"""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__           =	logging.get_logger(__name__)




class 				A__ (			_lowerCamelCase):
		A_		: Optional[int]            =			'AutoTokenizer'
		A_		: Optional[int]            =			['tokenizer']

		A_		: Optional[int]            =			{
		    'semantic_prompt': 1,
		    'coarse_prompt': 2,
		    'fine_prompt': 2,
		}

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=None				):
			super().__init__(_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Tuple		    =   speaker_embeddings

		@classmethod
		def 							__lowerCamelCase							(   cls ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="speaker_embeddings_path.json" ,	**_SCREAMING_SNAKE_CASE				):

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

			__lowerCAmelCase    :		List[Any]		    =   AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

			return cls(tokenizer=_SCREAMING_SNAKE_CASE ,	speaker_embeddings=_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE="speaker_embeddings_path.json" ,	_SCREAMING_SNAKE_CASE="speaker_embeddings" ,	_SCREAMING_SNAKE_CASE = False ,	**_SCREAMING_SNAKE_CASE ,	):
			if self.speaker_embeddings is not None:
				os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	'v2'				) ,	exist_ok=_SCREAMING_SNAKE_CASE				)

				__lowerCAmelCase    :		str		    =   {}

				__lowerCAmelCase    :		Optional[Any]		    =   save_directory

				for prompt_key in self.speaker_embeddings:
					if prompt_key != "repo_or_path":
						__lowerCAmelCase    :		Union[str, Any]		    =   self._load_voice_preset(_SCREAMING_SNAKE_CASE				)

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

						__lowerCAmelCase    :		Union[str, Any]		    =   tmp_dict

				with open(os.path.join(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) ,	'w'				) as fp:
					json.dump(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

			super().save_pretrained(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE = None ,	**_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Optional[int]		    =   self.speaker_embeddings[voice_preset]

			__lowerCAmelCase    :		Dict		    =   {}
			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    :		List[Any]		    =   get_file_from_repo(
				    self.speaker_embeddings.get('repo_or_path' ,	'/'				) ,	voice_preset_paths[key] ,	subfolder=kwargs.pop('subfolder' ,	_SCREAMING_SNAKE_CASE				) ,	cache_dir=kwargs.pop('cache_dir' ,	_SCREAMING_SNAKE_CASE				) ,	force_download=kwargs.pop('force_download' ,	_SCREAMING_SNAKE_CASE				) ,	proxies=kwargs.pop('proxies' ,	_SCREAMING_SNAKE_CASE				) ,	resume_download=kwargs.pop('resume_download' ,	_SCREAMING_SNAKE_CASE				) ,	local_files_only=kwargs.pop('local_files_only' ,	_SCREAMING_SNAKE_CASE				) ,	use_auth_token=kwargs.pop('use_auth_token' ,	_SCREAMING_SNAKE_CASE				) ,	revision=kwargs.pop('revision' ,	_SCREAMING_SNAKE_CASE				) ,	)
				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    :		Any		    =   np.load(_SCREAMING_SNAKE_CASE				)

			return voice_preset_dict

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE = None				):
			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 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="pt" ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				if (
				    isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
				    and self.speaker_embeddings is not None
				    and voice_preset in self.speaker_embeddings
				):
					__lowerCAmelCase    :		Dict		    =   self._load_voice_preset(_SCREAMING_SNAKE_CASE				)

				else:
					if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				) and not voice_preset.endswith('.npz'				):
						__lowerCAmelCase    :		Any		    =   voice_preset + '.npz'

					__lowerCAmelCase    :		Optional[Any]		    =   np.load(_SCREAMING_SNAKE_CASE				)

			if voice_preset is not None:
				self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   BatchFeature(data=_SCREAMING_SNAKE_CASE ,	tensor_type=_SCREAMING_SNAKE_CASE				)

			__lowerCAmelCase    :		Any		    =   self.tokenizer(
			    _SCREAMING_SNAKE_CASE ,	return_tensors=_SCREAMING_SNAKE_CASE ,	padding='max_length' ,	max_length=_SCREAMING_SNAKE_CASE ,	return_attention_mask=_SCREAMING_SNAKE_CASE ,	return_token_type_ids=_SCREAMING_SNAKE_CASE ,	add_special_tokens=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			if voice_preset is not None:
				__lowerCAmelCase    :		str		    =   voice_preset

			return encoded_text

"""simple docstring"""







class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   0
			__lowerCAmelCase    :		Any		    =   0
			__lowerCAmelCase    :		List[Any]		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			if vertex not in self.adjacency:
				__lowerCAmelCase    :		Dict		    =   {}
				self.num_vertices += 1

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			self.add_vertex(_SCREAMING_SNAKE_CASE				)
			self.add_vertex(_SCREAMING_SNAKE_CASE				)

			if head == tail:
				return

			__lowerCAmelCase    :		Union[str, Any]		    =   weight
			__lowerCAmelCase    :		str		    =   weight

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   self.get_edges()
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Optional[Any]		    =   edge
				edges.remove((tail, head, weight)				)
			for i in range(len(_SCREAMING_SNAKE_CASE				)				):
				__lowerCAmelCase    :		List[str]		    =   list(edges[i]				)

			edges.sort(key=lambda _SCREAMING_SNAKE_CASE				: e[2]				)
			for i in range(len(_SCREAMING_SNAKE_CASE				) - 1				):
				if edges[i][2] >= edges[i + 1][2]:
					__lowerCAmelCase    :		Dict		    =   edges[i][2] + 1
			for edge in edges:
				__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   edge
				__lowerCAmelCase    :		Union[str, Any]		    =   weight
				__lowerCAmelCase    :		Optional[int]		    =   weight

		def __str__(   self				):
			__lowerCAmelCase    :		List[Any]		    =   ''
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					__lowerCAmelCase    :		str		    =   self.adjacency[head][tail]
					string += f"{head} -> {tail} == {weight}\n"
			return string.rstrip('\n'				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   []
			for tail in self.adjacency:
				for head in self.adjacency[tail]:
					output.append((tail, head, self.adjacency[head][tail])				)
			return output

		def 							__lowerCamelCase							(   self				):
			return self.adjacency.keys()

		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE=None				):
			__lowerCAmelCase    :		List[str]		    =   Graph()
			if vertices is None:
				__lowerCAmelCase    :		int		    =   []
			if edges is None:
				__lowerCAmelCase    :		Optional[int]		    =   []
			for vertex in vertices:
				g.add_vertex(_SCREAMING_SNAKE_CASE				)
			for edge in edges:
				g.add_edge(*_SCREAMING_SNAKE_CASE				)
			return g




		class 				A__ :

				def __init__(   self				):
					__lowerCAmelCase    :		Dict		    =   {}
					__lowerCAmelCase    :		Optional[int]		    =   {}

				def __len__(   self				):
					return len(self.parent				)

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item in self.parent:
						return self.find(_SCREAMING_SNAKE_CASE				)

					__lowerCAmelCase    :		Dict		    =   item
					__lowerCAmelCase    :		int		    =   0
					return item

				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
					if item not in self.parent:
						return self.make_set(_SCREAMING_SNAKE_CASE				)
					if item != self.parent[item]:
						__lowerCAmelCase    :		Dict		    =   self.find(self.parent[item]				)
					return self.parent[item]







				def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
					__lowerCAmelCase    :		Any		    =   self.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		List[str]		    =   self.find(_SCREAMING_SNAKE_CASE				)

					if roota == roota:
						return roota

					if self.rank[roota] > self.rank[roota]:
						__lowerCAmelCase    :		Tuple		    =   roota
						return roota

					if self.rank[roota] < self.rank[roota]:
						__lowerCAmelCase    :		str		    =   roota
						return roota

					if self.rank[roota] == self.rank[roota]:
						self.rank[roota] += 1
						__lowerCAmelCase    :		Optional[int]		    =   roota
						return roota
					return None



		@staticmethod
		def 							__lowerCamelCase							(   _SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Union[str, Any]		    =   graph.num_vertices

			__lowerCAmelCase    :		List[Any]		    =   Graph.UnionFind()
			__lowerCAmelCase    :		Tuple		    =   []
			while num_components > 1:
				__lowerCAmelCase    :		str		    =   {}
				for vertex in graph.get_vertices():
					__lowerCAmelCase    :		Union[str, Any]		    =   -1

				__lowerCAmelCase    :		int		    =   graph.get_edges()
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Union[str, Any]		    =   edge
					edges.remove((tail, head, weight)				)
				for edge in edges:
					__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   edge
					__lowerCAmelCase    :		Optional[int]		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					__lowerCAmelCase    :		str		    =   union_find.find(_SCREAMING_SNAKE_CASE				)
					if seta != seta:
						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]

						if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
							__lowerCAmelCase    :		List[str]		    =   [head, tail, weight]
				for vertex in cheap_edge:
					if cheap_edge[vertex] != -1:
						__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		str		    =   cheap_edge[vertex]
						if union_find.find(_SCREAMING_SNAKE_CASE				) != union_find.find(_SCREAMING_SNAKE_CASE				):
							union_find.union(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)
							mst_edges.append(cheap_edge[vertex]				)
							__lowerCAmelCase    :		List[str]		    =   num_components - 1
			__lowerCAmelCase    :		Union[str, Any]		    =   Graph.build(edges=_SCREAMING_SNAKE_CASE				)
			return mst