs.pop("""cache_dir"""     ,A )     ,force_download=kwargs.pop("""force_download"""     ,A )     ,proxies=kwargs.pop("""proxies"""     ,A )     ,resume_download=kwargs.pop("""resume_download"""     ,A )     ,local_files_only=kwargs.pop("""local_files_only"""     ,A )     ,use_auth_token=kwargs.pop("""use_auth_token"""     ,A )     ,revision=kwargs.pop("""revision"""     ,A )     ,)
                if speaker_embeddings_path is None:
                      logger.warning(
                          F'''`{os.path.join(A     ,A )}` 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`.''' )
                      UpperCAmelCase          =	None
                else:
                      with open(A ) as speaker_embeddings_json:
                            UpperCAmelCase          =	json.load(A )
          else:
                UpperCAmelCase          =	None

          UpperCAmelCase          =	AutoTokenizer.from_pretrained(A     ,**A )

          return cls(tokenizer=A     ,speaker_embeddings=A )






    def     _UpperCamelCase							(			self     ,A     ,A="speaker_embeddings_path.json"     ,A="speaker_embeddings"     ,A = False     ,**A     ,):
          if self.speaker_embeddings is not None:
                os.makedirs(os.path.join(A     ,A     ,"""v2""" )     ,exist_ok=A )

                UpperCAmelCase          =	{}

                UpperCAmelCase          =	save_directory

                for prompt_key in self.speaker_embeddings:
                      if prompt_key != "repo_or_path":
                            UpperCAmelCase          =	self._load_voice_preset(A )

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

                            UpperCAmelCase          =	tmp_dict

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

          super().save_pretrained(A     ,A     ,**A )






    def     _UpperCamelCase							(			self     ,A = None     ,**A ):
          UpperCAmelCase          =	self.speaker_embeddings[voice_preset]

          UpperCAmelCase          =	{}
          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          =	get_file_from_repo(
                    self.speaker_embeddings.get("""repo_or_path"""     ,"""/""" )     ,voice_preset_paths[key]     ,subfolder=kwargs.pop("""subfolder"""     ,A )     ,cache_dir=kwargs.pop("""cache_dir"""     ,A )     ,force_download=kwargs.pop("""force_download"""     ,A )     ,proxies=kwargs.pop("""proxies"""     ,A )     ,resume_download=kwargs.pop("""resume_download"""     ,A )     ,local_files_only=kwargs.pop("""local_files_only"""     ,A )     ,use_auth_token=kwargs.pop("""use_auth_token"""     ,A )     ,revision=kwargs.pop("""revision"""     ,A )     ,)
                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.''' )

                UpperCAmelCase          =	np.load(A )

          return voice_preset_dict






    def     _UpperCamelCase							(			self     ,A = None ):
          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     ,A=None     ,A=None     ,A="pt"     ,A=256     ,A=False     ,A=True     ,A=False     ,**A     ,):
          if voice_preset is not None and not isinstance(A     ,A ):
                if (
                    isinstance(A     ,A )
                    and self.speaker_embeddings is not None
                    and voice_preset in self.speaker_embeddings
                ):
                      UpperCAmelCase          =	self._load_voice_preset(A )

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

                      UpperCAmelCase          =	np.load(A )

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

          UpperCAmelCase          =	self.tokenizer(
              A     ,return_tensors=A     ,padding="""max_length"""     ,max_length=A     ,return_attention_mask=A     ,return_token_type_ids=A     ,add_special_tokens=A     ,**A     ,)

          if voice_preset is not None:
                UpperCAmelCase          =	voice_preset

          return encoded_text











"""simple docstring"""







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  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

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

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

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

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

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



