
'''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


__lowercase							:   Optional[Any]					=     random.Random()

if is_torch_available():
					import torch




def lowercase_	(   _lowercase			,       _lowercase=1.0			,       _lowercase=None			,       _lowercase=None				)     ->							Dict:
					'''simple docstring'''

					if rng is None:
										lowerCamelCase_					:      Tuple     =		global_rng

					lowerCamelCase_					:      int     =		[]
					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			,   A			,   A=7			,   A=4_0_0			,   A=2_0_0_0			,   A=1			,   A=0.0			,   A=1_6_0_0_0			,   A=True			,   A=True			,   ):
								lowerCamelCase_					:      Any     =		parent
								lowerCamelCase_					:      Union[str, Any]     =		batch_size
								lowerCamelCase_					:      Tuple     =		min_seq_length
								lowerCamelCase_					:      Any     =		max_seq_length
								lowerCamelCase_					:      Any     =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								lowerCamelCase_					:      List[Any]     =		feature_size
								lowerCamelCase_					:      int     =		padding_value
								lowerCamelCase_					:      Optional[Any]     =		sampling_rate
								lowerCamelCase_					:      Tuple     =		return_attention_mask
								lowerCamelCase_					:      List[Any]     =		do_normalize

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

								if equal_length:
													lowerCamelCase_					:      List[str]     =		floats_list((self.batch_size, self.max_seq_length)      )
								else:
													# make sure that inputs increase in size
													lowerCamelCase_					:      int     =		[
													    _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:
													lowerCamelCase_					:      List[str]     =		[np.asarray(A      ) for x in speech_inputs]

								return speech_inputs

@require_torch
@require_torchaudio
class 	__lowercase				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		List[str]	      =			ASTFeatureExtractor

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		ASTFeatureExtractionTester(self      )

			def        UpperCAmelCase__ (self      ):
								# Tests that all call wrap to encode_plus and batch_encode_plus
								lowerCamelCase_					:      Union[str, Any]     =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								# create three inputs of length 800, 1000, and 1200
								lowerCamelCase_					:      Optional[Any]     =		[floats_list((1, x)      )[0] for x in range(8_0_0			,   1_4_0_0			,   2_0_0      )]
								lowerCamelCase_					:      List[Any]     =		[np.asarray(A      ) for speech_input in speech_inputs]

								# Test not batched input
								lowerCamelCase_					:      Any     =		feat_extract(speech_inputs[0]			,   return_tensors='''np'''      ).input_values
								lowerCamelCase_					:      Dict     =		feat_extract(np_speech_inputs[0]			,   return_tensors='''np'''      ).input_values
								self.assertTrue(np.allclose(A			,   A			,   atol=1E-3      )      )

								# Test batched
								lowerCamelCase_					:      Any     =		feat_extract(A			,   padding=A			,   return_tensors='''np'''      ).input_values
								lowerCamelCase_					:      str     =		feat_extract(A			,   padding=A			,   return_tensors='''np'''      ).input_values
								for enc_seq_a, enc_seq_a in zip(A			,   A      ):
													self.assertTrue(np.allclose(A			,   A			,   atol=1E-3      )      )

								# Test 2-D numpy arrays are batched.
								lowerCamelCase_					:      Any     =		[floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								lowerCamelCase_					:      Optional[int]     =		np.asarray(A      )
								lowerCamelCase_					:      Union[str, Any]     =		feat_extract(A			,   return_tensors='''np'''      ).input_values
								lowerCamelCase_					:      int     =		feat_extract(A			,   return_tensors='''np'''      ).input_values
								for enc_seq_a, enc_seq_a in zip(A			,   A      ):
													self.assertTrue(np.allclose(A			,   A			,   atol=1E-3      )      )

			@require_torch
			def        UpperCAmelCase__ (self      ):
								import torch

								lowerCamelCase_					:      Optional[Any]     =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
								lowerCamelCase_					:      Dict     =		np.random.rand(1_0_0      ).astype(np.floataa      )
								lowerCamelCase_					:      List[Any]     =		np_speech_inputs.tolist()

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

			def        UpperCAmelCase__ (self			,   A      ):
								from datasets import load_dataset

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

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






			@require_torch
			def        UpperCAmelCase__ (self      ):
								# fmt: off
								lowerCamelCase_					:      Union[str, Any]     =		torch.tensor(
								    [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
								     -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
								     -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
								     -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]      )
								# fmt: on

								lowerCamelCase_					:      str     =		self._load_datasamples(1      )
								lowerCamelCase_					:      str     =		ASTFeatureExtractor()
								lowerCamelCase_					:      Tuple     =		feature_extractor(A			,   return_tensors='''pt'''      ).input_values
								self.assertEquals(input_values.shape			,   (1, 1_0_2_4, 1_2_8)      )
								self.assertTrue(torch.allclose(input_values[0, 0, :3_0]			,   A			,   atol=1E-4      )      )




'''simple docstring'''


import itertools
import math




def lowercase_	(   _lowercase				)     ->							bool:
					'''simple docstring'''

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5			,       int(math.sqrt(_lowercase				) + 1				)			,       6				):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True




def lowercase_	(   )     ->							Optional[int]:
					'''simple docstring'''

					lowerCamelCase_					:      Union[str, Any]     =		2
					while True:
										if is_prime(_lowercase				):
															yield num
										num += 1




def lowercase_	(   _lowercase = 10_001				)     ->							int:
					'''simple docstring'''

					return next(itertools.islice(prime_generator()			,       nth - 1			,       _lowercase				)				)


if __name__ == "__main__":
					print(f'{solution() = }')


