




import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	Optional[Any]       ,     a       :	int       ,     a       :	List[Any]=13       ,     a       :	List[str]=7       ,     a       :	int=True       ,     a       :	Any=True       ,     a       :	Optional[int]=False       ,     a       :	Tuple=True       ,     a       :	str=99       ,     a       :	List[Any]=32       ,     a       :	Optional[int]=5       ,     a       :	Any=4       ,     a       :	Dict=64       ,     a       :	Any="gelu"       ,     a       :	int=0.1       ,     a       :	Optional[Any]=0.1       ,     a       :	Dict=512       ,     a       :	Tuple=16       ,     a       :	int=2       ,     a       :	int=0.02       ,     a       :	Union[str, Any]=3       ,     a       :	Any=4       ,     a       :	Any=None       ,     a       :	List[str]=2       ,     a       :	str=2       ,     a       :	str=2       ,     a       :	Optional[Any]=2       ,     a       :	Dict=4       ,     a       :	Optional[int]=1       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					parent
										SCREAMING_SNAKE_CASE           =					batch_size
										SCREAMING_SNAKE_CASE           =					seq_length
										SCREAMING_SNAKE_CASE           =					is_training
										SCREAMING_SNAKE_CASE           =					use_input_mask
										SCREAMING_SNAKE_CASE           =					use_token_type_ids
										SCREAMING_SNAKE_CASE           =					use_labels
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					type_sequence_label_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					num_labels
										SCREAMING_SNAKE_CASE           =					num_choices
										SCREAMING_SNAKE_CASE           =					scope
										SCREAMING_SNAKE_CASE           =					q_groups
										SCREAMING_SNAKE_CASE           =					k_groups
										SCREAMING_SNAKE_CASE           =					v_groups
										SCREAMING_SNAKE_CASE           =					post_attention_groups
										SCREAMING_SNAKE_CASE           =					intermediate_groups
										SCREAMING_SNAKE_CASE           =					output_groups





							def    _UpperCAmelCase     (					self       :	int			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size			)

										SCREAMING_SNAKE_CASE           =					None
										if self.use_input_mask:
													SCREAMING_SNAKE_CASE           =					random_attention_mask([self.batch_size, self.seq_length]			)

										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					None
										if self.use_labels:
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels			)
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size]       ,     self.num_choices			)

										SCREAMING_SNAKE_CASE           =					self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[Any]:
										return SqueezeBertConfig(
										    embedding_size=self.hidden_size       ,     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       ,     attention_probs_dropout_prob=self.hidden_dropout_prob       ,     attention_dropout=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     initializer_range=self.initializer_range       ,     q_groups=self.q_groups       ,     k_groups=self.k_groups       ,     v_groups=self.v_groups       ,     post_attention_groups=self.post_attention_groups       ,     intermediate_groups=self.intermediate_groups       ,     output_groups=self.output_groups       ,     )





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	int       ,     a       :	Dict       ,     a       :	int       ,     a       :	Optional[Any]       ,     a       :	Dict       ,     a       :	Optional[int]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					SqueezeBertModel(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     a			)
										SCREAMING_SNAKE_CASE           =					model(a			)
										self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	List[str]       ,     a       :	int       ,     a       :	int       ,     a       :	Dict       ,     a       :	str       ,     a       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					SqueezeBertForMaskedLM(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a       ,     labels=a			)
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)			)





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Any]       ,     a       :	Optional[Any]       ,     a       :	Optional[Any]       ,     a       :	Tuple       ,     a       :	List[Any]       ,     a       :	str			)			->					int:
										SCREAMING_SNAKE_CASE           =					SqueezeBertForQuestionAnswering(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     attention_mask=a       ,     start_positions=a       ,     end_positions=a			)
										self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)			)
										self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)			)





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	Optional[Any]       ,     a       :	str       ,     a       :	List[Any]       ,     a       :	str       ,     a       :	Any       ,     a       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					SqueezeBertForSequenceClassification(a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a       ,     labels=a			)
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)			)





							def    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	str       ,     a       :	Any       ,     a       :	List[Any]       ,     a       :	int       ,     a       :	str       ,     a       :	Dict			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					SqueezeBertForTokenClassification(config=a			)
										model.to(a			)
										model.eval()

										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a       ,     labels=a			)
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	int       ,     a       :	Dict       ,     a       :	Union[str, Any]       ,     a       :	str       ,     a       :	Dict       ,     a       :	List[str]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.num_choices
										SCREAMING_SNAKE_CASE           =					SqueezeBertForMultipleChoice(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					input_ids.unsqueeze(1			).expand(-1       ,     self.num_choices       ,     -1			).contiguous()
										SCREAMING_SNAKE_CASE           =					input_mask.unsqueeze(1			).expand(-1       ,     self.num_choices       ,     -1			).contiguous()
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     attention_mask=a       ,     labels=a       ,     )
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)			)







							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.prepare_config_and_inputs()
										((SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE)     ,  (SCREAMING_SNAKE_CASE))           =					config_and_inputs
										SCREAMING_SNAKE_CASE           =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict





@require_torch
class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     (
							    (
							        SqueezeBertModel,
							        SqueezeBertForMaskedLM,
							        SqueezeBertForMultipleChoice,
							        SqueezeBertForQuestionAnswering,
							        SqueezeBertForSequenceClassification,
							        SqueezeBertForTokenClassification,
							    )
							    if is_torch_available()
							    else None
							)
							a__						  =     (
							    {
							        '''feature-extraction''': SqueezeBertModel,
							        '''fill-mask''': SqueezeBertForMaskedLM,
							        '''question-answering''': SqueezeBertForQuestionAnswering,
							        '''text-classification''': SqueezeBertForSequenceClassification,
							        '''token-classification''': SqueezeBertForTokenClassification,
							        '''zero-shot''': SqueezeBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a__						  =     False
							a__						  =     True
							a__						  =     False





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					SqueezeBertModelTester(self			)
										SCREAMING_SNAKE_CASE           =					ConfigTester(self       ,     config_class=a       ,     dim=37			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										self.config_tester.run_common_tests()





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*a			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*a			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*a			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*a			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*a			)







							@slow
							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Any:
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					SqueezeBertModel.from_pretrained(a			)
													self.assertIsNotNone(a			)





@require_sentencepiece
@require_tokenizers
@require_torch
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""			)

										SCREAMING_SNAKE_CASE           =					torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]			)
										SCREAMING_SNAKE_CASE           =					model(a			)[0]
										SCREAMING_SNAKE_CASE           =					torch.Size((1, 3)			)
										self.assertEqual(output.shape       ,     a			)
										SCREAMING_SNAKE_CASE           =					torch.tensor([[0.6401, -0.0349, -0.6041]]			)
										self.assertTrue(torch.allclose(a       ,     a       ,     atol=1E-4			)			)







from __future__ import annotations

import math
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''

			if num <= 0:
						SCREAMING_SNAKE_CASE           =					f"""{num}: Invalid input, please enter a positive integer."""
						raise ValueError(SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[True] * (num + 1)
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					2
			SCREAMING_SNAKE_CASE           =					int(math.sqrt(SCREAMING_SNAKE_CASE   )   )

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

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

						start += 1

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

			return prime


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






