
'''simple docstring'''







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

_snake_case				:   Optional[int]     =				random.Random()



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]=1.0	,			_SCREAMING_SNAKE_CASE		:   str=None	,			_SCREAMING_SNAKE_CASE		:   Tuple=None  ):
						if rng is None:
												_SCREAMING_SNAKE_CASE				= global_rng

						_SCREAMING_SNAKE_CASE				= []
						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					,						UpperCamelCase					,						UpperCamelCase=7					,						UpperCamelCase=400					,						UpperCamelCase=2_000					,						UpperCamelCase=2_048					,						UpperCamelCase=128					,						UpperCamelCase=1					,						UpperCamelCase=512					,						UpperCamelCase=30					,						UpperCamelCase=44_100					,						):
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= min_seq_length
								_SCREAMING_SNAKE_CASE				= max_seq_length
								_SCREAMING_SNAKE_CASE				= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								_SCREAMING_SNAKE_CASE				= spectrogram_length
								_SCREAMING_SNAKE_CASE				= feature_size
								_SCREAMING_SNAKE_CASE				= num_audio_channels
								_SCREAMING_SNAKE_CASE				= hop_length
								_SCREAMING_SNAKE_CASE				= chunk_length
								_SCREAMING_SNAKE_CASE				= sampling_rate




		def     lowercase       (				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     lowercase       (				self					,						UpperCamelCase=False					,						UpperCamelCase=False      ):
								def _flatten(UpperCamelCase      ):
														return list(itertools.chain(*UpperCamelCase      )      )

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


@require_torch
@require_torchaudio
class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					List[Any]       =					TvltFeatureExtractor




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= TvltFeatureExtractionTester(self      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )
								self.assertTrue(hasattr(UpperCamelCase					,						"spectrogram_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"feature_size"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"num_audio_channels"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"hop_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"chunk_length"      )      )
								self.assertTrue(hasattr(UpperCamelCase					,						"sampling_rate"      )      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														_SCREAMING_SNAKE_CASE				= feat_extract_first.save_pretrained(UpperCamelCase      )[0]
														check_json_file_has_correct_format(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.feature_extraction_class.from_pretrained(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= feat_extract_first.to_dict()
								_SCREAMING_SNAKE_CASE				= feat_extract_second.to_dict()
								_SCREAMING_SNAKE_CASE				= dict_first.pop("mel_filters"      )
								_SCREAMING_SNAKE_CASE				= dict_second.pop("mel_filters"      )
								self.assertTrue(np.allclose(UpperCamelCase					,						UpperCamelCase      )      )
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								with tempfile.TemporaryDirectory() as tmpdirname:
														_SCREAMING_SNAKE_CASE				= os.path.join(UpperCamelCase					,						"feat_extract.json"      )
														feat_extract_first.to_json_file(UpperCamelCase      )
														_SCREAMING_SNAKE_CASE				= self.feature_extraction_class.from_json_file(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= feat_extract_first.to_dict()
								_SCREAMING_SNAKE_CASE				= feat_extract_second.to_dict()
								_SCREAMING_SNAKE_CASE				= dict_first.pop("mel_filters"      )
								_SCREAMING_SNAKE_CASE				= dict_second.pop("mel_filters"      )
								self.assertTrue(np.allclose(UpperCamelCase					,						UpperCamelCase      )      )
								self.assertEqual(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								# Initialize feature_extractor
								_SCREAMING_SNAKE_CASE				= self.feature_extraction_class(**self.feat_extract_dict      )

								# create three inputs of length 800, 1000, and 1200
								_SCREAMING_SNAKE_CASE				= [floats_list((1, x)      )[0] for x in range(800					,						1_400					,						200      )]
								_SCREAMING_SNAKE_CASE				= [np.asarray(UpperCamelCase      ) for speech_input in speech_inputs]

								# Test not batched input
								_SCREAMING_SNAKE_CASE				= feature_extractor(np_speech_inputs[0]					,						return_tensors="np"					,						sampling_rate=44_100      ).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
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100      ).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
								_SCREAMING_SNAKE_CASE				= feature_extractor(
								    UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100					,						mask_audio=UpperCamelCase      ).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.
								_SCREAMING_SNAKE_CASE				= [floats_list((1, x)      )[0] for x in (800, 800, 800)]
								_SCREAMING_SNAKE_CASE				= np.asarray(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="np"					,						sampling_rate=44_100      ).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     lowercase       (				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= load_dataset("hf-internal-testing/librispeech_asr_dummy"					,						"clean"					,						split="validation"      )
								# automatic decoding with librispeech
								_SCREAMING_SNAKE_CASE				= ds.sort("id"      ).select(range(UpperCamelCase      )      )[:num_samples]["audio"]

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self._load_datasamples(1      )
								_SCREAMING_SNAKE_CASE				= TvltFeatureExtractor()
								_SCREAMING_SNAKE_CASE				= feature_extractor(UpperCamelCase					,						return_tensors="pt"      ).audio_values

								self.assertEquals(audio_values.shape					,						(1, 1, 192, 128)      )

								_SCREAMING_SNAKE_CASE				= torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]					,						UpperCamelCase					,						atol=1e-4      )      )

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= int(_SCREAMING_SNAKE_CASE  )
						if n_element < 1:
												_SCREAMING_SNAKE_CASE				= ValueError("a should be a positive number"  )
												raise my_error

						_SCREAMING_SNAKE_CASE				= [1]
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= (0, 0, 0)
						_SCREAMING_SNAKE_CASE				= 1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2	,			hamming_list[j] * 3	,			hamming_list[k] * 5  )  )
												index += 1
						return hamming_list


if __name__ == "__main__":
						_snake_case				:   int     =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
						print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
						_snake_case				:   str     =				hamming(int(n))
						print("""-----------------------------------------------------""")
						print(F"The list with nth numbers is: {hamming_numbers}")
						print("""-----------------------------------------------------""")