


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


_snake_case	:						str         =							logging.get_logger(__name__)

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Dict	    =    "AutoTokenizer"
							__UpperCAmelCase       :				List[Any]	    =    ["tokenizer"]

							__UpperCAmelCase       :				Tuple	    =    {
							    "semantic_prompt": 1,
							    "coarse_prompt": 2,
							    "fine_prompt": 2,
							}

							def __init__(				self			:    str       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Union[str, Any]=None							)      ->       Optional[Any]:
														super().__init__(lowerCamelCase							)

														__snake_case						:     Tuple									=  speaker_embeddings

							@classmethod
							def        __snake_case   (				cls			:    Optional[Any]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[int]="speaker_embeddings_path.json"       ,      **lowerCamelCase			:    List[Any]							)      ->       List[str]:

														if speaker_embeddings_dict_path is not None:
																					__snake_case						:     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`.'							)
																												__snake_case						:     List[str]									=  None
																					else:
																												with open(lowerCamelCase							) as speaker_embeddings_json:
																																			__snake_case						:     List[Any]									=  json.load(lowerCamelCase							)
														else:
																					__snake_case						:     Dict									=  None

														__snake_case						:     Union[str, Any]									=  AutoTokenizer.from_pretrained(lowerCamelCase       ,      **lowerCamelCase							)

														return cls(tokenizer=lowerCamelCase       ,      speaker_embeddings=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[str]="speaker_embeddings_path.json"       ,      lowerCamelCase			:    List[Any]="speaker_embeddings"       ,      lowerCamelCase			:    bool = False       ,      **lowerCamelCase			:    List[Any]       ,      )      ->       str:
														if self.speaker_embeddings is not None:
																					os.makedirs(os.path.join(lowerCamelCase       ,      lowerCamelCase       ,      "v2"							)       ,      exist_ok=lowerCamelCase							)

																					__snake_case						:     List[Any]									=  {}

																					__snake_case						:     Optional[int]									=  save_directory

																					for prompt_key in self.speaker_embeddings:
																												if prompt_key != "repo_or_path":
																																			__snake_case						:     List[str]									=  self._load_voice_preset(lowerCamelCase							)

																																			__snake_case						:     int									=  {}
																																			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       ,      )
																																										__snake_case						:     Optional[Any]									=  os.path.join(lowerCamelCase       ,      F'{prompt_key}_{key}.npy'							)

																																			__snake_case						:     List[str]									=  tmp_dict

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

														super().save_pretrained(lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    str = None       ,      **lowerCamelCase			:    int							)      ->       Union[str, Any]:
														__snake_case						:     Optional[int]									=  self.speaker_embeddings[voice_preset]

														__snake_case						:     List[str]									=  {}
														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}].'							)

																					__snake_case						:     int									=  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.'							)

																					__snake_case						:     Tuple									=  np.load(lowerCamelCase							)

														return voice_preset_dict

							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    Optional[dict] = None							)      ->       Tuple:
														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			:    Optional[Any]       ,      lowerCamelCase			:    Optional[Any]=None       ,      lowerCamelCase			:    List[Any]=None       ,      lowerCamelCase			:    Dict="pt"       ,      lowerCamelCase			:    List[Any]=256       ,      lowerCamelCase			:    str=False       ,      lowerCamelCase			:    Optional[int]=True       ,      lowerCamelCase			:    str=False       ,      **lowerCamelCase			:    Any       ,      )      ->       List[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
																					):
																												__snake_case						:     int									=  self._load_voice_preset(lowerCamelCase							)

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

																												__snake_case						:     Union[str, Any]									=  np.load(lowerCamelCase							)

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

														__snake_case						:     Dict									=  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:
																					__snake_case						:     Tuple									=  voice_preset

														return encoded_text







import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase						,  [-1 * len(lowerCamelCase ), (key, value)] )

				# chosen_vertices = set of chosen vertices
				__magic_name__						:						Tuple										=set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								__magic_name__						:						Tuple										=heapq.heappop(lowerCamelCase )[1][0]
								chosen_vertices.add(lowerCamelCase )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																__magic_name__						:						Tuple										=elem[1][1].index(lowerCamelCase )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase )
				return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					Optional[int]          =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




