




import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXModel,
		)




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	Optional[Any]       ,     a       :	Dict=13       ,     a       :	Dict=7       ,     a       :	str=True       ,     a       :	str=True       ,     a       :	Dict=True       ,     a       :	List[Any]=True       ,     a       :	Any=99       ,     a       :	int=64       ,     a       :	str=5       ,     a       :	Any=4       ,     a       :	int=37       ,     a       :	Dict="gelu"       ,     a       :	Optional[int]=0.1       ,     a       :	str=0.1       ,     a       :	int=512       ,     a       :	Any=16       ,     a       :	Tuple=2       ,     a       :	Tuple=0.02       ,     a       :	Union[str, Any]=3       ,     a       :	Optional[Any]=4       ,     a       :	str=None       ,     )			->					Optional[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           =					vocab_size - 1





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					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
										if self.use_labels:
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels			)

										SCREAMING_SNAKE_CASE           =					self.get_config()

										return config, input_ids, input_mask, token_labels





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Union[str, Any]:
										return GPTNeoXConfig(
										    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       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=a       ,     initializer_range=self.initializer_range       ,     pad_token_id=self.pad_token_id       ,     )





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

										SCREAMING_SNAKE_CASE           =					True

										return config, input_ids, input_mask, token_labels





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[str]       ,     a       :	List[str]       ,     a       :	str			)			->					str:
										SCREAMING_SNAKE_CASE           =					GPTNeoXModel(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=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       :	Any       ,     a       :	Tuple       ,     a       :	Tuple			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					GPTNeoXModel(a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a			)
										self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	Any       ,     a       :	List[str]       ,     a       :	Dict       ,     a       :	Union[str, Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					GPTNeoXForCausalLM(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       :	int       ,     a       :	Any       ,     a       :	int       ,     a       :	Dict       ,     a       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					GPTNeoXForQuestionAnswering(a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=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       :	Union[str, Any]       ,     a       :	List[Any]       ,     a       :	Optional[int]       ,     a       :	Any       ,     a       :	str			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					GPTNeoXForSequenceClassification(a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
										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[Any]       ,     a       :	int       ,     a       :	Dict       ,     a       :	Optional[Any]       ,     a       :	Optional[int]			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					GPTNeoXForTokenClassification(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       :	Any       ,     a       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	Optional[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					True
										SCREAMING_SNAKE_CASE           =					GPTNeoXForCausalLM(config=a			)
										model.to(a			)
										model.eval()

										# first forward pass
										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a       ,     use_cache=a			)
										SCREAMING_SNAKE_CASE           =					outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										SCREAMING_SNAKE_CASE           =					ids_tensor((self.batch_size, 3)       ,     config.vocab_size			)
										SCREAMING_SNAKE_CASE           =					ids_tensor((self.batch_size, 3)       ,     vocab_size=2			)

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

										SCREAMING_SNAKE_CASE           =					model(a       ,     attention_mask=a       ,     output_hidden_states=a			)
										SCREAMING_SNAKE_CASE           =					output_from_no_past["""hidden_states"""][0]
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     attention_mask=a       ,     past_key_values=a       ,     output_hidden_states=a       ,     )["""hidden_states"""][0]

										# select random slice
										SCREAMING_SNAKE_CASE           =					ids_tensor((1,)       ,     output_from_past.shape[-1]			).item()
										SCREAMING_SNAKE_CASE           =					output_from_no_past[:, -3:, random_slice_idx].detach()
										SCREAMING_SNAKE_CASE           =					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(a       ,     a       ,     atol=1E-3			)			)







							def    _UpperCAmelCase     (					self       :	Tuple			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.prepare_config_and_inputs()
										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		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     (
							    (
							        GPTNeoXModel,
							        GPTNeoXForCausalLM,
							        GPTNeoXForQuestionAnswering,
							        GPTNeoXForSequenceClassification,
							        GPTNeoXForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							a__						  =     (GPTNeoXForCausalLM,) if is_torch_available() else ()
							a__						  =     (
							    {
							        '''feature-extraction''': GPTNeoXModel,
							        '''question-answering''': GPTNeoXForQuestionAnswering,
							        '''text-classification''': GPTNeoXForSequenceClassification,
							        '''text-generation''': GPTNeoXForCausalLM,
							        '''token-classification''': GPTNeoXForTokenClassification,
							        '''zero-shot''': GPTNeoXForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							a__						  =     False
							a__						  =     False
							a__						  =     False
							a__						  =     False





							def    _UpperCAmelCase     (					self       :	Any			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					GPTNeoXModelTester(self			)
										SCREAMING_SNAKE_CASE           =					ConfigTester(self       ,     config_class=a       ,     hidden_size=64       ,     num_attention_heads=8			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										self.config_tester.run_common_tests()





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





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(a       ,     a       ,     a			)





							def    _UpperCAmelCase     (					self       :	Dict			)			->					int:
										# This regression test was failing with PyTorch < 1.3
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_decoder()

										SCREAMING_SNAKE_CASE           =					None

										self.model_tester.create_and_check_model_as_decoder(a       ,     a       ,     a			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					List[str]:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,     a       ,     a			)





							def    _UpperCAmelCase     (					self       :	str			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_causal_lm(*a			)





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





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





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





							@unittest.skip(reason="""Feed forward chunking is not implemented"""			)
							def    _UpperCAmelCase     (					self       :	List[str]			)			->					str:
										pass







							@parameterized.expand([("""linear""",), ("""dynamic""",)]			)
							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int			)			->					Dict:
										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs_for_common()
										SCREAMING_SNAKE_CASE           =					ids_tensor([1, 10]       ,     config.vocab_size			)
										SCREAMING_SNAKE_CASE           =					ids_tensor([1, int(config.max_position_embeddings * 1.5			)]       ,     config.vocab_size			)

										set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
										SCREAMING_SNAKE_CASE           =					GPTNeoXModel(a			)
										original_model.to(a			)
										original_model.eval()
										SCREAMING_SNAKE_CASE           =					original_model(a			).last_hidden_state
										SCREAMING_SNAKE_CASE           =					original_model(a			).last_hidden_state

										set_seed(42			)  # Fixed seed at init time so the two models get the same random weights
										SCREAMING_SNAKE_CASE           =					{"""type""": scaling_type, """factor""": 10.0}
										SCREAMING_SNAKE_CASE           =					GPTNeoXModel(a			)
										scaled_model.to(a			)
										scaled_model.eval()
										SCREAMING_SNAKE_CASE           =					scaled_model(a			).last_hidden_state
										SCREAMING_SNAKE_CASE           =					scaled_model(a			).last_hidden_state

										# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
										# maximum sequence length, so the outputs for the short input should match.
										if scaling_type == "dynamic":
													self.assertTrue(torch.allclose(a       ,     a       ,     atol=1E-5			)			)
										else:
													self.assertFalse(torch.allclose(a       ,     a       ,     atol=1E-5			)			)

										# The output should be different for long inputs
										self.assertFalse(torch.allclose(a       ,     a       ,     atol=1E-5			)			)





@require_torch
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""			)
										for checkpointing in [True, False]:
													SCREAMING_SNAKE_CASE           =					GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""			)

													if checkpointing:
																model.gradient_checkpointing_enable()
													else:
																model.gradient_checkpointing_disable()
													model.to(a			)

													SCREAMING_SNAKE_CASE           =					tokenizer("""My favorite food is"""       ,     return_tensors="""pt"""			).to(a			)
													# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
													# See: https://github.com/huggingface/transformers/pull/24193
													SCREAMING_SNAKE_CASE           =					"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

													SCREAMING_SNAKE_CASE           =					model.generate(**a       ,     do_sample=a       ,     max_new_tokens=20			)
													SCREAMING_SNAKE_CASE           =					tokenizer.batch_decode(a			)[0]

													self.assertEqual(a       ,     a			)







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())))






