





'''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


lowerCAmelCase_   :   List[Any]       =					logging.get_logger(__name__)






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Union[str, Any]								=						"AutoTokenizer"
	lowerCAmelCase__					:			Any								=						["tokenizer"]

	lowerCAmelCase__					:			Union[str, Any]								=						{
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}







	def __init__(				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any]=None		):




				'''simple docstring'''
				super().__init__(lowerCamelCase		)

				a__     = speaker_embeddings







	@classmethod
	def 						__a   (				cls			:   int , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[Any]="speaker_embeddings_path.json" , **lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''

				if speaker_embeddings_dict_path is not None:
							a__     = 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`.'''		)
										a__     = None
							else:
										with open(lowerCamelCase		) as speaker_embeddings_json:
													a__     = json.load(lowerCamelCase		)
				else:
							a__     = None

				a__     = AutoTokenizer.from_pretrained(lowerCamelCase , **lowerCamelCase		)

				return cls(tokenizer=lowerCamelCase , speaker_embeddings=lowerCamelCase		)







	def 						__a   (				self			:   Any , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Any="speaker_embeddings_path.json" , lowerCamelCase			:   int="speaker_embeddings" , lowerCamelCase			:   bool = False , **lowerCamelCase			:   Union[str, Any] , ):




				'''simple docstring'''
				if self.speaker_embeddings is not None:
							os.makedirs(os.path.join(lowerCamelCase , lowerCamelCase , "v2"		) , exist_ok=lowerCamelCase		)

							a__     = {}

							a__     = save_directory

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

													a__     = {}
													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 , )
																a__     = os.path.join(lowerCamelCase , F'''{prompt_key}_{key}.npy'''		)

													a__     = tmp_dict

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

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







	def 						__a   (				self			:   int , lowerCamelCase			:   str = None , **lowerCamelCase			:   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" , 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.'''		)

							a__     = np.load(lowerCamelCase		)

				return voice_preset_dict







	def 						__a   (				self			:   int , lowerCamelCase			:   Optional[dict] = None		):




				'''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			:   Optional[int] , lowerCamelCase			:   int=None , lowerCamelCase			:   Any=None , lowerCamelCase			:   List[Any]="pt" , lowerCamelCase			:   List[str]=2_5_6 , lowerCamelCase			:   Dict=False , lowerCamelCase			:   Union[str, Any]=True , lowerCamelCase			:   Any=False , **lowerCamelCase			:   Dict , ):




				'''simple docstring'''
				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
							):
										a__     = self._load_voice_preset(lowerCamelCase		)

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

										a__     = np.load(lowerCamelCase		)

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

				a__     = 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:
							a__     = voice_preset

				return encoded_text










'''simple docstring'''


import math







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int = 0					,  __lowerCamelCase  :     int = 0     )   ->    list:
			a__     = end or len(__lowerCamelCase     )
			for i in range(__lowerCamelCase					,  __lowerCamelCase     ):
						a__     = i
						a__     = array[i]
						while temp_index != start and temp_index_value < array[temp_index - 1]:
									a__     = array[temp_index - 1]
									temp_index -= 1
						a__     = temp_index_value
			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    None:  # Max Heap
			a__     = index
			a__     = 2 * index + 1  # Left Node
			a__     = 2 * index + 2  # Right Node

			if left_index < heap_size and array[largest] < array[left_index]:
						a__     = left_index

			if right_index < heap_size and array[largest] < array[right_index]:
						a__     = right_index

			if largest != index:
						a__   ,				a__     = array[largest], array[index]
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			a__     = len(__lowerCamelCase     )

			for i in range(n // 2					,  -1					,  -1     ):
						heapify(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )

			for i in range(n - 1					,  0					,  -1     ):
						a__   ,				a__     = array[0], array[i]
						heapify(__lowerCamelCase					,  0					,  __lowerCamelCase     )

			return array







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			if (array[first_index] > array[middle_index]) != (
			    array[first_index] > array[last_index]
			):
						return array[first_index]
			elif (array[middle_index] > array[first_index]) != (
			    array[middle_index] > array[last_index]
			):
						return array[middle_index]
			else:
						return array[last_index]







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    int:
			a__     = low
			a__     = high
			while True:
						while array[i] < pivot:
									i += 1
						j -= 1
						while pivot < array[j]:
									j -= 1
						if i >= j:
									return i
						a__   ,				a__     = array[j], array[i]
						i += 1







def    _lowerCamelCase  (__lowerCamelCase  :     list     )   ->    list:
			if len(__lowerCamelCase     ) == 0:
						return array
			a__     = 2 * math.ceil(math.loga(len(__lowerCamelCase     )     )     )
			a__     = 16
			return intro_sort(__lowerCamelCase					,  0					,  len(__lowerCamelCase     )					,  __lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     list					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int					,  __lowerCamelCase  :     int     )   ->    list:
			while end - start > size_threshold:
						if max_depth == 0:
									return heap_sort(__lowerCamelCase     )
						max_depth -= 1
						a__     = median_of_a(__lowerCamelCase					,  __lowerCamelCase					,  start + ((end - start) // 2) + 1					,  end - 1     )
						a__     = partition(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						intro_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )
						a__     = p
			return insertion_sort(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		lowerCAmelCase_   :   Optional[int]       =					input("Enter numbers separated by a comma : ").strip()
		lowerCAmelCase_   :   List[str]       =					[float(item) for item in user_input.split(",")]
		print(sort(unsorted))



