





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				:    List[str]       =   logging.get_logger(__name__)

class 	A_       (a_			):
	UpperCAmelCase__					=			'''AutoTokenizer'''
	UpperCAmelCase__					=			['''tokenizer''']

	UpperCAmelCase__					=			{
	    '''semantic_prompt''': 1,
	    '''coarse_prompt''': 2,
	    '''fine_prompt''': 2,
	}
	def __init__(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				super().__init__(_A		)

				UpperCAmelCase   		=				speaker_embeddings
	@classmethod
	def      _lowercase				(    cls							,					_A							,					_A="speaker_embeddings_path.json"							,					**_A		):






				'''simple docstring'''







				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      _lowercase				(    self							,					_A							,					_A="speaker_embeddings_path.json"							,					_A="speaker_embeddings"							,					_A = False							,					**_A							,					):






				'''simple docstring'''







				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      _lowercase				(    self							,					_A = None							,					**_A		):






				'''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'''							,					_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      _lowercase				(    self							,					_A = None		):






				'''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							,					_A=None							,					_A=None							,					_A="pt"							,					_A=2_5_6							,					_A=False							,					_A=True							,					_A=False							,					**_A							,					):






				'''simple docstring'''







				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










def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			exponent // 2			,			UpperCamelCase__ ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase__			,			exponent - 1			,			UpperCamelCase__ )) % modulo_value





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ = 1777			,			UpperCamelCase__ = 1855			,			UpperCamelCase__ = 8 )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				base
			for _ in range(1			,			UpperCamelCase__ ):
						UpperCAmelCase   		=				_modexpt(UpperCamelCase__			,			UpperCamelCase__			,			10**digits )

			return result


if __name__ == "__main__":
				print(F'{solution() = }')



