



'''simple docstring'''




import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import BertGenerationDecoder, BertGenerationEncoder







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=99       ,      lowercase=32       ,      lowercase=5       ,      lowercase=4       ,      lowercase=37       ,      lowercase="gelu"       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=50       ,      lowercase=0.02       ,      lowercase=True       ,      lowercase=None       ,      ):
				A_	:						str	=     parent
				A_	:						int	=     batch_size
				A_	:						str	=     seq_length
				A_	:						Optional[Any]	=     is_training
				A_	:						List[Any]	=     use_input_mask
				A_	:						List[Any]	=     vocab_size
				A_	:						Optional[int]	=     hidden_size
				A_	:						Any	=     num_hidden_layers
				A_	:						int	=     num_attention_heads
				A_	:						List[Any]	=     intermediate_size
				A_	:						List[Any]	=     hidden_act
				A_	:						Optional[Any]	=     hidden_dropout_prob
				A_	:						str	=     attention_probs_dropout_prob
				A_	:						Dict	=     max_position_embeddings
				A_	:						List[str]	=     initializer_range
				A_	:						List[str]	=     use_labels
				A_	:						Optional[Any]	=     scope


	def     _a	(self							):
				A_	:						Optional[int]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Optional[Any]	=     None
				if self.use_input_mask:
							A_	:						Optional[Any]	=     random_attention_mask([self.batch_size, self.seq_length]							)

				if self.use_labels:
							A_	:						List[Any]	=     ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

				A_	:						Any	=     self.get_config()

				return config, input_ids, input_mask, token_labels


	def     _a	(self							):
				return BertGenerationConfig(
				    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      is_decoder=lowercase       ,      initializer_range=self.initializer_range       ,      )


	def     _a	(self							):
				(
				    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    
				)	:						Tuple	=     self.prepare_config_and_inputs()

				A_	:						List[Any]	=     True
				A_	:						Union[str, Any]	=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
				A_	:						Optional[Any]	=     ids_tensor([self.batch_size, self.seq_length]       ,      vocab_size=2							)

				return (
				    config,
				    input_ids,
				    input_mask,
				    token_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase       ,      ):
				A_	:						Dict	=     BertGenerationEncoder(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[Any]	=     model(lowercase       ,      attention_mask=lowercase							)
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase       ,      ):
				A_	:						List[str]	=     True
				A_	:						Any	=     BertGenerationEncoder(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Tuple	=     model(
				    lowercase       ,      attention_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      encoder_attention_mask=lowercase       ,      )
				A_	:						List[Any]	=     model(
				    lowercase       ,      attention_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      )
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      **lowercase       ,      ):
				A_	:						int	=     True
				A_	:						Optional[Any]	=     True
				A_	:						List[Any]	=     BertGenerationDecoder(config=lowercase							).to(lowercase							).eval()

				# first forward pass
				A_	:						Optional[int]	=     model(
				    lowercase       ,      attention_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      encoder_attention_mask=lowercase       ,      use_cache=lowercase       ,      )
				A_	:						Any	=     outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				A_	:						List[str]	=     ids_tensor((self.batch_size, 3)       ,      config.vocab_size							)
				A_	:						Tuple	=     ids_tensor((self.batch_size, 3)       ,      vocab_size=2							)

				# append to next input_ids and
				A_	:						List[Any]	=     torch.cat([input_ids, next_tokens]       ,      dim=-1							)
				A_	:						Any	=     torch.cat([input_mask, next_mask]       ,      dim=-1							)

				A_	:						int	=     model(
				    lowercase       ,      attention_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      encoder_attention_mask=lowercase       ,      output_hidden_states=lowercase       ,      )["""hidden_states"""][0]
				A_	:						Any	=     model(
				    lowercase       ,      attention_mask=lowercase       ,      encoder_hidden_states=lowercase       ,      encoder_attention_mask=lowercase       ,      past_key_values=lowercase       ,      output_hidden_states=lowercase       ,      )["""hidden_states"""][0]

				# select random slice
				A_	:						Optional[Any]	=     ids_tensor((1,)       ,      output_from_past.shape[-1]							).item()
				A_	:						Optional[int]	=     output_from_no_past[:, -3:, random_slice_idx].detach()
				A_	:						Union[str, Any]	=     output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(lowercase       ,      lowercase       ,      atol=1E-3							)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      *lowercase       ,      ):
				A_	:						Tuple	=     BertGenerationDecoder(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						List[str]	=     model(lowercase       ,      attention_mask=lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)							)


	def     _a	(self							):
				A_,    A_,    A_,    A_	:						Optional[Any]	=     self.prepare_config_and_inputs()
				A_	:						Union[str, Any]	=     {"""input_ids""": input_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					(BertGenerationDecoder,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Any					=					(
	    {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
	    if is_torch_available()
	    else {}
	)


	def     _a	(self							):
				A_	:						Optional[Any]	=     BertGenerationEncoderTester(self							)
				A_	:						Any	=     ConfigTester(self       ,      config_class=lowercase       ,      hidden_size=37							)


	def     _a	(self							):
				self.config_tester.run_common_tests()


	def     _a	(self							):
				A_	:						Dict	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_,    A_,    A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs()
				A_	:						Dict	=     """bert"""
				self.model_tester.create_and_check_model(lowercase       ,      lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				A_	:						Dict	=     self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*lowercase							)


	def     _a	(self							):
				A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase							)


	def     _a	(self							):
				# This regression test was failing with PyTorch < 1.3
				(
				    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    (
				    A_
				),    
				)	:						Union[str, Any]	=     self.model_tester.prepare_config_and_inputs_for_decoder()

				A_	:						Tuple	=     None

				self.model_tester.create_and_check_model_as_decoder(
				    lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      )


	def     _a	(self							):
				A_	:						List[str]	=     self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_for_causal_lm(*lowercase							)


	@slow
	def     _a	(self							):
				A_	:						str	=     BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
				self.assertIsNotNone(lowercase							)







@require_torch
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						Tuple	=     BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
				A_	:						List[str]	=     torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]							)
				with torch.no_grad():
							A_	:						int	=     model(lowercase							)[0]
				A_	:						Optional[Any]	=     torch.Size([1, 8, 1024]							)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						int	=     torch.tensor(
				    [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]]							)
				self.assertTrue(torch.allclose(output[:, :3, :3]       ,      lowercase       ,      atol=1E-4							)							)







@require_torch
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@slow
	def     _a	(self							):
				A_	:						Any	=     BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""							)
				A_	:						Optional[Any]	=     torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]							)
				with torch.no_grad():
							A_	:						List[str]	=     model(lowercase							)[0]
				A_	:						Dict	=     torch.Size([1, 8, 50358]							)
				self.assertEqual(output.shape       ,      lowercase							)
				A_	:						Dict	=     torch.tensor(
				    [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]]							)
				self.assertTrue(torch.allclose(output[:, :3, :3]       ,      lowercase       ,      atol=1E-4							)							)




'''simple docstring'''




from __future__ import annotations

import math



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if num <= 0:
						A_	:						List[Any]	=     f'{num}: Invalid input, please enter a positive integer.'
						raise ValueError(lowerCamelCase__    )

			A_	:						Dict	=     [True] * (num + 1)
			A_	:						List[Any]	=     []
			A_	:						Tuple	=     2
			A_	:						Optional[int]	=     int(math.sqrt(lowerCamelCase__    )    )

			while start <= end:
						# If start is a prime
						if sieve[start] is True:
									prime.append(lowerCamelCase__    )

									# Set multiples of start be False
									for i in range(start * start						,      num + 1						,      lowerCamelCase__    ):
												if sieve[i] is True:
															A_	:						List[Any]	=     False

						start += 1

			for j in range(end + 1						,      num + 1    ):
						if sieve[j] is True:
									prime.append(lowerCamelCase__    )

			return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))