


"""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


__SCREAMING_SNAKE_CASE      		=    logging.get_logger(__name__)





class __snake_case		(       _a   ):







   """simple docstring"""




   lowerCAmelCase_					: List[Any]  						=      '''AutoTokenizer'''
   lowerCAmelCase_					: List[str]  						=      ['''tokenizer''']

   lowerCAmelCase_					: str  						=      {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }




   def __init__(		self				:Dict					,	UpperCamelCase__				:List[str]					,	UpperCamelCase__				:Any=None							):
          super().__init__(UpperCamelCase__							)

          _a							 =					speaker_embeddings




   @classmethod
   def SCREAMING_SNAKE_CASE_   (		cls				:Dict					,	UpperCamelCase__				:List[str]					,	UpperCamelCase__				:int="speaker_embeddings_path.json"					,	**UpperCamelCase__				:int							):

          if speaker_embeddings_dict_path is not None:
                 _a							 =					get_file_from_repo(
                     UpperCamelCase__					,	UpperCamelCase__					,	subfolder=kwargs.pop("subfolder"					,	UpperCamelCase__							)					,	cache_dir=kwargs.pop("cache_dir"					,	UpperCamelCase__							)					,	force_download=kwargs.pop("force_download"					,	UpperCamelCase__							)					,	proxies=kwargs.pop("proxies"					,	UpperCamelCase__							)					,	resume_download=kwargs.pop("resume_download"					,	UpperCamelCase__							)					,	local_files_only=kwargs.pop("local_files_only"					,	UpperCamelCase__							)					,	use_auth_token=kwargs.pop("use_auth_token"					,	UpperCamelCase__							)					,	revision=kwargs.pop("revision"					,	UpperCamelCase__							)					,	)
                 if speaker_embeddings_path is None:
                        logger.warning(
                            f'`{os.path.join(UpperCamelCase__					,	UpperCamelCase__							)}` 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`.'							)
                        _a							 =					None
                 else:
                        with open(UpperCamelCase__							) as speaker_embeddings_json:
                               _a							 =					json.load(UpperCamelCase__							)
          else:
                 _a							 =					None

          _a							 =					AutoTokenizer.from_pretrained(UpperCamelCase__					,	**UpperCamelCase__							)

          return cls(tokenizer=UpperCamelCase__					,	speaker_embeddings=UpperCamelCase__							)




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple					,	UpperCamelCase__				:List[Any]					,	UpperCamelCase__				:Optional[int]="speaker_embeddings_path.json"					,	UpperCamelCase__				:Tuple="speaker_embeddings"					,	UpperCamelCase__				:bool = False					,	**UpperCamelCase__				:Optional[Any]					,	):

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

                 _a							 =					{}

                 _a							 =					save_directory

                 for prompt_key in self.speaker_embeddings:
                        if prompt_key != "repo_or_path":
                               _a							 =					self._load_voice_preset(UpperCamelCase__							)

                               _a							 =					{}
                               for key in self.speaker_embeddings[prompt_key]:
                                      np.save(
                                          os.path.join(
                                              embeddings_dict["repo_or_path"]					,	UpperCamelCase__					,	f'{prompt_key}_{key}'							)					,	voice_preset[key]					,	allow_pickle=UpperCamelCase__					,	)
                                      _a							 =					os.path.join(UpperCamelCase__					,	f'{prompt_key}_{key}.npy'							)

                               _a							 =					tmp_dict

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

          super().save_pretrained(UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__							)




   def SCREAMING_SNAKE_CASE_   (		self				:Dict					,	UpperCamelCase__				:str = None					,	**UpperCamelCase__				:List[Any]							):
          _a							 =					self.speaker_embeddings[voice_preset]

          _a							 =					{}
          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}].'							)

                 _a							 =					get_file_from_repo(
                     self.speaker_embeddings.get("repo_or_path"					,	"/"							)					,	voice_preset_paths[key]					,	subfolder=kwargs.pop("subfolder"					,	UpperCamelCase__							)					,	cache_dir=kwargs.pop("cache_dir"					,	UpperCamelCase__							)					,	force_download=kwargs.pop("force_download"					,	UpperCamelCase__							)					,	proxies=kwargs.pop("proxies"					,	UpperCamelCase__							)					,	resume_download=kwargs.pop("resume_download"					,	UpperCamelCase__							)					,	local_files_only=kwargs.pop("local_files_only"					,	UpperCamelCase__							)					,	use_auth_token=kwargs.pop("use_auth_token"					,	UpperCamelCase__							)					,	revision=kwargs.pop("revision"					,	UpperCamelCase__							)					,	)
                 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.'							)

                 _a							 =					np.load(UpperCamelCase__							)

          return voice_preset_dict




   def SCREAMING_SNAKE_CASE_   (		self				:Tuple					,	UpperCamelCase__				:Optional[dict] = 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				:Optional[int]					,	UpperCamelCase__				:Optional[int]=None					,	UpperCamelCase__				:Optional[int]=None					,	UpperCamelCase__				:Any="pt"					,	UpperCamelCase__				:Union[str, Any]=256					,	UpperCamelCase__				:Optional[int]=False					,	UpperCamelCase__				:List[str]=True					,	UpperCamelCase__				:int=False					,	**UpperCamelCase__				:Tuple					,	):

          if voice_preset is not None and not isinstance(UpperCamelCase__					,	UpperCamelCase__							):
                 if (
                     isinstance(UpperCamelCase__					,	UpperCamelCase__							)
                     and self.speaker_embeddings is not None
                     and voice_preset in self.speaker_embeddings
                 ):
                        _a							 =					self._load_voice_preset(UpperCamelCase__							)

                 else:
                        if isinstance(UpperCamelCase__					,	UpperCamelCase__							) and not voice_preset.endswith(".npz"							):
                               _a							 =					voice_preset + ".npz"

                        _a							 =					np.load(UpperCamelCase__							)

          if voice_preset is not None:
                 self._validate_voice_preset_dict(UpperCamelCase__					,	**UpperCamelCase__							)
                 _a							 =					BatchFeature(data=UpperCamelCase__					,	tensor_type=UpperCamelCase__							)

          _a							 =					self.tokenizer(
              UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	padding="max_length"					,	max_length=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	return_token_type_ids=UpperCamelCase__					,	add_special_tokens=UpperCamelCase__					,	**UpperCamelCase__					,	)

          if voice_preset is not None:
                 _a							 =					voice_preset

          return encoded_text








class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


