





'''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       lowerCAmelCase_						(						snake_case__						):
 __lowerCamelCase  :							Dict				     =    "AutoTokenizer"
 __lowerCamelCase  :							int				     =    ["tokenizer"]

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




 def __init__(		self	,   _lowerCAmelCase	,   _lowerCAmelCase=None						)			->							int:
     super().__init__(_lowerCAmelCase						)

     _lowerCAmelCase			=					speaker_embeddings




 @classmethod
 def 							_snake_case      (		cls	,   _lowerCAmelCase	,   _lowerCAmelCase="speaker_embeddings_path.json"	,   **_lowerCAmelCase						)			->							Any:







     if speaker_embeddings_dict_path is not None:
         _lowerCAmelCase			=					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
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''						)
             _lowerCAmelCase			=					None
         else:
             with open(_lowerCAmelCase						) as speaker_embeddings_json:
                 _lowerCAmelCase			=					json.load(_lowerCAmelCase						)
     else:
         _lowerCAmelCase			=					None

     _lowerCAmelCase			=					AutoTokenizer.from_pretrained(_lowerCAmelCase	,   **_lowerCAmelCase						)

     return cls(tokenizer=_lowerCAmelCase	,   speaker_embeddings=_lowerCAmelCase						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase="speaker_embeddings_path.json"	,   _lowerCAmelCase="speaker_embeddings"	,   _lowerCAmelCase = False	,   **_lowerCAmelCase	,   )			->							Any:







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

         _lowerCAmelCase			=					{}

         _lowerCAmelCase			=					save_directory

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

                 _lowerCAmelCase			=					{}
                 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	,   )
                     _lowerCAmelCase			=					os.path.join(_lowerCAmelCase	,   f'''{prompt_key}_{key}.npy'''						)

                 _lowerCAmelCase			=					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	,   _lowerCAmelCase = None	,   **_lowerCAmelCase						)			->							str:
     _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"	,   _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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''						)

         _lowerCAmelCase			=					np.load(_lowerCAmelCase						)

     return voice_preset_dict




 def 							_snake_case      (		self	,   _lowerCAmelCase = None						)			->							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	,   _lowerCAmelCase=None	,   _lowerCAmelCase=None	,   _lowerCAmelCase="pt"	,   _lowerCAmelCase=256	,   _lowerCAmelCase=False	,   _lowerCAmelCase=True	,   _lowerCAmelCase=False	,   **_lowerCAmelCase	,   )			->							str:







     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
         ):
             _lowerCAmelCase			=					self._load_voice_preset(_lowerCAmelCase						)

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

             _lowerCAmelCase			=					np.load(_lowerCAmelCase						)

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

     _lowerCAmelCase			=					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:
         _lowerCAmelCase			=					voice_preset

     return encoded_text










def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
