



import math

class 	__snake_case :







							'''simple docstring'''






							def __init__(							self					,					a_						):
											a__			 =						len(__UpperCamelCase						)
											a__			 =						[0] * (
											    4 * self.N
											)  # approximate the overall size of segment tree with array N
											self.build(1					,					0					,					self.N - 1						)


							def _a (							self					,					a_						):
											return idx * 2


							def _a (							self					,					a_						):
											return idx * 2 + 1


							def _a (							self					,					a_					,					a_					,					a_						):  # noqa: E741







											if l == r:
															a__			 =						A[l]
											else:
															a__			 =						(l + r) // 2
															self.build(self.left(__UpperCamelCase						)					,					__UpperCamelCase					,					__UpperCamelCase						)
															self.build(self.right(__UpperCamelCase						)					,					mid + 1					,					__UpperCamelCase						)
															a__			 =						max(self.st[self.left(__UpperCamelCase						)]					,					self.st[self.right(__UpperCamelCase						)]						)


							def _a (							self					,					a_					,					a_					,					a_						):
											return self.update_recursive(1					,					0					,					self.N - 1					,					a - 1					,					b - 1					,					__UpperCamelCase						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):  # noqa: E741







											if r < a or l > b:
															return True
											if l == r:
															a__			 =						val
															return True
											a__			 =						(l + r) // 2
											self.update_recursive(self.left(__UpperCamelCase						)					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase						)
											self.update_recursive(self.right(__UpperCamelCase						)					,					mid + 1					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase						)
											a__			 =						max(self.st[self.left(__UpperCamelCase						)]					,					self.st[self.right(__UpperCamelCase						)]						)
											return True


							def _a (							self					,					a_					,					a_						):
											return self.query_recursive(1					,					0					,					self.N - 1					,					a - 1					,					b - 1						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_						):  # noqa: E741







											if r < a or l > b:
															return -math.inf
											if l >= a and r <= b:
															return self.st[idx]
											a__			 =						(l + r) // 2
											a__			 =						self.query_recursive(self.left(__UpperCamelCase						)					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase						)
											a__			 =						self.query_recursive(self.right(__UpperCamelCase						)					,					mid + 1					,					__UpperCamelCase					,					__UpperCamelCase					,					__UpperCamelCase						)
											return max(__UpperCamelCase					,					__UpperCamelCase						)







							def _a (							self						):
											a__			 =						[]
											for i in range(1					,					N + 1						):
															show_list += [self.query(__UpperCamelCase					,					__UpperCamelCase						)]
											print(__UpperCamelCase						)


if __name__ == "__main__":
	UpperCAmelCase								=   [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
	UpperCAmelCase								=   15
	UpperCAmelCase								=   SegmentTree(A)
	print(segt.query(4, 6))
	print(segt.query(7, 11))
	print(segt.query(7, 12))
	segt.update(1, 3, 111)
	print(segt.query(1, 15))
	segt.update(7, 8, 235)
	segt.show_data()







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


UpperCAmelCase								=   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						tempfile.mkdtemp()

											a__			 =						SpeechTaTokenizer(a_						)
											tokenizer.save_pretrained(self.tmpdirname						)

											a__			 =						{
											    """feature_size""": 1,
											    """padding_value""": 0.0,
											    """sampling_rate""": 16_000,
											    """do_normalize""": False,
											    """num_mel_bins""": 80,
											    """hop_length""": 16,
											    """win_length""": 64,
											    """win_function""": """hann_window""",
											    """fmin""": 80,
											    """fmax""": 7_600,
											    """mel_floor""": 1E-1_0,
											    """reduction_factor""": 2,
											    """return_attention_mask""": True,
											}

											a__			 =						os.path.join(self.tmpdirname					,					a_						)
											with open(self.feature_extraction_file					,					"""w"""					,					encoding="""utf-8"""						) as fp:
															fp.write(json.dumps(a_						) + """\n"""						)


							def _a (							self					,					**a_						):
											return SpeechTaTokenizer.from_pretrained(self.tmpdirname					,					**a_						)


							def _a (							self					,					**a_						):
											return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname					,					**a_						)


							def _a (							self						):
											shutil.rmtree(self.tmpdirname						)


							def _a (							self						):
											a__			 =						self.get_tokenizer()
											a__			 =						self.get_feature_extractor()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											processor.save_pretrained(self.tmpdirname						)
											a__			 =						SpeechTaProcessor.from_pretrained(self.tmpdirname						)

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

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


							def _a (							self						):
											a__			 =						SpeechTaProcessor(tokenizer=self.get_tokenizer()					,					feature_extractor=self.get_feature_extractor()						)
											processor.save_pretrained(self.tmpdirname						)

											a__			 =						self.get_tokenizer(bos_token="""(BOS)"""					,					eos_token="""(EOS)"""						)
											a__			 =						self.get_feature_extractor(do_normalize=a_					,					padding_value=1.0						)

											a__			 =						SpeechTaProcessor.from_pretrained(
											    self.tmpdirname					,					bos_token="""(BOS)"""					,					eos_token="""(EOS)"""					,					do_normalize=a_					,					padding_value=1.0						)

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

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


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio=a_					,					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						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						floats_list((3, 1_000)						)

											a__			 =						feature_extractor(audio_target=a_					,					return_tensors="""np"""						)
											a__			 =						processor(audio_target=a_					,					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						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text=a_						)
											a__			 =						tokenizer(a_						)

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


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

											a__			 =						"""This is a test string"""

											a__			 =						processor(text_target=a_						)
											a__			 =						tokenizer(a_						)

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


							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

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

											a__			 =						processor.batch_decode(a_						)
											a__			 =						tokenizer.batch_decode(a_						)

											self.assertListEqual(a_					,					a_						)







							def _a (							self						):
											a__			 =						self.get_feature_extractor()
											a__			 =						self.get_tokenizer()

											a__			 =						SpeechTaProcessor(tokenizer=a_					,					feature_extractor=a_						)

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

