


"""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


lowercase  		=			random.Random()



def 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      List[str]=1.0				,    _UpperCAmelCase    :      Union[str, Any]=None				,    _UpperCAmelCase    :      Any=None	)							->	int:

	'''simple docstring'''


	if rng is None:
		snake_case__  :						Dict		=      global_rng

	snake_case__  :						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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					**lowerCamelCase__)   ->	Tuple:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								shutil.rmtree(self.tmpdirname)




							def        UpperCAmelCase (						self)   ->	Any:







								'''simple docstring'''




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

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

								processor.save_pretrained(self.tmpdirname)
								snake_case__  :						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        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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

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

								snake_case__  :						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        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




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

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

								snake_case__  :						Union[str, Any]		=      floats_list((3, 1_000))

								snake_case__  :						Tuple		=      feature_extractor(_lowerCamelCase	,					return_tensors="np")
								snake_case__  :						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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




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

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

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

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

								snake_case__  :						Tuple		=      tokenizer(_lowerCamelCase)

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




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




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

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

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

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

								self.assertListEqual(_lowerCamelCase	,					_lowerCamelCase)




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




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

								snake_case__  :						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        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__  :						Optional[Any]		=      self.get_feature_extractor(padding_side="left")
								snake_case__  :						Optional[int]		=      self.get_tokenizer()

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

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

								snake_case__  :						int		=      np.asarray(floats_list((3, 20)))[:, None, :]
								snake_case__  :						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'''




import os




def     snake_case__     (			) -> int:
					A_			:  List[str]			=				os.path.dirname(os.path.realpath(lowerCamelCase__      )      )
					A_			:  str			=				os.path.join(lowerCamelCase__      ,		'''triangle.txt'''      )

					with open(lowerCamelCase__      ) as in_file:
										A_			:  List[str]			=				[[int(lowerCamelCase__      ) for i in line.split()] for line in in_file]

					while len(lowerCamelCase__      ) != 1:
										A_			:  List[str]			=				triangle.pop()
										A_			:  Optional[Any]			=				triangle[-1]
										for j in range(len(lowerCamelCase__      ) - 1      ):
															curr_row[j] += max(last_row[j]      ,		last_row[j + 1]      )
					return triangle[0][0]


if __name__ == "__main__":
							print(solution())



