






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					:		Optional[Any]	  =						logging.get_logger(__name__)


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "AutoTokenizer"
							_UpperCAmelCase     =       ["tokenizer"]

							_UpperCAmelCase     =       {
							    "semantic_prompt": 1,
							    "coarse_prompt": 2,
							    "fine_prompt": 2,
							}
							def __init__(     self:      int       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Tuple=None						)		->		Union[str, Any]:
											super().__init__(UpperCamelCase						)

											snake_case__									=     speaker_embeddings
							@classmethod
							def lowerCAmelCase_ (     cls:      List[str]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]="speaker_embeddings_path.json"       ,	**UpperCamelCase:      int						)		->		str:

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

											snake_case__									=     AutoTokenizer.from_pretrained(UpperCamelCase       ,	**UpperCamelCase						)

											return cls(tokenizer=UpperCamelCase       ,	speaker_embeddings=UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      List[Any]       ,	UpperCamelCase:      str="speaker_embeddings_path.json"       ,	UpperCamelCase:      Union[str, Any]="speaker_embeddings"       ,	UpperCamelCase:      bool = False       ,	**UpperCamelCase:      Tuple       ,	)		->		Dict:
											if self.speaker_embeddings is not None:
															os.makedirs(os.path.join(UpperCamelCase       ,	UpperCamelCase       ,	'v2'						)       ,	exist_ok=UpperCamelCase						)

															snake_case__									=     {}

															snake_case__									=     save_directory

															for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																							snake_case__									=     self._load_voice_preset(UpperCamelCase						)

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

																							snake_case__									=     tmp_dict

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

											super().save_pretrained(UpperCamelCase       ,	UpperCamelCase       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      str = None       ,	**UpperCamelCase:      Union[str, Any]						)		->		int:
											snake_case__									=     self.speaker_embeddings[voice_preset]

											snake_case__									=     {}
											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}].'''						)

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

															snake_case__									=     np.load(UpperCamelCase						)

											return voice_preset_dict
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      Optional[dict] = None						)		->		List[str]:
											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:      Dict       ,	UpperCamelCase:      Optional[int]=None       ,	UpperCamelCase:      Union[str, Any]=None       ,	UpperCamelCase:      List[Any]="pt"       ,	UpperCamelCase:      List[Any]=2_56       ,	UpperCamelCase:      Optional[int]=False       ,	UpperCamelCase:      Optional[Any]=True       ,	UpperCamelCase:      Optional[Any]=False       ,	**UpperCamelCase:      int       ,	)		->		Optional[int]:
											if voice_preset is not None and not isinstance(UpperCamelCase       ,	UpperCamelCase						):
															if (
															    isinstance(UpperCamelCase       ,	UpperCamelCase						)
															    and self.speaker_embeddings is not None
															    and voice_preset in self.speaker_embeddings
															):
																			snake_case__									=     self._load_voice_preset(UpperCamelCase						)

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

																			snake_case__									=     np.load(UpperCamelCase						)

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

											snake_case__									=     self.tokenizer(
											    UpperCamelCase       ,	return_tensors=UpperCamelCase       ,	padding='max_length'       ,	max_length=UpperCamelCase       ,	return_attention_mask=UpperCamelCase       ,	return_token_type_ids=UpperCamelCase       ,	add_special_tokens=UpperCamelCase       ,	**UpperCamelCase       ,	)

											if voice_preset is not None:
															snake_case__									=     voice_preset

											return encoded_text












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




