



'''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


UpperCAmelCase     		=   logging.get_logger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		"AutoTokenizer"
						UpperCAmelCase							:   List[str]									=		["tokenizer"]

						UpperCAmelCase							:   Optional[int]									=		{
						    "semantic_prompt": 1,
						    "coarse_prompt": 2,
						    "fine_prompt": 2,
						}


						def __init__( self     ,	A_     ,	A_=None				)     ->     List[str]:
							super().__init__(A_				)

							lowerCAmelCase   =     speaker_embeddings


						@classmethod
						def     __snake_case   ( cls     ,	A_     ,	A_="speaker_embeddings_path.json"     ,	**A_				)     ->     Tuple:

							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     __snake_case   ( self     ,	A_     ,	A_="speaker_embeddings_path.json"     ,	A_="speaker_embeddings"     ,	A_ = False     ,	**A_     ,	)     ->     List[str]:
							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     __snake_case   ( self     ,	A_ = None     ,	**A_				)     ->     Optional[int]:
							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     __snake_case   ( self     ,	A_ = None				)     ->     Optional[int]:
							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_     ,	)     ->     str:
							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'''

class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     None:
							lowerCAmelCase   =     {}  # Mapping from char to TrieNode
							lowerCAmelCase   =     False


						def     __snake_case   ( self     ,	A_				)     ->     None:
							for word in words:
								self.insert(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									lowerCAmelCase   =     TrieNode()
								lowerCAmelCase   =     curr.nodes[char]
							lowerCAmelCase   =     True


						def     __snake_case   ( self     ,	A_				)     ->     bool:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									return False
								lowerCAmelCase   =     curr.nodes[char]
							return curr.is_leaf




						def     __snake_case   ( self     ,	A_				)     ->     None:

							def _delete(A_     ,	A_     ,	A_				) -> bool:
								if index == len(A_				):
									# If word does not exist
									if not curr.is_leaf:
										return False
									lowerCAmelCase   =     False
									return len(curr.nodes				) == 0
								lowerCAmelCase   =     word[index]
								lowerCAmelCase   =     curr.nodes.get(A_				)
								# If char not in current trie node
								if not char_node:
									return False
								# Flag to check if node can be deleted
								lowerCAmelCase   =     _delete(A_     ,	A_     ,	index + 1				)
								if delete_curr:
									del curr.nodes[char]
									return len(curr.nodes				) == 0
								return delete_curr

							_delete(self     ,	A_     ,	0				)







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						TrieNode							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	if node.is_leaf:
		print(_SCREAMING_SNAKE_CASE							,		end=""" """							)

	for key, value in node.nodes.items():
		print_words(_SCREAMING_SNAKE_CASE							,		word + key							)






def 		_snake_case		( )      ->						bool:







	"""simple docstring"""
	lowerCAmelCase   =     """banana bananas bandana band apple all beast""".split()
	lowerCAmelCase   =     TrieNode()
	root.insert_many(_SCREAMING_SNAKE_CASE							)
	# print_words(root, "")
	assert all(root.find(_SCREAMING_SNAKE_CASE							) for word in words							)
	assert root.find("""banana"""							)
	assert not root.find("""bandanas"""							)
	assert not root.find("""apps"""							)
	assert root.find("""apple"""							)
	assert root.find("""all"""							)
	root.delete("""all"""							)
	assert not root.find("""all"""							)
	root.delete("""banana"""							)
	assert not root.find("""banana"""							)
	assert root.find("""bananas"""							)
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						bool							)      ->						None:







	"""simple docstring"""
	print(str(_SCREAMING_SNAKE_CASE							)							,		"""works!""" if passes else """doesn't work :("""							)






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	assert test_trie()






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	print_results("""Testing trie functionality"""							,		test_trie()							)


if __name__ == "__main__":
				main()