"""simple docstring"""




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    a_   (  unittest.TestCase      ):




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							lowerCAmelCase__   = """ylacombe/bark-small"""
							lowerCAmelCase__   = tempfile.mkdtemp()
							lowerCAmelCase__   = """en_speaker_1"""
							lowerCAmelCase__   = """This is a test string"""
							lowerCAmelCase__   = """speaker_embeddings_path.json"""
							lowerCAmelCase__   = """speaker_embeddings"""




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]	,    **snake_case__    :  List[Any]  ):
							return AutoTokenizer.from_pretrained(self.checkpoint	,    **snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							shutil.rmtree(self.tmpdirname  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							lowerCAmelCase__   = self.get_tokenizer()

							lowerCAmelCase__   = BarkProcessor(tokenizer=snake_case__  )

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

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




					@slow
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]  ):
							lowerCAmelCase__   = 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	,    )

							lowerCAmelCase__   = self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )

							lowerCAmelCase__   = 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     _SCREAMING_SNAKE_CASE    (						self    :  List[str]  ):
							lowerCAmelCase__   = BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint	,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,    )

							lowerCAmelCase__   = 35
							lowerCAmelCase__   = 2
							lowerCAmelCase__   = 8

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

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

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

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

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

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




					def     _SCREAMING_SNAKE_CASE    (						self    :  str  ):
							lowerCAmelCase__   = self.get_tokenizer()

							lowerCAmelCase__   = BarkProcessor(tokenizer=snake_case__  )

							lowerCAmelCase__   = processor(text=self.input_string  )

							lowerCAmelCase__   = tokenizer(
							    self.input_string	,    padding="""max_length"""	,    max_length=256	,    add_special_tokens=snake_case__	,    return_attention_mask=snake_case__	,    return_token_type_ids=snake_case__	,    )

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





"""simple docstring"""




from __future__ import annotations

from statistics import mean







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		lowerCAmelCase__   = [0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i]
		lowerCAmelCase__   = []

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 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:
				lowerCAmelCase__   = []
				lowerCAmelCase__   = -1

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

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

		return waiting_time







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




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


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

			__lowerCAmelCase     :				str    				=    4
			__lowerCAmelCase     :				Dict    				=    [2, 5, 3, 7]
			__lowerCAmelCase     :				List[str]    				=    [0, 0, 0, 0]
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__lowerCAmelCase     :				Union[str, Any]    				=    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}")




