



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin






class      UpperCamelCase    (						_UpperCAmelCase     ):
	# to overwrite at feature extractactor specific tests
	lowerCAmelCase	:   List[Any]				=	None
	lowerCAmelCase	:   List[Any]				=	None



	@property
	def 			__A							(       self		):
							return self.feat_extract_tester.prepare_feat_extract_dict()



	def 			__A							(       self		):
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							self.assertTrue(hasattr(UpperCAmelCase__			,	"feature_size"		)		)
							self.assertTrue(hasattr(UpperCAmelCase__			,	"sampling_rate"		)		)
							self.assertTrue(hasattr(UpperCAmelCase__			,	"padding_value"		)		)



	def 			__A							(       self		):
							A__							=       self.feat_extract_tester.prepare_inputs_for_common()
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							self.assertTrue(all(len(UpperCAmelCase__		) == len(UpperCAmelCase__		) for x, y in zip(UpperCAmelCase__			,	processed_features[input_name]		)		)		)

							A__							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase__		)
							A__							=       BatchFeature({input_name: speech_inputs}			,	tensor_type="np"		)

							A__							=       processed_features[input_name]

							if len(batch_features_input.shape		) < 3:
													A__							=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)



	@require_torch
	def 			__A							(       self		):
							A__							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase__		)
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}			,	tensor_type="pt"		)

							A__							=       processed_features[input_name]

							if len(batch_features_input.shape		) < 3:
													A__							=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)



	@require_tf
	def 			__A							(       self		):
							A__							=       self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCAmelCase__		)
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}			,	tensor_type="tf"		)

							A__							=       processed_features[input_name]

							if len(batch_features_input.shape		) < 3:
													A__							=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]		), self.feat_extract_tester.feature_size)		)



	def 			__A							(       self			,	UpperCAmelCase__=False		):
							def _inputs_have_equal_length(UpperCAmelCase__		):
													A__							=       len(input[0]		)
													for input_slice in input[1:]:
																			if len(UpperCAmelCase__		) != length:
																									return False
													return True

							def _inputs_are_equal(UpperCAmelCase__			,	UpperCAmelCase__		):
													if len(UpperCAmelCase__		) != len(UpperCAmelCase__		):
																			return False

													for input_slice_a, input_slice_a in zip(UpperCAmelCase__			,	UpperCAmelCase__		):
																			if not np.allclose(np.asarray(UpperCAmelCase__		)			,	np.asarray(UpperCAmelCase__		)			,	atol=1e-3		):
																									return False
													return True

							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCAmelCase__		)
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							A__							=       self.feat_extract_tester.seq_length_diff
							A__							=       self.feat_extract_tester.max_seq_length + pad_diff
							A__							=       self.feat_extract_tester.min_seq_length
							A__							=       self.feat_extract_tester.batch_size
							A__							=       self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding=UpperCAmelCase__		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[-1]		)		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="np"		)
							A__							=       input_a[input_name]

							# max_length parameter has to be provided when setting `padding="max_length"`
							with self.assertRaises(UpperCAmelCase__		):
													feat_extract.pad(UpperCAmelCase__			,	padding="max_length"		)[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=UpperCAmelCase__			,	return_tensors="np"		)
							A__							=       input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(_inputs_are_equal(UpperCAmelCase__			,	UpperCAmelCase__		)		)
							self.assertTrue(len(input_a[0]		) == pad_min_length		)
							self.assertTrue(len(input_a[1]		) == pad_min_length + pad_diff		)
							self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]		))		)
							self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)		)

							if feature_size > 1:
													self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size		)

							# test padding for `pad_to_multiple_of` for List[int] + numpy
							A__							=       feat_extract.pad(UpperCAmelCase__			,	pad_to_multiple_of=10		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	pad_to_multiple_of=10		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	pad_to_multiple_of=10			,	max_length=UpperCAmelCase__		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	pad_to_multiple_of=10			,	max_length=UpperCAmelCase__			,	return_tensors="np"			,	)
							A__							=       input_a[input_name]

							self.assertTrue(all(len(UpperCAmelCase__		) % 10 == 0 for x in input_a		)		)
							self.assertTrue(_inputs_are_equal(UpperCAmelCase__			,	UpperCAmelCase__		)		)

							A__							=       pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(UpperCAmelCase__		) == expected_mult_pad_length for x in input_a		)		)
							self.assertEqual(input_a.shape[:2]			,	(batch_size, expected_mult_pad_length)		)

							if feature_size > 1:
													self.assertTrue(input_a.shape[2] == feature_size		)

							# Check padding value is correct
							A__							=       (np.ones(self.feat_extract_tester.feature_size		) * feat_extract.padding_value).sum()
							self.assertTrue(
							    abs(np.asarray(input_a[0]		)[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		)
							    < 1e-3		)
							self.assertTrue(
							    abs(
							        np.asarray(input_a[1]		)[pad_min_length + pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)		)
							    < 1e-3		)
							self.assertTrue(
							    abs(
							        np.asarray(input_a[2]		)[pad_min_length + 2 * pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)		)
							    < 1e-3		)
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)		) < 1e-3		)
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)		)
							    < 1e-3		)



	def 			__A							(       self			,	UpperCAmelCase__=False		):
							def _inputs_have_equal_length(UpperCAmelCase__		):
													A__							=       len(input[0]		)
													for input_slice in input[1:]:
																			if len(UpperCAmelCase__		) != length:
																									return False
													return True

							def _inputs_are_equal(UpperCAmelCase__			,	UpperCAmelCase__		):
													if len(UpperCAmelCase__		) != len(UpperCAmelCase__		):
																			return False

													for input_slice_a, input_slice_a in zip(UpperCAmelCase__			,	UpperCAmelCase__		):
																			if not np.allclose(np.asarray(UpperCAmelCase__		)			,	np.asarray(UpperCAmelCase__		)			,	atol=1e-3		):
																									return False
													return True

							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCAmelCase__		)
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							# truncate to smallest
							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)			,	truncation=UpperCAmelCase__		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)		)
							A__							=       input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__		)		)

							# truncate to smallest with np
							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)			,	return_tensors="np"			,	truncation=UpperCAmelCase__			,	)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)			,	return_tensors="np"		)
							A__							=       input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(input_a.shape[1] == len(speech_inputs[0]		)		)

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__		)		)

							# truncate to middle
							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[1]		)			,	truncation=UpperCAmelCase__			,	return_tensors="np"			,	)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[1]		)			,	truncation=UpperCAmelCase__		)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[1]		)			,	return_tensors="np"		)
							A__							=       input_a[input_name]

							self.assertTrue(input_a.shape[1] == len(speech_inputs[1]		)		)
							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(_inputs_are_equal(UpperCAmelCase__			,	UpperCAmelCase__		)		)

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertTrue(len(input_a[-1]		) == len(speech_inputs[-1]		)		)

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(UpperCAmelCase__		):
													feat_extract.pad(UpperCAmelCase__			,	truncation=UpperCAmelCase__		)[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(UpperCAmelCase__		):
													feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	truncation=UpperCAmelCase__		)[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(UpperCAmelCase__		):
													feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	truncation=UpperCAmelCase__		)[input_name]

							# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
							with self.assertRaises(UpperCAmelCase__		):
													feat_extract.pad(UpperCAmelCase__			,	padding="max_length"			,	truncation=UpperCAmelCase__		)[input_name]

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							A__							=       12
							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)			,	pad_to_multiple_of=UpperCAmelCase__			,	truncation=UpperCAmelCase__			,	)
							A__							=       input_a[input_name]

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=len(speech_inputs[0]		)			,	pad_to_multiple_of=UpperCAmelCase__			,	)
							A__							=       input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							A__							=       len(speech_inputs[0]		)
							if expected_length % pad_to_multiple_of != 0:
													A__							=       ((len(speech_inputs[0]		) // pad_to_multiple_of) + 1) * pad_to_multiple_of

							self.assertTrue(len(input_a[0]		) == expected_length		)
							self.assertTrue(_inputs_have_equal_length(UpperCAmelCase__		)		)
							self.assertFalse(_inputs_have_equal_length(UpperCAmelCase__		)		)



	def 			__A							(       self		):
							self._check_padding(numpify=UpperCAmelCase__		)



	def 			__A							(       self		):
							self._check_padding(numpify=UpperCAmelCase__		)



	def 			__A							(       self		):
							self._check_truncation(numpify=UpperCAmelCase__		)



	def 			__A							(       self		):
							self._check_truncation(numpify=UpperCAmelCase__		)



	@require_torch
	def 			__A							(       self		):
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common()
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="np"		)[input_name]
							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="pt"		)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_pt.numpy().astype(np.floataa		).sum()		) < 1e-2		)



	@require_tf
	def 			__A							(       self		):
							A__							=       self.feature_extraction_class(**self.feat_extract_dict		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common()
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="np"		)[input_name]
							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="tf"		)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa		).sum() - input_tf.numpy().astype(np.floataa		).sum()		) < 1e-2		)



	def 			__A							(       self		):
							A__							=       self.feat_extract_dict
							A__							=       True
							A__							=       self.feature_extraction_class(**UpperCAmelCase__		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common()
							A__							=       [len(UpperCAmelCase__		) for x in speech_inputs]
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)

							A__							=       feat_extract.pad(UpperCAmelCase__			,	padding="longest"			,	return_tensors="np"		)
							self.assertIn("attention_mask"			,	UpperCAmelCase__		)
							self.assertListEqual(list(processed.attention_mask.shape		)			,	list(processed[input_name].shape[:2]		)		)
							self.assertListEqual(processed.attention_mask.sum(-1		).tolist()			,	UpperCAmelCase__		)



	def 			__A							(       self		):
							A__							=       self.feat_extract_dict
							A__							=       True
							A__							=       self.feature_extraction_class(**UpperCAmelCase__		)
							A__							=       self.feat_extract_tester.prepare_inputs_for_common()
							A__							=       [len(UpperCAmelCase__		) for x in speech_inputs]
							A__							=       feat_extract.model_input_names[0]

							A__							=       BatchFeature({input_name: speech_inputs}		)
							A__							=       min(UpperCAmelCase__		)

							A__							=       feat_extract.pad(
							    UpperCAmelCase__			,	padding="max_length"			,	max_length=UpperCAmelCase__			,	truncation=UpperCAmelCase__			,	return_tensors="np"		)
							self.assertIn("attention_mask"			,	UpperCAmelCase__		)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape		)			,	[processed_pad[input_name].shape[0], max_length]		)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1		).tolist()			,	[max_length for x in speech_inputs]		)










import math
from collections.abc import Iterator
from itertools import takewhile
def  UpperCamelCase						(       _A			:				int    )->       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(_A    ) + 1    )			,							6    ):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def  UpperCamelCase						(       )->       Iterator[int]:





						"""simple docstring"""





						A__							=       2
						while True:
												if is_prime(_A    ):
																		yield num
												num += 1
def  UpperCamelCase						(       _A			:				int = 2000000    )->       int:





						"""simple docstring"""





						return sum(takewhile(lambda _A    : x < n			,							prime_generator()    )    )


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





