






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      List[Any]						)		->		str:
											snake_case__									=     'ylacombe/bark-small'
											snake_case__									=     tempfile.mkdtemp()
											snake_case__									=     'en_speaker_1'
											snake_case__									=     'This is a test string'
											snake_case__									=     'speaker_embeddings_path.json'
											snake_case__									=     'speaker_embeddings'
							def lowerCAmelCase_ (     self:      List[str]       ,	**UpperCamelCase:      List[Any]						)		->		Dict:
											return AutoTokenizer.from_pretrained(self.checkpoint       ,	**UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											shutil.rmtree(self.tmpdirname						)
							def lowerCAmelCase_ (     self:      int						)		->		str:
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     BarkProcessor(tokenizer=UpperCamelCase						)

											processor.save_pretrained(self.tmpdirname						)
											snake_case__									=     BarkProcessor.from_pretrained(self.tmpdirname						)

											self.assertEqual(processor.tokenizer.get_vocab()       ,	tokenizer.get_vocab()						)
							@slow
							def lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											snake_case__									=     BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint       ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,	)
											processor.save_pretrained(
											    self.tmpdirname       ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,	speaker_embeddings_directory=self.speaker_embeddings_directory       ,	)

											snake_case__									=     self.get_tokenizer(bos_token='(BOS)'       ,	eos_token='(EOS)'						)

											snake_case__									=     BarkProcessor.from_pretrained(
											    self.tmpdirname       ,	self.speaker_embeddings_dict_path       ,	bos_token='(BOS)'       ,	eos_token='(EOS)'       ,	)

											self.assertEqual(processor.tokenizer.get_vocab()       ,	tokenizer_add_kwargs.get_vocab()						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		List[str]:
											snake_case__									=     BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint       ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,	)

											snake_case__									=     35
											snake_case__									=     2
											snake_case__									=     8

											snake_case__									=     {
											    'semantic_prompt': np.ones(UpperCamelCase						),
											    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)						),
											    'fine_prompt': np.ones((nb_codebooks_total, seq_len)						),
											}

											# test providing already loaded voice_preset
											snake_case__									=     processor(text=self.input_string       ,	voice_preset=UpperCamelCase						)

											snake_case__									=     inputs['history_prompt']
											for key in voice_preset:
															self.assertListEqual(voice_preset[key].tolist()       ,	processed_voice_preset.get(UpperCamelCase       ,	np.array([]						)						).tolist()						)

											# test loading voice preset from npz file
											snake_case__									=     os.path.join(self.tmpdirname       ,	'file.npz'						)
											np.savez(UpperCamelCase       ,	**UpperCamelCase						)
											snake_case__									=     processor(text=self.input_string       ,	voice_preset=UpperCamelCase						)
											snake_case__									=     inputs['history_prompt']

											for key in voice_preset:
															self.assertListEqual(voice_preset[key].tolist()       ,	processed_voice_preset.get(UpperCamelCase       ,	np.array([]						)						).tolist()						)

											# test loading voice preset from the hub
											snake_case__									=     processor(text=self.input_string       ,	voice_preset=self.voice_preset						)


							def lowerCAmelCase_ (     self:      Dict						)		->		Any:
											snake_case__									=     self.get_tokenizer()

											snake_case__									=     BarkProcessor(tokenizer=UpperCamelCase						)

											snake_case__									=     processor(text=self.input_string						)

											snake_case__									=     tokenizer(
											    self.input_string       ,	padding='max_length'       ,	max_length=2_56       ,	add_special_tokens=UpperCamelCase       ,	return_attention_mask=UpperCamelCase       ,	return_token_type_ids=UpperCamelCase       ,	)

											for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key].squeeze().tolist()						)












from __future__ import annotations
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				# Checks if the entire collection has been sorted
				if len(_A  ) <= 1 or n <= 1:
								return

				insert_next(_A      ,   n - 1  )
				rec_insertion_sort(_A      ,   n - 1  )
def      a_ (      _A      ,   _A  )			->			Tuple:







				"""simple docstring"""
				# Checks order between adjacent elements
				if index >= len(_A  ) or collection[index - 1] <= collection[index]:
								return

				# Swaps adjacent elements since they are not in ascending order
				snake_case__							,						snake_case__									=     (
				    collection[index],
				    collection[index - 1],
				)

				insert_next(_A      ,   index + 1  )


if __name__ == "__main__":
							__UpperCamelCase					:		Optional[int]	  =						input("""Enter integers separated by spaces: """)
							__UpperCamelCase					:		list[int]	  =						[int(num) for num in numbers.split()]
							rec_insertion_sort(number_list, len(number_list))
							print(number_list)




