




"""simple docstring"""

import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_lowerCamelCase  :			int        =       random.Random()

if is_torch_available():
		import torch

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1.0  ,      _UpperCAmelCase=None  ,      _UpperCAmelCase=None	):




	"""simple docstring"""






	if rng is None:
		A_  :				Tuple	   =		global_rng

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

	return values

class        lowercase      (    unittest.TestCase):







				def __init__(							self   :					Optional[Any]      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					Union[str, Any]=7      ,     _lowerCamelCase   :					Any=4_00      ,     _lowerCamelCase   :					Optional[int]=20_00      ,     _lowerCamelCase   :					Union[str, Any]=1      ,     _lowerCamelCase   :					Dict=0.0      ,     _lowerCamelCase   :					List[str]=1_60_00      ,     _lowerCamelCase   :					Optional[Any]=True      ,     _lowerCamelCase   :					Union[str, Any]=True      ,     ):



					"""simple docstring"""

					A_  :				Dict	   =		parent
					A_  :				Union[str, Any]	   =		batch_size
					A_  :				Any	   =		min_seq_length
					A_  :				List[Any]	   =		max_seq_length
					A_  :				Dict	   =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					A_  :				Dict	   =		feature_size
					A_  :				Tuple	   =		padding_value
					A_  :				Union[str, Any]	   =		sampling_rate
					A_  :				Union[str, Any]	   =		return_attention_mask
					A_  :				Any	   =		do_normalize







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return {
					    "feature_size": self.feature_size,
					    "padding_value": self.padding_value,
					    "sampling_rate": self.sampling_rate,
					    "return_attention_mask": self.return_attention_mask,
					    "do_normalize": self.do_normalize,
					}







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					Optional[int]=False      ,     _lowerCamelCase   :					Union[str, Any]=False       ):



					"""simple docstring"""

					def _flatten(_lowerCamelCase   :					Union[str, Any]       ):
						return list(itertools.chain(*_lowerCamelCase       )       )

					if equal_length:
						A_  :				str	   =		floats_list((self.batch_size, self.max_seq_length)       )
					else:
						# make sure that inputs increase in size
						A_  :				Optional[Any]	   =		[
						    _flatten(floats_list((x, self.feature_size)       )       )
						    for x in range(self.min_seq_length      ,     self.max_seq_length      ,     self.seq_length_diff       )
						]

					if numpify:
						A_  :				Dict	   =		[np.asarray(_lowerCamelCase       ) for x in speech_inputs]

					return speech_inputs

@require_torch
@require_torchaudio
class        lowercase      (    __UpperCAmelCase     ,	unittest.TestCase):
				__lowerCAmelCase    :    List[str]         =   ASTFeatureExtractor







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		ASTFeatureExtractionTester(self       )







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
					# create three inputs of length 800, 1000, and 1200
					A_  :				List[str]	   =		[floats_list((1, x)       )[0] for x in range(8_00      ,     14_00      ,     2_00       )]
					A_  :				Tuple	   =		[np.asarray(_lowerCamelCase       ) for speech_input in speech_inputs]

					# Test not batched input
					A_  :				Union[str, Any]	   =		feat_extract(speech_inputs[0]      ,     return_tensors='''np'''       ).input_values
					A_  :				List[Any]	   =		feat_extract(np_speech_inputs[0]      ,     return_tensors='''np'''       ).input_values
					self.assertTrue(np.allclose(_lowerCamelCase      ,     _lowerCamelCase      ,     atol=1E-3       )       )

					# Test batched
					A_  :				List[Any]	   =		feat_extract(_lowerCamelCase      ,     padding=_lowerCamelCase      ,     return_tensors='''np'''       ).input_values
					A_  :				Optional[int]	   =		feat_extract(_lowerCamelCase      ,     padding=_lowerCamelCase      ,     return_tensors='''np'''       ).input_values
					for enc_seq_a, enc_seq_a in zip(_lowerCamelCase      ,     _lowerCamelCase       ):
						self.assertTrue(np.allclose(_lowerCamelCase      ,     _lowerCamelCase      ,     atol=1E-3       )       )

					# Test 2-D numpy arrays are batched.
					A_  :				Tuple	   =		[floats_list((1, x)       )[0] for x in (8_00, 8_00, 8_00)]
					A_  :				Dict	   =		np.asarray(_lowerCamelCase       )
					A_  :				Optional[int]	   =		feat_extract(_lowerCamelCase      ,     return_tensors='''np'''       ).input_values
					A_  :				Optional[Any]	   =		feat_extract(_lowerCamelCase      ,     return_tensors='''np'''       ).input_values
					for enc_seq_a, enc_seq_a in zip(_lowerCamelCase      ,     _lowerCamelCase       ):
						self.assertTrue(np.allclose(_lowerCamelCase      ,     _lowerCamelCase      ,     atol=1E-3       )       )







				@require_torch
				def 					a_  (							self   :					Optional[Any]       ):



					"""simple docstring"""

					import torch

					A_  :				str	   =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
					A_  :				Any	   =		np.random.rand(1_00       ).astype(np.floataa       )
					A_  :				int	   =		np_speech_inputs.tolist()

					for inputs in [py_speech_inputs, np_speech_inputs]:
						A_  :				int	   =		feature_extractor.pad([{'''input_values''': inputs}]      ,     return_tensors='''np'''       )
						self.assertTrue(np_processed.input_values.dtype == np.floataa       )
						A_  :				int	   =		feature_extractor.pad([{'''input_values''': inputs}]      ,     return_tensors='''pt'''       )
						self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )







				def 					a_  (							self   :					str      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					from datasets import load_dataset

					A_  :				Optional[int]	   =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,     '''clean'''      ,     split='''validation'''       )
					# automatic decoding with librispeech
					A_  :				List[str]	   =		ds.sort('''id'''       ).select(range(_lowerCamelCase       )       )[:num_samples]['''audio''']

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







				@require_torch
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		torch.tensor(
					    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
					     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
					     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
					     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]       )
					# fmt: on

					A_  :				Optional[int]	   =		self._load_datasamples(1       )
					A_  :				List[Any]	   =		ASTFeatureExtractor()
					A_  :				str	   =		feature_extractor(_lowerCamelCase      ,     return_tensors='''pt'''       ).input_values
					self.assertEquals(input_values.shape      ,     (1, 10_24, 1_28)       )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]      ,     _lowerCamelCase      ,     atol=1E-4       )       )












"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





