

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			='laion/clap-htsat-unfused'
        _lowercase      			=tempfile.mkdtemp()




  def     A__	(							self				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






        return RobertaTokenizer.from_pretrained(self.checkpoint				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        return ClapFeatureExtractor.from_pretrained(self.checkpoint				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()
        _lowercase      			=self.get_feature_extractor()

        _lowercase      			=ClapProcessor(tokenizer=lowerCAmelCase				,  feature_extractor=lowerCAmelCase   )

        processor.save_pretrained(self.tmpdirname   )
        _lowercase      			=ClapProcessor.from_pretrained(self.tmpdirname   )

        self.assertEqual(processor.tokenizer.get_vocab()				,  tokenizer.get_vocab()   )
        self.assertIsInstance(processor.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor.feature_extractor.to_json_string()				,  feature_extractor.to_json_string()   )
        self.assertIsInstance(processor.feature_extractor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=ClapProcessor(tokenizer=self.get_tokenizer()				,  feature_extractor=self.get_feature_extractor()   )
        processor.save_pretrained(self.tmpdirname   )

        _lowercase      			=self.get_tokenizer(bos_token='(BOS)'				,  eos_token='(EOS)'   )
        _lowercase      			=self.get_feature_extractor(do_normalize=lowerCAmelCase				,  padding_value=1.0   )

        _lowercase      			=ClapProcessor.from_pretrained(
            self.tmpdirname				,  bos_token='(BOS)'				,  eos_token='(EOS)'				,  do_normalize=lowerCAmelCase				,  padding_value=1.0   )

        self.assertEqual(processor.tokenizer.get_vocab()				,  tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor.feature_extractor.to_json_string()				,  feature_extractor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.feature_extractor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_feature_extractor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ClapProcessor(tokenizer=lowerCAmelCase				,  feature_extractor=lowerCAmelCase   )

        _lowercase      			=floats_list((3, 1_000)   )

        _lowercase      			=feature_extractor(lowerCAmelCase				,  return_tensors='np'   )
        _lowercase      			=processor(audios=lowerCAmelCase				,  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     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=self.get_feature_extractor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ClapProcessor(tokenizer=lowerCAmelCase				,  feature_extractor=lowerCAmelCase   )

        _lowercase      			='This is a test string'

        _lowercase      			=processor(text=lowerCAmelCase   )

        _lowercase      			=tokenizer(lowerCAmelCase   )

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




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_feature_extractor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ClapProcessor(tokenizer=lowerCAmelCase				,  feature_extractor=lowerCAmelCase   )

        _lowercase      			=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase      			=processor.batch_decode(lowerCAmelCase   )
        _lowercase      			=tokenizer.batch_decode(lowerCAmelCase   )

        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_feature_extractor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ClapProcessor(tokenizer=lowerCAmelCase				,  feature_extractor=lowerCAmelCase   )

        self.assertListEqual(
            processor.model_input_names[2:]				,  feature_extractor.model_input_names				,  msg='`processor` and `feature_extractor` model input names do not match'				,  )





from __future__ import annotations

from statistics import mean
def 	a (     A__      :  list[int] ,  A__      :  list[int] ,  A__      :  int	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      _lowercase      			=[0] * no_of_processes

      # Initialize remaining_time to waiting_time.

      for i in range(A__	):
            _lowercase      			=burst_time[i]
      _lowercase      			=[]

      _lowercase      			=0
      _lowercase      			=0

      # When processes are not completed,
      # A process whose arrival time has passed \
      # and has remaining execution time is put into the ready_process.
      # The shortest process in the ready_process, target_process is executed.

      while completed != no_of_processes:
            _lowercase      			=[]
            _lowercase      			=-1

            for i in range(A__	):
                  if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                        ready_process.append(A__	)

            if len(A__	) > 0:
                  _lowercase      			=ready_process[0]
                  for i in ready_process:
                        if remaining_time[i] < remaining_time[target_process]:
                              _lowercase      			=i
                  total_time += burst_time[target_process]
                  completed += 1
                  _lowercase      			=0
                  _lowercase      			=(
                      total_time - arrival_time[target_process] - burst_time[target_process]
                  )
            else:
                  total_time += 1

      return waiting_time
def 	a (     A__      :  list[int] ,  A__      :  int ,  A__      :  list[int]	)       -> list[int]:





      """simple docstring"""



      _lowercase      			=[0] * no_of_processes
      for i in range(A__	):
            _lowercase      			=burst_time[i] + waiting_time[i]
      return turn_around_time


if __name__ == "__main__":
  print('[TEST CASE 01]')

  lowercase_   							=  4
  lowercase_   							=  [2, 5, 3, 7]
  lowercase_   							=  [0, 0, 0, 0]
  lowercase_   							=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  lowercase_   							=  calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
        f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
    )
  print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
  print(f"Average turnaround time = {mean(turn_around_time):.5f}")


