




'''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


_snake_case : Any  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							'AutoTokenizer'
							lowercase_							     =							['tokenizer']

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







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


														"""simple docstring"""


														super().__init__(lowerCAmelCase_   )

														_a      	=							speaker_embeddings







							@classmethod
							def   __lowerCAmelCase     (    cls			:    Any				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    List[Any]="speaker_embeddings_path.json"				,     **lowerCAmelCase_			:    Any   )     ->				str:


														"""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\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`.'   )
																												_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   __lowerCAmelCase     (    self			:    Optional[int]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Optional[Any]="speaker_embeddings_path.json"				,     lowerCAmelCase_			:    Dict="speaker_embeddings"				,     lowerCAmelCase_			:    bool = False				,     **lowerCAmelCase_			:    List[str]				,     )     ->				str:


														"""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   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    str = None				,     **lowerCAmelCase_			:    Union[str, Any]   )     ->				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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'   )

																					_a      	=							np.load(lowerCAmelCase_   )

														return voice_preset_dict







							def   __lowerCAmelCase     (    self			:    int				,     lowerCAmelCase_			:    Optional[dict] = None   )     ->				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			:    Union[str, Any]				,     lowerCAmelCase_			:    int=None				,     lowerCAmelCase_			:    Union[str, Any]=None				,     lowerCAmelCase_			:    Dict="pt"				,     lowerCAmelCase_			:    Dict=2_56				,     lowerCAmelCase_			:    Tuple=False				,     lowerCAmelCase_			:    Union[str, Any]=True				,     lowerCAmelCase_			:    Union[str, Any]=False				,     **lowerCAmelCase_			:    int				,     )     ->				str:


														"""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







'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (						_a       ):
							lowercase_							     =							(DDPMParallelScheduler,)







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 10_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														    '''variance_type''': '''fixed_small''',
														    '''clip_sample''': True,
														}

														config.update(**lowerCAmelCase_   )
														return config







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														for timesteps in [1, 5, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				,     [0.0_0_2, 0.0_2, 0.2, 2]   ):
																					self.check_over_configs(beta_start=lowerCAmelCase_				,     beta_end=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				Optional[Any]:


														"""simple docstring"""


														for variance in ["fixed_small", "fixed_large", "other"]:
																					self.check_over_configs(variance_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[Any]:


														"""simple docstring"""


														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														self.check_over_configs(thresholding=lowerCAmelCase_   )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "sample", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCAmelCase_				,     prediction_type=lowerCAmelCase_				,     sample_max_value=lowerCAmelCase_				,     )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				str:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    str   )     ->				List[str]:


														"""simple docstring"""


														for t in [0, 5_00, 9_99]:
																					self.check_over_forward(time_step=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(4_87   ) - 0.0_0_9_7_9   )   ) < 1e-5
														assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.0_2   )   ) < 1e-5







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							self.dummy_sample_deter + 0.1
														_a      	=							self.dummy_sample_deter - 0.1

														_a      	=							samplea.shape[0]
														_a      	=							torch.stack([samplea, samplea, samplea]				,     dim=0   )
														_a      	=							torch.arange(lowerCAmelCase_   )[0:3, None].repeat(1				,     lowerCAmelCase_   )

														_a      	=							model(samples.flatten(0				,     1   )				,     timesteps.flatten(0				,     1   )   )
														_a      	=							scheduler.batch_step_no_noise(lowerCAmelCase_				,     timesteps.flatten(0				,     1   )				,     samples.flatten(0				,     1   )   )

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 1_1_5_3.1_8_3_3   ) < 1e-2
														assert abs(result_mean.item() - 0.5_0_0_5   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
														assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							len(lowerCAmelCase_   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter
														_a      	=							torch.manual_seed(0   )

														for t in reversed(range(lowerCAmelCase_   )   ):
																					# 1. predict noise residual
																					_a      	=							model(lowerCAmelCase_				,     lowerCAmelCase_   )

																					# 2. predict previous mean of sample x_t-1
																					_a      	=							scheduler.step(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     generator=lowerCAmelCase_   ).prev_sample

																					_a      	=							pred_prev_sample

														_a      	=							torch.sum(torch.abs(lowerCAmelCase_   )   )
														_a      	=							torch.mean(torch.abs(lowerCAmelCase_   )   )

														assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
														assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    int   )     ->				Dict:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]

														scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

														_a      	=							scheduler.timesteps

														for i, timestep in enumerate(lowerCAmelCase_   ):
																					if i == len(lowerCAmelCase_   ) - 1:
																												_a      	=							-1
																					else:
																												_a      	=							timesteps[i + 1]

																					_a      	=							scheduler.previous_timestep(lowerCAmelCase_   )
																					_a      	=							prev_t.item()

																					self.assertEqual(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 51, 0]

														with self.assertRaises(lowerCAmelCase_				,     msg='''`custom_timesteps` must be in descending order.'''   ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[1_00, 87, 50, 1, 0]
														_a      	=							len(lowerCAmelCase_   )

														with self.assertRaises(lowerCAmelCase_				,     msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
																					scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_				,     timesteps=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Any:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**lowerCAmelCase_   )

														_a      	=							[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    lowerCAmelCase_				,     msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,     ):
																					scheduler.set_timesteps(timesteps=lowerCAmelCase_   )

