






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


__magic_name__:					Tuple							=     logging.get_logger(__name__)





class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					str											=   '''AutoTokenizer'''
	lowercase__						:					Dict											=   ['''tokenizer''']

	lowercase__						:					Union[str, Any]											=   {
	    '''semantic_prompt''': 1,
	    '''coarse_prompt''': 2,
	    '''fine_prompt''': 2,
	}
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None			)							->				Union[str, Any]:
							super().__init__(lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= speaker_embeddings
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       lowerCAmelCase__="speaker_embeddings_path.json"      ,       **lowerCAmelCase__			)							->				int:

							if speaker_embeddings_dict_path is not None:
													__magic_name__      :       Optional[Any]       						= get_file_from_repo(
													    lowerCAmelCase__      ,       lowerCAmelCase__      ,       subfolder=kwargs.pop("""subfolder"""      ,       lowerCAmelCase__			)      ,       cache_dir=kwargs.pop("""cache_dir"""      ,       lowerCAmelCase__			)      ,       force_download=kwargs.pop("""force_download"""      ,       lowerCAmelCase__			)      ,       proxies=kwargs.pop("""proxies"""      ,       lowerCAmelCase__			)      ,       resume_download=kwargs.pop("""resume_download"""      ,       lowerCAmelCase__			)      ,       local_files_only=kwargs.pop("""local_files_only"""      ,       lowerCAmelCase__			)      ,       use_auth_token=kwargs.pop("""use_auth_token"""      ,       lowerCAmelCase__			)      ,       revision=kwargs.pop("""revision"""      ,       lowerCAmelCase__			)      ,       )
													if speaker_embeddings_path is None:
																			logger.warning(
																			    F'`{os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__			)}` 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`.'			)
																			__magic_name__      :       Union[str, Any]       						= None
													else:
																			with open(lowerCAmelCase__			) as speaker_embeddings_json:
																									__magic_name__      :       List[str]       						= json.load(lowerCAmelCase__			)
							else:
													__magic_name__      :       List[str]       						= None

							__magic_name__      :       str       						= AutoTokenizer.from_pretrained(lowerCAmelCase__      ,       **lowerCAmelCase__			)

							return cls(tokenizer=lowerCAmelCase__      ,       speaker_embeddings=lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__="speaker_embeddings_path.json"      ,       lowerCAmelCase__="speaker_embeddings"      ,       lowerCAmelCase__ = False      ,       **lowerCAmelCase__      ,       )							->				Optional[Any]:
							if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(lowerCAmelCase__      ,       lowerCAmelCase__      ,       """v2"""			)      ,       exist_ok=lowerCAmelCase__			)

													__magic_name__      :       str       						= {}

													__magic_name__      :       Tuple       						= save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									__magic_name__      :       Optional[int]       						= self._load_voice_preset(lowerCAmelCase__			)

																									__magic_name__      :       Union[str, Any]       						= {}
																									for key in self.speaker_embeddings[prompt_key]:
																															np.save(
																															    os.path.join(
																															        embeddings_dict["""repo_or_path"""]      ,       lowerCAmelCase__      ,       F'{prompt_key}_{key}'			)      ,       voice_preset[key]      ,       allow_pickle=lowerCAmelCase__      ,       )
																															__magic_name__      :       Union[str, Any]       						= os.path.join(lowerCAmelCase__      ,       F'{prompt_key}_{key}.npy'			)

																									__magic_name__      :       Tuple       						= tmp_dict

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

							super().save_pretrained(lowerCAmelCase__      ,       lowerCAmelCase__      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__ = None      ,       **lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       Dict       						= self.speaker_embeddings[voice_preset]

							__magic_name__      :       Optional[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}].'			)

													__magic_name__      :       Union[str, Any]       						= get_file_from_repo(
													    self.speaker_embeddings.get("""repo_or_path"""      ,       """/"""			)      ,       voice_preset_paths[key]      ,       subfolder=kwargs.pop("""subfolder"""      ,       lowerCAmelCase__			)      ,       cache_dir=kwargs.pop("""cache_dir"""      ,       lowerCAmelCase__			)      ,       force_download=kwargs.pop("""force_download"""      ,       lowerCAmelCase__			)      ,       proxies=kwargs.pop("""proxies"""      ,       lowerCAmelCase__			)      ,       resume_download=kwargs.pop("""resume_download"""      ,       lowerCAmelCase__			)      ,       local_files_only=kwargs.pop("""local_files_only"""      ,       lowerCAmelCase__			)      ,       use_auth_token=kwargs.pop("""use_auth_token"""      ,       lowerCAmelCase__			)      ,       revision=kwargs.pop("""revision"""      ,       lowerCAmelCase__			)      ,       )
													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.'			)

													__magic_name__      :       int       						= np.load(lowerCAmelCase__			)

							return voice_preset_dict
	def       __magic_name__    (    self      ,       lowerCAmelCase__ = None			)							->				Dict:
							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      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__="pt"      ,       lowerCAmelCase__=2_56      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=False      ,       **lowerCAmelCase__      ,       )							->				Optional[Any]:
							if voice_preset is not None and not isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			):
													if (
													    isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			)
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																			__magic_name__      :       int       						= self._load_voice_preset(lowerCAmelCase__			)

													else:
																			if isinstance(lowerCAmelCase__      ,       lowerCAmelCase__			) and not voice_preset.endswith(""".npz"""			):
																									__magic_name__      :       Union[str, Any]       						= voice_preset + """.npz"""

																			__magic_name__      :       Tuple       						= np.load(lowerCAmelCase__			)

							if voice_preset is not None:
													self._validate_voice_preset_dict(lowerCAmelCase__      ,       **lowerCAmelCase__			)
													__magic_name__      :       str       						= BatchFeature(data=lowerCAmelCase__      ,       tensor_type=lowerCAmelCase__			)

							__magic_name__      :       Optional[int]       						= self.tokenizer(
							    lowerCAmelCase__      ,       return_tensors=lowerCAmelCase__      ,       padding="""max_length"""      ,       max_length=lowerCAmelCase__      ,       return_attention_mask=lowerCAmelCase__      ,       return_token_type_ids=lowerCAmelCase__      ,       add_special_tokens=lowerCAmelCase__      ,       **lowerCAmelCase__      ,       )

							if voice_preset is not None:
													__magic_name__      :       Tuple       						= voice_preset

							return encoded_text








from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return math.sqrt(sum(pow(a - b,   2			) for a, b in zip(_A,   _A			)			)			)

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if dataset.ndim != value_array.ndim:
												__magic_name__      :       str       						= (
												    """Wrong input data's dimensions... """
												    f'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
												)
												raise ValueError(_A			)

						try:
												if dataset.shape[1] != value_array.shape[1]:
																		__magic_name__      :       Optional[Any]       						= (
																		    """Wrong input data's shape... """
																		    f'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
																		)
																		raise ValueError(_A			)
						except IndexError:
												if dataset.ndim != value_array.ndim:
																		raise TypeError("""Wrong shape"""			)

						if dataset.dtype != value_array.dtype:
												__magic_name__      :       List[Any]       						= (
												    """Input data have different datatype... """
												    f'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
												)
												raise TypeError(_A			)

						__magic_name__      :       Dict       						= []

						for value in value_array:
												__magic_name__      :       Tuple       						= euclidean(_A,   dataset[0]			)
												__magic_name__      :       Any       						= dataset[0].tolist()

												for dataset_value in dataset[1:]:
																		__magic_name__      :       Any       						= euclidean(_A,   _A			)

																		if dist > temp_dist:
																								__magic_name__      :       Dict       						= temp_dist
																								__magic_name__      :       Any       						= dataset_value.tolist()

												answer.append([vector, dist]			)

						return answer

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						return np.dot(_A,   _A			) / (norm(_A			) * norm(_A			))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
