




'''simple docstring'''




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
							pass

if is_speech_available():
							from transformers import EncodecFeatureExtractor, MusicgenProcessor


snake_case__ 							=    random.Random()




def     snake_case__     (			lowerCamelCase__					: List[Any]      ,		lowerCamelCase__					: List[str]=1.0      ,		lowerCamelCase__					: Union[str, Any]=None      ,		lowerCamelCase__					: Any=None      ) -> int:
					if rng is None:
										A_			:  Dict			=				global_rng

					A_			:  Optional[Any]			=				[]
					for batch_idx in range(shape[0]      ):
										values.append([]      )
										for _ in range(shape[1]      ):
															values[-1].append(rng.random() * scale      )

					return values






@require_torch
@require_sentencepiece
class      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				'''facebook/musicgen-small'''
											A_			:  Tuple			=				tempfile.mkdtemp()







						def    _a     (  self   :							str				,			**_lowerCamelCase   :							Dict     ):





											"""simple docstring"""







											return TaTokenizer.from_pretrained(self.checkpoint				,			**_lowerCamelCase     )







						def    _a     (  self   :							List[str]				,			**_lowerCamelCase   :							str     ):





											"""simple docstring"""







											return EncodecFeatureExtractor.from_pretrained(self.checkpoint				,			**_lowerCamelCase     )







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											shutil.rmtree(self.tmpdirname     )







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											A_			:  int			=				self.get_tokenizer()
											A_			:  int			=				self.get_feature_extractor()

											A_			:  Optional[Any]			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

											processor.save_pretrained(self.tmpdirname     )
											A_			:  List[str]			=				MusicgenProcessor.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   :							str     ):





											"""simple docstring"""







											A_			:  int			=				MusicgenProcessor(tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()     )
											processor.save_pretrained(self.tmpdirname     )

											A_			:  Dict			=				self.get_tokenizer(bos_token='''(BOS)'''				,			eos_token='''(EOS)'''     )
											A_			:  int			=				self.get_feature_extractor(do_normalize=_lowerCamelCase				,			padding_value=1.0     )

											A_			:  Optional[Any]			=				MusicgenProcessor.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   :							Optional[Any]     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.get_feature_extractor()
											A_			:  List[str]			=				self.get_tokenizer()

											A_			:  Union[str, Any]			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

											A_			:  Union[str, Any]			=				floats_list((3, 1000)     )

											A_			:  Tuple			=				feature_extractor(_lowerCamelCase				,			return_tensors='''np'''     )
											A_			:  str			=				processor(_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   :							Tuple     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				self.get_feature_extractor()
											A_			:  Optional[Any]			=				self.get_tokenizer()

											A_			:  Optional[int]			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

											A_			:  Tuple			=				'''This is a test string'''

											A_			:  Optional[Any]			=				processor(text=_lowerCamelCase     )

											A_			:  Tuple			=				tokenizer(_lowerCamelCase     )

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







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											A_			:  Any			=				self.get_feature_extractor()
											A_			:  int			=				self.get_tokenizer()

											A_			:  Any			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

											A_			:  Dict			=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											A_			:  Optional[int]			=				processor.batch_decode(sequences=_lowerCamelCase     )
											A_			:  Optional[Any]			=				tokenizer.batch_decode(_lowerCamelCase     )

											self.assertListEqual(_lowerCamelCase				,			_lowerCamelCase     )







						def    _a     (  self   :							str     ):





											"""simple docstring"""







											A_			:  Any			=				self.get_feature_extractor()
											A_			:  int			=				self.get_tokenizer()

											A_			:  str			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

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







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Optional[Any]			=				self.get_feature_extractor(padding_side='''left'''     )
											A_			:  Optional[int]			=				self.get_tokenizer()

											A_			:  Union[str, Any]			=				MusicgenProcessor(tokenizer=_lowerCamelCase				,			feature_extractor=_lowerCamelCase     )

											A_			:  Optional[Any]			=				[floats_list((1, x)     )[0] for x in range(5				,			20				,			5     )]
											A_			:  List[str]			=				processor(_lowerCamelCase     ).padding_mask

											A_			:  int			=				np.asarray(floats_list((3, 20)     )     )[:, None, :]
											A_			:  str			=				processor.batch_decode(_lowerCamelCase				,			padding_mask=_lowerCamelCase     )

											self.assertIsInstance(_lowerCamelCase				,			_lowerCamelCase     )

											for audio in decoded_audios:
																self.assertIsInstance(_lowerCamelCase				,			np.ndarray     )

											self.assertTrue(decoded_audios[0].shape == (1, 10)     )
											self.assertTrue(decoded_audios[1].shape == (1, 15)     )
											self.assertTrue(decoded_audios[2].shape == (1, 20)     )









'''simple docstring'''




from __future__ import annotations

from statistics import mean




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  Dict			=				2
					A_			:  List[str]			=				list(lowerCamelCase__      )
					A_			:  int			=				[0] * len(lowerCamelCase__      )
					A_			:  int			=				0
					while True:
										A_			:  List[str]			=				True
										for i, burst_time in enumerate(lowerCamelCase__      ):
															if rem_burst_times[i] > 0:
																				A_			:  Any			=				False
																				if rem_burst_times[i] > quantum:
																									t += quantum
																									rem_burst_times[i] -= quantum
																				else:
																									t += rem_burst_times[i]
																									A_			:  Any			=				t - burst_time
																									A_			:  Optional[int]			=				0
										if done is True:
															return waiting_times




def     snake_case__     (			lowerCamelCase__					: list[int]      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					return [burst + waiting for burst, waiting in zip(lowerCamelCase__      ,		lowerCamelCase__      )]


if __name__ == "__main__":
							snake_case__ 							=    [3, 5, 7]
							snake_case__ 							=    calculate_waiting_times(burst_times)
							snake_case__ 							=    calculate_turn_around_times(burst_times, waiting_times)
							print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
							for i, burst_time in enumerate(burst_times):
														print(
														    F'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
														    F'{turn_around_times[i]}'
														)
							print(F'\nAverage waiting time = {mean(waiting_times):.5f}')
							print(F'Average turn around time = {mean(turn_around_times):.5f}')



