





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 							_UpperCamelCase					(	lowerCAmelCase_     ):
							_UpperCamelCase				:					List[str]         =   '''AutoTokenizer'''
							_UpperCamelCase				:					Any         =   ['''tokenizer''']

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

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




														"""simple docstring"""


														super().__init__(_SCREAMING_SNAKE_CASE		)

														UpperCamelCase_          =  speaker_embeddings

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




														"""simple docstring"""


														if speaker_embeddings_dict_path is not None:
																					UpperCamelCase_          =  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
                    , 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(_SCREAMING_SNAKE_CASE		) as speaker_embeddings_json:
																																			UpperCamelCase_          =  json.load(_SCREAMING_SNAKE_CASE		)
														else:
																					UpperCamelCase_          =  None

														UpperCamelCase_          =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

														return cls(tokenizer=_SCREAMING_SNAKE_CASE					,   speaker_embeddings=_SCREAMING_SNAKE_CASE		)

							def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Optional[Any]="speaker_embeddings_path.json"					,   _SCREAMING_SNAKE_CASE: int="speaker_embeddings"					,   _SCREAMING_SNAKE_CASE: bool = False					,   **_SCREAMING_SNAKE_CASE: Optional[Any]					,   )       -> Optional[int]:




														"""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_          =  {}

																					UpperCamelCase_          =  save_directory

																					for prompt_key in self.speaker_embeddings:
																												if prompt_key != "repo_or_path":
																																			UpperCamelCase_          =  self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

																																			UpperCamelCase_          =  {}
																																			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_          =  os.path.join(_SCREAMING_SNAKE_CASE					,   f'''{prompt_key}_{key}.npy'''		)

																																			UpperCamelCase_          =  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 lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: str = None					,   **_SCREAMING_SNAKE_CASE: List[Any]		)       -> List[str]:




														"""simple docstring"""


														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"					,   _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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''		)

																					UpperCamelCase_          =  np.load(_SCREAMING_SNAKE_CASE		)

														return voice_preset_dict

							def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[dict] = None		)       -> Optional[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					,   _SCREAMING_SNAKE_CASE: str=None					,   _SCREAMING_SNAKE_CASE: Dict=None					,   _SCREAMING_SNAKE_CASE: str="pt"					,   _SCREAMING_SNAKE_CASE: str=256					,   _SCREAMING_SNAKE_CASE: List[str]=False					,   _SCREAMING_SNAKE_CASE: List[Any]=True					,   _SCREAMING_SNAKE_CASE: Tuple=False					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> Any:




														"""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_          =  self._load_voice_preset(_SCREAMING_SNAKE_CASE		)

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

																												UpperCamelCase_          =  np.load(_SCREAMING_SNAKE_CASE		)

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

														UpperCamelCase_          =  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_          =  voice_preset

														return encoded_text








import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory




def  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

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




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

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




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

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




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("No executable batch size found, reached zero."					,   cm.exception.args[0]		)

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




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "hello"					,   "world"		)
                     self.assertIn("Batch size was passed into `f`"					,   cm.exception.args[0]		)
                     self.assertIn("`f(arg1='hello', arg2='world')"					,   cm.exception.args[0]		)

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




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function()
                     self.assertIn("Oops, we had an error!"					,   cm.exception.args[0]		)



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

