






"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
       from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

       from .test_feature_extraction_speechta import floats_list


lowercase_								=	get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')


@require_torch
class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					tempfile.mkdtemp()

     __A		     =					SpeechTaTokenizer(_lowerCamelCase    )
     tokenizer.save_pretrained(self.tmpdirname    )

     __A		     =					{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 1_60_00,
         '''do_normalize''': False,
         '''num_mel_bins''': 80,
         '''hop_length''': 16,
         '''win_length''': 64,
         '''win_function''': '''hann_window''',
         '''fmin''': 80,
         '''fmax''': 76_00,
         '''mel_floor''': 1e-10,
         '''reduction_factor''': 2,
         '''return_attention_mask''': True,
     }

     __A		     =					os.path.join(self.tmpdirname,			_lowerCamelCase    )
     with open(self.feature_extraction_file,			'''w''',			encoding='''utf-8'''    ) as fp:
       fp.write(json.dumps(_lowerCamelCase    ) + '''\n'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			**_lowerCamelCase					:	Optional[int]    ):

     '''simple docstring'''
     return SpeechTaTokenizer.from_pretrained(self.tmpdirname,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			**_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     shutil.rmtree(self.tmpdirname    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					self.get_tokenizer()
     __A		     =					self.get_feature_extractor()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     processor.save_pretrained(self.tmpdirname    )
     __A		     =					SpeechTaProcessor.from_pretrained(self.tmpdirname    )

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

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


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

     '''simple docstring'''
     __A		     =					SpeechTaProcessor(tokenizer=self.get_tokenizer(),			feature_extractor=self.get_feature_extractor()    )
     processor.save_pretrained(self.tmpdirname    )

     __A		     =					self.get_tokenizer(bos_token='''(BOS)''',			eos_token='''(EOS)'''    )
     __A		     =					self.get_feature_extractor(do_normalize=_lowerCamelCase,			padding_value=1.0    )

     __A		     =					SpeechTaProcessor.from_pretrained(
         self.tmpdirname,			bos_token='''(BOS)''',			eos_token='''(EOS)''',			do_normalize=_lowerCamelCase,			padding_value=1.0    )

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					floats_list((3, 10_00)    )

     __A		     =					feature_extractor(audio=_lowerCamelCase,			return_tensors='''np'''    )
     __A		     =					processor(audio=_lowerCamelCase,			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 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					floats_list((3, 10_00)    )

     __A		     =					feature_extractor(audio_target=_lowerCamelCase,			return_tensors='''np'''    )
     __A		     =					processor(audio_target=_lowerCamelCase,			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 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					'''This is a test string'''

     __A		     =					processor(text=_lowerCamelCase    )
     __A		     =					tokenizer(_lowerCamelCase    )

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					'''This is a test string'''

     __A		     =					processor(text_target=_lowerCamelCase    )
     __A		     =					tokenizer(_lowerCamelCase    )

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


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

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

     __A		     =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     __A		     =					processor.batch_decode(_lowerCamelCase    )
     __A		     =					tokenizer.batch_decode(_lowerCamelCase    )

     self.assertListEqual(_lowerCamelCase,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

     '''simple docstring'''
     __A		     =					self.get_feature_extractor()
     __A		     =					self.get_tokenizer()

     __A		     =					SpeechTaProcessor(tokenizer=_lowerCamelCase,			feature_extractor=_lowerCamelCase    )

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










import math
from collections.abc import Generator







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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)
										__magic_name__				:						Tuple     				=				int(math.sqrt(__A					)					) + 1
										for j in range(2				,__A					):
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		__A:						int					):





					'''simple docstring'''






					__magic_name__				:						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):
										__magic_name__				:						str     				=				int(math.sqrt(__A					)					) + 1
										for j in range(3				,__A				,2					):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i







def 		lowercase__ (		):





					'''simple docstring'''






					from timeit import timeit

					__magic_name__				:						Dict     				=				'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)
					print(timeit('''fast_primes(1_000_000_000_000)'''				,setup=__A				,number=1_0_0_0_0_0_0					)					)


if __name__ == "__main__":
	__lowerCamelCase :    Union[str, Any]      						=      int(input('''Calculate primes up to:\n>> ''').strip())
	for ret in primes(number):
		print(ret)
	benchmark()



