




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast


@require_vision
class  lowerCamelCase     (		unittest.TestCase    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : int	)    -> Optional[int]:
								__magic_name__				:						Optional[Any]     				=				tempfile.mkdtemp()

								__magic_name__				:						Tuple     				=				CLIPImageProcessor()
								__magic_name__				:						Optional[Any]     				=				BertTokenizer.from_pretrained(
								    '''hf-internal-testing/tiny-random-BertModel'''							,    model_input_names=['''input_ids''', '''attention_mask''']	)

								__magic_name__				:						Any     				=				GitProcessor(lowerCamelCase_							,    lowerCamelCase_	)

								processor.save_pretrained(self.tmpdirname	)

			def 				UpperCAmelCase__							(			self      : Dict							,    **lowerCamelCase_      : Dict	)    -> str:
								return AutoProcessor.from_pretrained(self.tmpdirname							,    **lowerCamelCase_	).tokenizer

			def 				UpperCAmelCase__							(			self      : Optional[int]							,    **lowerCamelCase_      : Dict	)    -> str:
								return AutoProcessor.from_pretrained(self.tmpdirname							,    **lowerCamelCase_	).image_processor

			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> List[str]:
								shutil.rmtree(self.tmpdirname	)

			def 				UpperCAmelCase__							(			self      : int	)    -> Tuple:
								__magic_name__				:						str     				=				[np.random.randint(255							,    size=(3, 30, 400)							,    dtype=np.uinta	)]

								__magic_name__				:						Any     				=				[Image.fromarray(np.moveaxis(lowerCamelCase_							,    0							,    -1	)	) for x in image_inputs]

								return image_inputs

			def 				UpperCAmelCase__							(			self      : List[str]	)    -> List[Any]:
								__magic_name__				:						Optional[Any]     				=				GitProcessor(tokenizer=self.get_tokenizer()							,    image_processor=self.get_image_processor()	)
								processor.save_pretrained(self.tmpdirname	)

								__magic_name__				:						List[Any]     				=				self.get_tokenizer(bos_token='''(BOS)'''							,    eos_token='''(EOS)'''	)
								__magic_name__				:						Any     				=				self.get_image_processor(do_normalize=lowerCamelCase_							,    padding_value=1.0	)

								__magic_name__				:						Any     				=				GitProcessor.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.image_processor.to_json_string()							,    image_processor_add_kwargs.to_json_string()	)
								self.assertIsInstance(processor.image_processor							,    lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : int	)    -> Dict:
								__magic_name__				:						Dict     				=				self.get_image_processor()
								__magic_name__				:						List[Any]     				=				self.get_tokenizer()

								__magic_name__				:						Union[str, Any]     				=				GitProcessor(tokenizer=lowerCamelCase_							,    image_processor=lowerCamelCase_	)

								__magic_name__				:						List[Any]     				=				self.prepare_image_inputs()

								__magic_name__				:						Dict     				=				image_processor(lowerCamelCase_							,    return_tensors='''np'''	)
								__magic_name__				:						Optional[int]     				=				processor(images=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 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> Optional[Any]:
								__magic_name__				:						List[Any]     				=				self.get_image_processor()
								__magic_name__				:						Dict     				=				self.get_tokenizer()

								__magic_name__				:						int     				=				GitProcessor(tokenizer=lowerCamelCase_							,    image_processor=lowerCamelCase_	)

								__magic_name__				:						int     				=				'''lower newer'''

								__magic_name__				:						str     				=				processor(text=lowerCamelCase_	)

								__magic_name__				:						Tuple     				=				tokenizer(lowerCamelCase_							,    return_token_type_ids=lowerCamelCase_	)

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

			def 				UpperCAmelCase__							(			self      : List[str]	)    -> List[str]:
								__magic_name__				:						Any     				=				self.get_image_processor()
								__magic_name__				:						Optional[int]     				=				self.get_tokenizer()

								__magic_name__				:						Tuple     				=				GitProcessor(tokenizer=lowerCamelCase_							,    image_processor=lowerCamelCase_	)

								__magic_name__				:						List[str]     				=				'''lower newer'''
								__magic_name__				:						Union[str, Any]     				=				self.prepare_image_inputs()

								__magic_name__				:						Union[str, Any]     				=				processor(text=lowerCamelCase_							,    images=lowerCamelCase_	)

								self.assertListEqual(list(inputs.keys()	)							,    ['''input_ids''', '''attention_mask''', '''pixel_values''']	)

								# test if it raises when no input is passed
								with pytest.raises(lowerCamelCase_	):
													processor()

			def 				UpperCAmelCase__							(			self      : Tuple	)    -> Tuple:
								__magic_name__				:						Dict     				=				self.get_image_processor()
								__magic_name__				:						Any     				=				self.get_tokenizer()

								__magic_name__				:						int     				=				GitProcessor(tokenizer=lowerCamelCase_							,    image_processor=lowerCamelCase_	)

								__magic_name__				:						int     				=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__magic_name__				:						Optional[int]     				=				processor.batch_decode(lowerCamelCase_	)
								__magic_name__				:						int     				=				tokenizer.batch_decode(lowerCamelCase_	)

								self.assertListEqual(lowerCamelCase_							,    lowerCamelCase_	)


			def 				UpperCAmelCase__							(			self      : List[str]	)    -> Dict:
								__magic_name__				:						List[Any]     				=				self.get_image_processor()
								__magic_name__				:						str     				=				self.get_tokenizer()

								__magic_name__				:						List[str]     				=				GitProcessor(tokenizer=lowerCamelCase_							,    image_processor=lowerCamelCase_	)

								__magic_name__				:						Tuple     				=				'''lower newer'''
								__magic_name__				:						Union[str, Any]     				=				self.prepare_image_inputs()

								__magic_name__				:						Optional[Any]     				=				processor(text=lowerCamelCase_							,    images=lowerCamelCase_	)

								# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
								self.assertListEqual(list(inputs.keys()	)							,    ['''input_ids''', '''attention_mask''', '''pixel_values''']	)









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()



