



'''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


lowercase_		       =  logging.get_logger(__name__)






class __A			(  lowerCAmelCase_						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       """AutoTokenizer"""
 __lowerCamelCase    :       int          =       ["""tokenizer"""]

 __lowerCamelCase    :       Dict          =       {
     """semantic_prompt""": 1,
     """coarse_prompt""": 2,
     """fine_prompt""": 2,
 }



 def __init__(self ,   A ,   A=None       )    ->     Any:


      """simple docstring"""



      super().__init__(snake_case_       )

      _a      =  speaker_embeddings



 @classmethod
 def 					a__  (cls ,   A ,   A="speaker_embeddings_path.json" ,   **A       )    ->     Optional[Any]:


      """simple docstring"""



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

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

      return cls(tokenizer=snake_case_ ,   speaker_embeddings=snake_case_       )



 def 					a__  (self ,   A ,   A="speaker_embeddings_path.json" ,   A="speaker_embeddings" ,   A = False ,   **A ,   )    ->     Tuple:


      """simple docstring"""



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

           _a      =  {}

           _a      =  save_directory

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

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

                     _a      =  tmp_dict

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

      super().save_pretrained(snake_case_ ,   snake_case_ ,   **snake_case_       )



 def 					a__  (self ,   A = None ,   **A       )    ->     List[Any]:


      """simple docstring"""



      _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''' ,   snake_case_       ) ,   cache_dir=kwargs.pop('''cache_dir''' ,   snake_case_       ) ,   force_download=kwargs.pop('''force_download''' ,   snake_case_       ) ,   proxies=kwargs.pop('''proxies''' ,   snake_case_       ) ,   resume_download=kwargs.pop('''resume_download''' ,   snake_case_       ) ,   local_files_only=kwargs.pop('''local_files_only''' ,   snake_case_       ) ,   use_auth_token=kwargs.pop('''use_auth_token''' ,   snake_case_       ) ,   revision=kwargs.pop('''revision''' ,   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.'''       )

           _a      =  np.load(snake_case_       )

      return voice_preset_dict



 def 					a__  (self ,   A = None       )    ->     List[str]:


      """simple docstring"""



      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 ,   )    ->     int:


      """simple docstring"""



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

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

                _a      =  np.load(snake_case_       )

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

      _a      =  self.tokenizer(
          snake_case_ ,   return_tensors=snake_case_ ,   padding='''max_length''' ,   max_length=snake_case_ ,   return_attention_mask=snake_case_ ,   return_token_type_ids=snake_case_ ,   add_special_tokens=snake_case_ ,   **snake_case_ ,   )

      if voice_preset is not None:
           _a      =  voice_preset

      return encoded_text










'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




