
'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list




class     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:List[Any])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """)
								UpperCamelCase_     :	Optional[Any]					     =     dict(zip(_SCREAMING_SNAKE_CASE			,    range(len(_SCREAMING_SNAKE_CASE))))

								UpperCamelCase_     :	Any					     =     {
								    """pad_token""": """<pad>""",
								    """unk_token""": """<unk>""",
								    """bos_token""": """<s>""",
								    """eos_token""": """</s>""",
								}
								UpperCamelCase_     :	Union[str, Any]					     =     {
								    """feature_size""": 1,
								    """padding_value""": 0.0,
								    """sampling_rate""": 1_6_0_0_0,
								    """return_attention_mask""": False,
								    """do_normalize""": True,
								}

								UpperCamelCase_     :	Any					     =     tempfile.mkdtemp()
								UpperCamelCase_     :	int					     =     os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCamelCase_     :	Optional[int]					     =     os.path.join(self.tmpdirname			,    _SCREAMING_SNAKE_CASE)
								with open(self.vocab_file			,    """w"""			,    encoding="""utf-8""") as fp:
														fp.write(json.dumps(_SCREAMING_SNAKE_CASE) + """\n""")

								with open(self.feature_extraction_file			,    """w"""			,    encoding="""utf-8""") as fp:
														fp.write(json.dumps(_SCREAMING_SNAKE_CASE) + """\n""")




		def 	_snake_case	(  self					:int			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     self.add_kwargs_tokens_map.copy()
								kwargs.update(_SCREAMING_SNAKE_CASE)
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,    **_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Union[str, Any]			,    **_SCREAMING_SNAKE_CASE					:Optional[Any])					->			List[str]:






								'''simple docstring'''






								return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,    **_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int])					->			Optional[int]:






								'''simple docstring'''






								shutil.rmtree(self.tmpdirname)




		def 	_snake_case	(  self					:List[Any])					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     self.get_tokenizer()
								UpperCamelCase_     :	Dict					     =     self.get_feature_extractor()

								UpperCamelCase_     :	Optional[int]					     =     WavaVecaProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE)

								processor.save_pretrained(self.tmpdirname)
								UpperCamelCase_     :	int					     =     WavaVecaProcessor.from_pretrained(self.tmpdirname)

								self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer.get_vocab())
								self.assertIsInstance(processor.tokenizer			,    _SCREAMING_SNAKE_CASE)

								self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor.to_json_string())
								self.assertIsInstance(processor.feature_extractor			,    _SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Tuple)					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     WavaVecaProcessor(tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor())
								processor.save_pretrained(self.tmpdirname)

								UpperCamelCase_     :	Optional[int]					     =     self.get_tokenizer(bos_token="""(BOS)"""			,    eos_token="""(EOS)""")
								UpperCamelCase_     :	int					     =     self.get_feature_extractor(do_normalize=_SCREAMING_SNAKE_CASE			,    padding_value=1.0)

								UpperCamelCase_     :	Optional[int]					     =     WavaVecaProcessor.from_pretrained(
								    self.tmpdirname			,    bos_token="""(BOS)"""			,    eos_token="""(EOS)"""			,    do_normalize=_SCREAMING_SNAKE_CASE			,    padding_value=1.0)

								self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.tokenizer			,    _SCREAMING_SNAKE_CASE)

								self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.feature_extractor			,    _SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int])					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.get_feature_extractor()
								UpperCamelCase_     :	List[Any]					     =     self.get_tokenizer()

								UpperCamelCase_     :	Optional[int]					     =     WavaVecaProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Dict					     =     floats_list((3, 1_0_0_0))

								UpperCamelCase_     :	List[Any]					     =     feature_extractor(_SCREAMING_SNAKE_CASE			,    return_tensors="""np""")
								UpperCamelCase_     :	List[Any]					     =     processor(_SCREAMING_SNAKE_CASE			,    return_tensors="""np""")

								for key in input_feat_extract.keys():
														self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1E-2)




		def 	_snake_case	(  self					:str)					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     self.get_feature_extractor()
								UpperCamelCase_     :	Union[str, Any]					     =     self.get_tokenizer()

								UpperCamelCase_     :	int					     =     WavaVecaProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Union[str, Any]					     =     """This is a test string"""

								UpperCamelCase_     :	Union[str, Any]					     =     processor(text=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Optional[int]					     =     tokenizer(_SCREAMING_SNAKE_CASE)

								for key in encoded_tok.keys():
														self.assertListEqual(encoded_tok[key]			,    encoded_processor[key])




		def 	_snake_case	(  self					:Any)					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     self.get_feature_extractor()
								UpperCamelCase_     :	List[str]					     =     self.get_tokenizer()

								UpperCamelCase_     :	Dict					     =     WavaVecaProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Union[str, Any]					     =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCamelCase_     :	Union[str, Any]					     =     processor.batch_decode(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[str]					     =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE)

								self.assertListEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)





		def 	_snake_case	(  self					:List[str])					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[int]					     =     self.get_feature_extractor()
								UpperCamelCase_     :	Any					     =     self.get_tokenizer()

								UpperCamelCase_     :	Union[str, Any]					     =     WavaVecaProcessor(tokenizer=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE)

								self.assertListEqual(
								    processor.model_input_names			,    feature_extractor.model_input_names			,    msg="""`processor` and `feature_extractor` model input names do not match"""			,    )






'''simple docstring'''


import math
from collections.abc import Generator
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)	))
						for i in (n for n in numbers if n > 1):
												# only need to check for factors up to sqrt(i)
												UpperCamelCase_     :	List[Any]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(2							,				UpperCAmelCase__	):
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Generator					     =     (i for i in range(1							,				(max_n + 1)							,				2	))
						# It's useless to test even numbers as they will not be prime
						if max_n > 2:
												yield 2  # Because 2 will not be tested, it's necessary to yield it now
						for i in (n for n in numbers if n > 1):
												UpperCamelCase_     :	Optional[int]					     =     int(math.sqrt(UpperCAmelCase__	)	) + 1
												for j in range(3							,				UpperCAmelCase__							,				2	):
																		# As we removed the even numbers, we don't need them now
																		if (i % j) == 0:
																								break
												else:
																		yield i
def 					__magic_name__(       ):
						from timeit import timeit

						UpperCamelCase_     :	int					     =     """from __main__ import slow_primes, primes, fast_primes"""
						print(timeit("""slow_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)
						print(timeit("""fast_primes(1_000_000_000_000)"""							,				setup=UpperCAmelCase__							,				number=1000000	)	)


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      int(input("Calculate primes up to:\n>> ").strip())
	for ret in primes(number):
		print(ret)
	benchmark()




