

'''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							:				Union[str, Any]     	=			logging.get_logger(__name__)






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     """AutoTokenizer"""
				_a    	=     ["""tokenizer"""]

				_a    	=     {
				    """semantic_prompt""": 1,
				    """coarse_prompt""": 2,
				    """fine_prompt""": 2,
				}



				def __init__(    self,     _lowercase,     _lowercase=None       )     ->							List[Any]:
											super().__init__(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				speaker_embeddings



				@classmethod
				def   a__					(    cls,     _lowercase,     _lowercase="speaker_embeddings_path.json",     **_lowercase       )     ->							Tuple:

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

											SCREAMING_SNAKE_CASE_				=				AutoTokenizer.from_pretrained(_lowercase,     **_lowercase       )

											return cls(tokenizer=_lowercase,     speaker_embeddings=_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase="speaker_embeddings_path.json",     _lowercase="speaker_embeddings",     _lowercase = False,     **_lowercase,     )     ->							List[Any]:
											if self.speaker_embeddings is not None:
																		os.makedirs(os.path.join(_lowercase,     _lowercase,     'v2'       ),     exist_ok=_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				{}

																		SCREAMING_SNAKE_CASE_				=				save_directory

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

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

																																SCREAMING_SNAKE_CASE_				=				tmp_dict

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

											super().save_pretrained(_lowercase,     _lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase = None,     **_lowercase       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				self.speaker_embeddings[voice_preset]

											SCREAMING_SNAKE_CASE_				=				{}
											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}]."""       )

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

																		SCREAMING_SNAKE_CASE_				=				np.load(_lowercase       )

											return voice_preset_dict



				def   a__					(    self,     _lowercase = None       )     ->							Optional[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,     _lowercase=None,     _lowercase=None,     _lowercase="pt",     _lowercase=256,     _lowercase=False,     _lowercase=True,     _lowercase=False,     **_lowercase,     )     ->							Union[str, Any]:
											if voice_preset is not None and not isinstance(_lowercase,     _lowercase       ):
																		if (
																		    isinstance(_lowercase,     _lowercase       )
																		    and self.speaker_embeddings is not None
																		    and voice_preset in self.speaker_embeddings
																		):
																									SCREAMING_SNAKE_CASE_				=				self._load_voice_preset(_lowercase       )

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

																									SCREAMING_SNAKE_CASE_				=				np.load(_lowercase       )

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

											SCREAMING_SNAKE_CASE_				=				self.tokenizer(
											    _lowercase,     return_tensors=_lowercase,     padding='max_length',     max_length=_lowercase,     return_attention_mask=_lowercase,     return_token_type_ids=_lowercase,     add_special_tokens=_lowercase,     **_lowercase,     )

											if voice_preset is not None:
																		SCREAMING_SNAKE_CASE_				=				voice_preset

											return encoded_text








'''simple docstring'''

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			0  # The first color of the flag.
SCREAMING_SNAKE_CASE							:				List[str]     	=			1  # The second color of the flag.
SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			2  # The third color of the flag.
SCREAMING_SNAKE_CASE							:				Tuple     	=			(red, white, blue)




def 							_UpperCamelCase				(   lowerCAmelCase__:					list   )      -> list:
							if not sequence:
														return []
							if len(lowerCAmelCase__   ) == 1:
														return list(lowerCAmelCase__   )
							SCREAMING_SNAKE_CASE_				=				0
							SCREAMING_SNAKE_CASE_				=				len(lowerCAmelCase__   ) - 1
							SCREAMING_SNAKE_CASE_				=				0
							while mid <= high:
														if sequence[mid] == colors[0]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[mid], sequence[low]
																					low += 1
																					mid += 1
														elif sequence[mid] == colors[1]:
																					mid += 1
														elif sequence[mid] == colors[2]:
																					SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				sequence[high], sequence[mid]
																					high -= 1
														else:
																					SCREAMING_SNAKE_CASE_				=				F"""The elements inside the sequence must contains only {colors} values"""
																					raise ValueError(lowerCAmelCase__   )
							return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE							:				str     	=			input("Enter numbers separated by commas:\n").strip()
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			[int(item.strip()) for item in user_input.split(",")]
							print(f"{dutch_national_flag_sort(unsorted)}")





