


from collections.abc import Generator




def     __SCREAMING_SNAKE_CASE							(       )		->     Generator[int, None, None]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							List[Any]	    =	0, 1
						while True:
												__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	b, a + b
												yield b




def     __SCREAMING_SNAKE_CASE							(       lowercase_ = 1000      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	1
						__UpperCAmelCase			:							Union[str, Any]	    =	fibonacci_generator()
						while len(str(next(lowercase_      )      )      ) < n:
												answer += 1
						return answer + 1


if __name__ == "__main__":
				print(solution(int(str(input()).strip())))





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset

lowerCAmelCase       		=    random.Random()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=1.0      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







						if rng is None:
												__UpperCAmelCase			:							str	    =	global_rng

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

						return values




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=4_0_0				,     lowercase__=2_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=1_2_8				,     lowercase__=1				,     lowercase__=5_1_2				,     lowercase__=3_0				,     lowercase__=4_4_1_0_0				,     ):
							__UpperCAmelCase			:							Optional[Any]	    =	parent
							__UpperCAmelCase			:							Any	    =	batch_size
							__UpperCAmelCase			:							int	    =	min_seq_length
							__UpperCAmelCase			:							List[str]	    =	max_seq_length
							__UpperCAmelCase			:							Dict	    =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__UpperCAmelCase			:							Any	    =	spectrogram_length
							__UpperCAmelCase			:							List[Any]	    =	feature_size
							__UpperCAmelCase			:							Union[str, Any]	    =	num_audio_channels
							__UpperCAmelCase			:							Optional[int]	    =	hop_length
							__UpperCAmelCase			:							Tuple	    =	chunk_length
							__UpperCAmelCase			:							Any	    =	sampling_rate


	def  A(    self):
							return {
							    "spectrogram_length": self.spectrogram_length,
							    "feature_size": self.feature_size,
							    "num_audio_channels": self.num_audio_channels,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "sampling_rate": self.sampling_rate,
							}


	def  A(    self				,     lowercase__=False				,     lowercase__=False):
							def _flatten(lowercase__):
													return list(itertools.chain(*lowercase__))

							if equal_length:
													__UpperCAmelCase			:							str	    =	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
							else:
													# make sure that inputs increase in size
													__UpperCAmelCase			:							List[str]	    =	[
													    floats_list((x, self.feature_size))
													    for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff)
													]
							if numpify:
													__UpperCAmelCase			:							List[str]	    =	[np.asarray(lowercase__) for x in speech_inputs]
							return speech_inputs






@require_torch
@require_torchaudio
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[int]										=						TvltFeatureExtractor


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	TvltFeatureExtractionTester(self)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)
							self.assertTrue(hasattr(lowercase__				,     '''spectrogram_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''feature_size'''))
							self.assertTrue(hasattr(lowercase__				,     '''num_audio_channels'''))
							self.assertTrue(hasattr(lowercase__				,     '''hop_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''chunk_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''sampling_rate'''))


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							str	    =	feat_extract_first.save_pretrained(lowercase__)[0]
													check_json_file_has_correct_format(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class.from_pretrained(lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     '''feat_extract.json''')
													feat_extract_first.to_json_file(lowercase__)
													__UpperCAmelCase			:							str	    =	self.feature_extraction_class.from_json_file(lowercase__)

							__UpperCAmelCase			:							Any	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Tuple	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							List[str]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							# Initialize feature_extractor
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							# create three inputs of length 800, 1000, and 1200
							__UpperCAmelCase			:							Optional[int]	    =	[floats_list((1, x))[0] for x in range(8_0_0				,     1_4_0_0				,     2_0_0)]
							__UpperCAmelCase			:							int	    =	[np.asarray(lowercase__) for speech_input in speech_inputs]

							# Test not batched input
							__UpperCAmelCase			:							Dict	    =	feature_extractor(np_speech_inputs[0]				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test batched
							__UpperCAmelCase			:							List[str]	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test audio masking
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(
							    lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0				,     mask_audio=lowercase__).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test 2-D numpy arrays are batched.
							__UpperCAmelCase			:							Any	    =	[floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							__UpperCAmelCase			:							Optional[Any]	    =	np.asarray(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values
							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,     '''clean'''				,     split='''validation''')
							# automatic decoding with librispeech
							__UpperCAmelCase			:							int	    =	ds.sort('''id''').select(range(lowercase__))[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self._load_datasamples(1)
							__UpperCAmelCase			:							Tuple	    =	TvltFeatureExtractor()
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''pt''').audio_values

							self.assertEquals(audio_values.shape				,     (1, 1, 1_9_2, 1_2_8))

							__UpperCAmelCase			:							int	    =	torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]])
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     lowercase__				,     atol=1e-4))

