


'''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   : Dict             =				logging.get_logger(__name__)




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				'''AutoTokenizer'''
				_snake_case						       =				['''tokenizer''']

				_snake_case						       =				{
				    '''semantic_prompt''': 1,
				    '''coarse_prompt''': 2,
				    '''fine_prompt''': 2,
				}

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

					lowercase						:		str   					=		speaker_embeddings

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

					if speaker_embeddings_dict_path is not None:
						lowercase						:		Optional[int]   					=		get_file_from_repo(
						    a_							,      a_							,      subfolder=kwargs.pop("subfolder"							,      a_							)							,      cache_dir=kwargs.pop("cache_dir"							,      a_							)							,      force_download=kwargs.pop("force_download"							,      a_							)							,      proxies=kwargs.pop("proxies"							,      a_							)							,      resume_download=kwargs.pop("resume_download"							,      a_							)							,      local_files_only=kwargs.pop("local_files_only"							,      a_							)							,      use_auth_token=kwargs.pop("use_auth_token"							,      a_							)							,      revision=kwargs.pop("revision"							,      a_							)							,      )
						if speaker_embeddings_path is None:
							logger.warning(
							    F'''`{os.path.join(a_							,      a_							)}` 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`.'''							)
							lowercase						:		Dict   					=		None
						else:
							with open(a_							) as speaker_embeddings_json:
								lowercase						:		Optional[int]   					=		json.load(a_							)
					else:
						lowercase						:		List[str]   					=		None

					lowercase						:		Any   					=		AutoTokenizer.from_pretrained(a_							,      **a_							)

					return cls(tokenizer=a_							,      speaker_embeddings=a_							)

				def 	a__		(     self							,      a_							,      a_="speaker_embeddings_path.json"							,      a_="speaker_embeddings"							,      a_ = False							,      **a_							,      )    ->  Dict:
					if self.speaker_embeddings is not None:
						os.makedirs(os.path.join(a_							,      a_							,      "v2"							)							,      exist_ok=a_							)

						lowercase						:		Tuple   					=		{}

						lowercase						:		int   					=		save_directory

						for prompt_key in self.speaker_embeddings:
							if prompt_key != "repo_or_path":
								lowercase						:		int   					=		self._load_voice_preset(a_							)

								lowercase						:		Union[str, Any]   					=		{}
								for key in self.speaker_embeddings[prompt_key]:
									np.save(
									    os.path.join(
									        embeddings_dict["repo_or_path"]							,      a_							,      F'''{prompt_key}_{key}'''							)							,      voice_preset[key]							,      allow_pickle=a_							,      )
									lowercase						:		str   					=		os.path.join(a_							,      F'''{prompt_key}_{key}.npy'''							)

								lowercase						:		Union[str, Any]   					=		tmp_dict

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

					super().save_pretrained(a_							,      a_							,      **a_							)

				def 	a__		(     self							,      a_ = None							,      **a_							)    ->  int:
					lowercase						:		Dict   					=		self.speaker_embeddings[voice_preset]

					lowercase						:		List[str]   					=		{}
					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}].'''							)

						lowercase						:		Optional[Any]   					=		get_file_from_repo(
						    self.speaker_embeddings.get("repo_or_path"							,      "/"							)							,      voice_preset_paths[key]							,      subfolder=kwargs.pop("subfolder"							,      a_							)							,      cache_dir=kwargs.pop("cache_dir"							,      a_							)							,      force_download=kwargs.pop("force_download"							,      a_							)							,      proxies=kwargs.pop("proxies"							,      a_							)							,      resume_download=kwargs.pop("resume_download"							,      a_							)							,      local_files_only=kwargs.pop("local_files_only"							,      a_							)							,      use_auth_token=kwargs.pop("use_auth_token"							,      a_							)							,      revision=kwargs.pop("revision"							,      a_							)							,      )
						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.'''							)

						lowercase						:		int   					=		np.load(a_							)

					return voice_preset_dict

				def 	a__		(     self							,      a_ = None							)    ->  Dict:
					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_=2_5_6							,      a_=False							,      a_=True							,      a_=False							,      **a_							,      )    ->  Optional[Any]:
					if voice_preset is not None and not isinstance(a_							,      a_							):
						if (
						    isinstance(a_							,      a_							)
						    and self.speaker_embeddings is not None
						    and voice_preset in self.speaker_embeddings
						):
							lowercase						:		List[Any]   					=		self._load_voice_preset(a_							)

						else:
							if isinstance(a_							,      a_							) and not voice_preset.endswith(".npz"							):
								lowercase						:		List[str]   					=		voice_preset + ".npz"

							lowercase						:		Tuple   					=		np.load(a_							)

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

					lowercase						:		int   					=		self.tokenizer(
					    a_							,      return_tensors=a_							,      padding="max_length"							,      max_length=a_							,      return_attention_mask=a_							,      return_token_type_ids=a_							,      add_special_tokens=a_							,      **a_							,      )

					if voice_preset is not None:
						lowercase						:		str   					=		voice_preset

					return encoded_text






'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


