




"""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


lowerCAmelCase							:			Union[str, Any]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__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 )

							lowerCamelCase			      =					speaker_embeddings





				@classmethod
				def    _lowerCAmelCase				(       cls		, _a		, _a="speaker_embeddings_path.json"		, **_a ):





							"""simple docstring"""

							if speaker_embeddings_dict_path is not None:
										lowerCamelCase			      =					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\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(_a ) as speaker_embeddings_json:
																lowerCamelCase			      =					json.load(_a )
							else:
										lowerCamelCase			      =					None

							lowerCamelCase			      =					AutoTokenizer.from_pretrained(_a		, **_a )

							return cls(tokenizer=_a		, speaker_embeddings=_a )





				def    _lowerCAmelCase				(       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 )

										lowerCamelCase			      =					{}

										lowerCamelCase			      =					save_directory

										for prompt_key in self.speaker_embeddings:
													if prompt_key != "repo_or_path":
																lowerCamelCase			      =					self._load_voice_preset(_a )

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

																lowerCamelCase			      =					tmp_dict

										with open(os.path.join(_a		, _a )		, """w""" ) as fp:
													json.dump(_a		, _a )

							super().save_pretrained(_a		, _a		, **_a )





				def    _lowerCAmelCase				(       self		, _a = None		, **_a ):





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

										lowerCamelCase			      =					np.load(_a )

							return voice_preset_dict





				def    _lowerCAmelCase				(       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=256		, _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
										):
													lowerCamelCase			      =					self._load_voice_preset(_a )

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

													lowerCamelCase			      =					np.load(_a )

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

							lowerCamelCase			      =					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:
										lowerCamelCase			      =					voice_preset

							return encoded_text









"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



