

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_     =			logging.get_logger(__name__)






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			lowerCamelCase__							='AutoTokenizer'
			lowerCamelCase__							=['tokenizer']

			lowerCamelCase__							={
			    'semantic_prompt': 1,
			    'coarse_prompt': 2,
			    'fine_prompt': 2,
			}
			def __init__(	self							:       Any					,					a							:       int					,					a							:       Any=None		)							->				List[Any]:

				"""simple docstring"""

				super().__init__(a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				speaker_embeddings
			@classmethod
			def 							__UpperCamelCase       (	cls							:       Optional[int]					,					a							:       Optional[Any]					,					a							:       Any="speaker_embeddings_path.json"					,					**a							:       List[str]		)							->				List[str]:

				"""simple docstring"""

				if speaker_embeddings_dict_path is not None:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				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`."		)
						SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
					else:
						with open(a		) as speaker_embeddings_json:
							SCREAMING_SNAKE_CASE      :					str    =				json.load(a		)
				else:
					SCREAMING_SNAKE_CASE      :					List[str]    =				None

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				AutoTokenizer.from_pretrained(a					,					**a		)

				return cls(tokenizer=a					,					speaker_embeddings=a		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       Tuple					,					a							:       Tuple="speaker_embeddings_path.json"					,					a							:       Union[str, Any]="speaker_embeddings"					,					a							:       bool = False					,					**a							:       List[str]					,					)							->				List[Any]:

				"""simple docstring"""

				if self.speaker_embeddings is not None:
					os.makedirs(os.path.join(a					,					a					,					"v2"		)					,					exist_ok=a		)

					SCREAMING_SNAKE_CASE      :					Any    =				{}

					SCREAMING_SNAKE_CASE      :					int    =				save_directory

					for prompt_key in self.speaker_embeddings:
						if prompt_key != "repo_or_path":
							SCREAMING_SNAKE_CASE      :					Tuple    =				self._load_voice_preset(a		)

							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{}
							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					,					)
								SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				os.path.join(a					,					F"{prompt_key}_{key}.npy"		)

							SCREAMING_SNAKE_CASE      :					int    =				tmp_dict

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

				super().save_pretrained(a					,					a					,					**a		)
			def 							__UpperCamelCase       (	self							:       List[str]					,					a							:       str = None					,					**a							:       str		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.speaker_embeddings[voice_preset]

				SCREAMING_SNAKE_CASE      :					Any    =				{}
				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}]."		)

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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."		)

					SCREAMING_SNAKE_CASE      :					Optional[int]    =				np.load(a		)

				return voice_preset_dict
			def 							__UpperCamelCase       (	self							:       Dict					,					a							:       Optional[dict] = None		)							->				List[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]					,					a							:       Dict=None					,					a							:       Dict=None					,					a							:       Tuple="pt"					,					a							:       List[Any]=256					,					a							:       Optional[Any]=False					,					a							:       Tuple=True					,					a							:       str=False					,					**a							:       List[str]					,					)							->				Tuple:

				"""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
					):
						SCREAMING_SNAKE_CASE      :					str    =				self._load_voice_preset(a		)

					else:
						if isinstance(a					,					a		) and not voice_preset.endswith(".npz"		):
							SCREAMING_SNAKE_CASE      :					List[str]    =				voice_preset + ".npz"

						SCREAMING_SNAKE_CASE      :					Dict    =				np.load(a		)

				if voice_preset is not None:
					self._validate_voice_preset_dict(a					,					**a		)
					SCREAMING_SNAKE_CASE      :					Dict    =				BatchFeature(data=a					,					tensor_type=a		)

				SCREAMING_SNAKE_CASE      :					str    =				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:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				voice_preset

				return encoded_text



"""simple docstring"""




import numpy as np

def        lowercase							(		lowerCAmelCase__ :     np.ndarray    ,					lowerCAmelCase__ :     float			) ->      np.ndarray:
		return np.where(vector > 0    ,					lowerCAmelCase__    ,					(alpha * (np.exp(lowerCAmelCase__			) - 1))			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






