code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from __future__ import annotations
  
  
  
  
  
  
  class 							__snake_case    :
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_		)			->		None:
  		snake_case__  					=      data
  		snake_case__  					=      None
  
  
  
  
  
  
  class 							__snake_case    :
  
  
  
  
  
  	def __init__(		self		)			->		Optional[Any]:
  		snake_case__  					=      None
  
  
  
  
  
  	def      _snake_case      (		self			,					UpperCamelCase_		)			->		int:
  		snake_case__  					=      Node(UpperCamelCase_		)
  		snake_case__  					=      self.head
  		snake_case__  					=      new_node
  		return self.head.data
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int | None:
  		snake_case__  					=      self.head
  		snake_case__  					=      self.head
  		if self.head:
  			while fast_pointer and fast_pointer.next:
  				snake_case__  					=      fast_pointer.next.next
  				snake_case__  					=      slow_pointer.next
  			return slow_pointer.data
  		else:
  			print('No element found.'		)
  			return None
  
  
  if __name__ == "__main__":
  			a__				: Union[str, Any]									=    LinkedList()
  			for _ in range(int(input().strip())):
  						a__				: Optional[int]									=    int(input().strip())
  						link.push(data)
  			print(link.middle_element())
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import shutil
  import tempfile
  import unittest
  
  import pytest
  
  from transformers import WhisperTokenizer, is_speech_available
  from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
  
  from .test_feature_extraction_whisper import floats_list
  
  
  if is_speech_available():
  			from transformers import WhisperFeatureExtractor, WhisperProcessor
  
  
  a__				: Tuple									=    5_0358
  a__				: str									=    5_0362
  
  
  
  
  
  
  @require_torch
  @require_torchaudio
  @require_sentencepiece
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      'openai/whisper-small.en'
  		snake_case__  					=      tempfile.mkdtemp()
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[int]:
  		return WhisperTokenizer.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
  		return WhisperFeatureExtractor.from_pretrained(self.checkpoint			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Any:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_feature_extractor()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		processor.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      WhisperProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		processor.save_pretrained(self.tmpdirname		)
  
  		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
  		snake_case__  					=      self.get_feature_extractor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      WhisperProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.feature_extractor.to_json_string()			,					feature_extractor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.feature_extractor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      floats_list((3, 1000)		)
  
  		snake_case__  					=      feature_extractor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      'This is a test string'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    processor.model_input_names			,					feature_extractor.model_input_names			,					msg='`processor` and `feature_extractor` model input names do not match'			,					)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_feature_extractor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      WhisperProcessor(tokenizer=UpperCamelCase_			,					feature_extractor=UpperCamelCase_		)
  		snake_case__  					=      processor.get_decoder_prompt_ids(task='transcribe'			,					no_timestamps=UpperCamelCase_		)
  
  		self.assertIsInstance(UpperCamelCase_			,					UpperCamelCase_		)
  		for ids in forced_decoder_ids:
  			self.assertIsInstance(UpperCamelCase_			,					(list, tuple)		)
  
  		snake_case__  					=      [TRANSCRIBE, NOTIMESTAMPS]
  		self.assertListEqual([ids[-1] for ids in forced_decoder_ids]			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		snake_case__  					=      processor.get_prompt_ids('Mr. Quilter'		)
  		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)
  
  		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 1770, 13, 2264, 346, 353]		)
  		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> Mr. Quilter'		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  		snake_case__  					=      processor.get_prompt_ids(''		)
  		snake_case__  					=      processor.tokenizer.decode(UpperCamelCase_		)
  
  		self.assertListEqual(prompt_ids.tolist()			,					[5_0360, 220]		)
  		self.assertEqual(UpperCamelCase_			,					'<|startofprev|> '		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		int:
  		snake_case__  					=      WhisperProcessor(tokenizer=self.get_tokenizer()			,					feature_extractor=self.get_feature_extractor()		)
  
  		def _test_prompt_error_raised_helper(UpperCamelCase_			,					UpperCamelCase_		):
  			with pytest.raises(UpperCamelCase_		) as excinfo:
  				processor.get_prompt_ids(UpperCamelCase_		)
  			snake_case__  					=      F'''Encountered text in the prompt corresponding to disallowed special token: {special_token}.'''
  			self.assertEqual(UpperCamelCase_			,					str(excinfo.value		)		)
  
  		_test_prompt_error_raised_helper('<|startofprev|> test'			,					'<|startofprev|>'		)
  		_test_prompt_error_raised_helper('test <|notimestamps|>'			,					'<|notimestamps|>'		)
  		_test_prompt_error_raised_helper('test <|zh|> test <|transcribe|>'			,					'<|zh|>'		)
  
  
  
  