'''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_     :						str    							=logging.get_logger(__name__)


class     __UpperCAmelCase       ( _SCREAMING_SNAKE_CASE		):
  __A :				Tuple											= """AutoTokenizer"""
  __A :				Optional[Any]											= ["""tokenizer"""]

  __A :				str											= {
      """semantic_prompt""": 1,
      """coarse_prompt""": 2,
      """fine_prompt""": 2,
  }





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=None ):
     super().__init__(__snake_case )

     lowerCAmelCase_			=      speaker_embeddings





  @classmethod
  def 						UpperCAmelCase_	(	cls    ,     _lowerCamelCase    ,     _lowerCamelCase="speaker_embeddings_path.json"    ,     **_lowerCamelCase ):





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

     lowerCAmelCase_			=      AutoTokenizer.from_pretrained(__snake_case    ,     **__snake_case )

     return cls(tokenizer=__snake_case    ,     speaker_embeddings=__snake_case )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase="speaker_embeddings_path.json"    ,     _lowerCamelCase="speaker_embeddings"    ,     _lowerCamelCase = False    ,     **_lowerCamelCase    ,     ):





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

        lowerCAmelCase_			=      {}

        lowerCAmelCase_			=      save_directory

        for prompt_key in self.speaker_embeddings:
           if prompt_key != "repo_or_path":
              lowerCAmelCase_			=      self._load_voice_preset(__snake_case )

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

              lowerCAmelCase_			=      tmp_dict

        with open(os.path.join(__snake_case    ,     __snake_case )    ,     '''w''' ) as fp:
           json.dump(__snake_case    ,     __snake_case )

     super().save_pretrained(__snake_case    ,     __snake_case    ,     **__snake_case )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase = None    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      self.speaker_embeddings[voice_preset]

     lowerCAmelCase_			=      {}
     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}].''' )

        lowerCAmelCase_			=      get_file_from_repo(
            self.speaker_embeddings.get('''repo_or_path'''    ,     '''/''' )    ,     voice_preset_paths[key]    ,     subfolder=kwargs.pop('''subfolder'''    ,     __snake_case )    ,     cache_dir=kwargs.pop('''cache_dir'''    ,     __snake_case )    ,     force_download=kwargs.pop('''force_download'''    ,     __snake_case )    ,     proxies=kwargs.pop('''proxies'''    ,     __snake_case )    ,     resume_download=kwargs.pop('''resume_download'''    ,     __snake_case )    ,     local_files_only=kwargs.pop('''local_files_only'''    ,     __snake_case )    ,     use_auth_token=kwargs.pop('''use_auth_token'''    ,     __snake_case )    ,     revision=kwargs.pop('''revision'''    ,     __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.''' )

        lowerCAmelCase_			=      np.load(__snake_case )

     return voice_preset_dict





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase = 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    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase="pt"    ,     _lowerCamelCase=256    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     **_lowerCamelCase    ,     ):





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

        else:
           if isinstance(__snake_case    ,     __snake_case ) and not voice_preset.endswith('''.npz''' ):
              lowerCAmelCase_			=      voice_preset + '''.npz'''

           lowerCAmelCase_			=      np.load(__snake_case )

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

     lowerCAmelCase_			=      self.tokenizer(
         __snake_case    ,     return_tensors=__snake_case    ,     padding='''max_length'''    ,     max_length=__snake_case    ,     return_attention_mask=__snake_case    ,     return_token_type_ids=__snake_case    ,     add_special_tokens=__snake_case    ,     **__snake_case    ,     )

     if voice_preset is not None:
        lowerCAmelCase_			=      voice_preset

     return encoded_text












import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						int							=   (KDPMaDiscreteScheduler,)
  __lowerCAmelCase   :						Union[str, Any]							=   10


  def 						__UpperCAmelCase     (							self ,**__snake_case ):



       """simple docstring"""





       A_          =       {
           '''num_train_timesteps''': 1_1_0_0,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__snake_case )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,[0.0002, 0.002, 0.02] ):
            self.check_over_configs(beta_start=__snake_case ,beta_end=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config(prediction_type='''v_prediction''' )
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
            assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
            assert abs(result_mean.item() - 0.0002 ) < 1E-3


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter * scheduler.init_noise_sigma
       A_          =       sample.to(__snake_case )

       for i, t in enumerate(scheduler.timesteps ):
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       if torch_device == "mps":
            return
       A_          =       self.scheduler_classes[0]
       A_          =       self.get_scheduler_config()
       A_          =       scheduler_class(**__snake_case )

       scheduler.set_timesteps(self.num_inference_steps ,device=__snake_case )

       A_          =       self.dummy_model()
       A_          =       self.dummy_sample_deter.to(__snake_case ) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            A_          =       scheduler.scale_model_input(__snake_case ,__snake_case )

            A_          =       model(__snake_case ,__snake_case )

            A_          =       scheduler.step(__snake_case ,__snake_case ,__snake_case )
            A_          =       output.prev_sample

       A_          =       torch.sum(torch.abs(__snake_case ) )
       A_          =       torch.mean(torch.abs(__snake_case ) )

       if str(__snake_case ).startswith('''cpu''' ):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125 ) < 1E-2
            assert abs(result_mean.item() - 0.0266 ) < 1E-3



