
"""simple docstring"""




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 			snake_case  :

							def __init__(self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=13							,							SCREAMING_SNAKE_CASE_=7							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=True							,							SCREAMING_SNAKE_CASE_=99							,							SCREAMING_SNAKE_CASE_=64							,							SCREAMING_SNAKE_CASE_=5							,							SCREAMING_SNAKE_CASE_=4							,							SCREAMING_SNAKE_CASE_=37							,							SCREAMING_SNAKE_CASE_="gelu"							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=0.1							,							SCREAMING_SNAKE_CASE_=5_12							,							SCREAMING_SNAKE_CASE_=16							,							SCREAMING_SNAKE_CASE_=2							,							SCREAMING_SNAKE_CASE_=0.02							,							SCREAMING_SNAKE_CASE_=3							,							SCREAMING_SNAKE_CASE_=4							,							SCREAMING_SNAKE_CASE_=None							,							):

									"""simple docstring"""







									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 _lowercase       (self     ):

									"""simple docstring"""







									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 _lowercase       (self     ):

									"""simple docstring"""







									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=SCREAMING_SNAKE_CASE_							,							initializer_range=self.initializer_range							,							pad_token_id=self.pad_token_id							,							)

							def _lowercase       (self     ):

									"""simple docstring"""







									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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			GPTNeoXModel(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			True
									SCREAMING_SNAKE_CASE_    			=			GPTNeoXModel(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			GPTNeoXForQuestionAnswering(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_     )
									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 _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			GPTNeoXForSequenceClassification(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			ids_tensor([self.batch_size]							,							self.type_sequence_label_size     )
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.num_labels
									SCREAMING_SNAKE_CASE_    			=			GPTNeoXForTokenClassification(SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							labels=SCREAMING_SNAKE_CASE_     )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)     )

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			True
									SCREAMING_SNAKE_CASE_    			=			GPTNeoXForCausalLM(config=SCREAMING_SNAKE_CASE_     )
									model.to(SCREAMING_SNAKE_CASE_     )
									model.eval()

									# first forward pass
									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							use_cache=SCREAMING_SNAKE_CASE_     )
									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(SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			output_from_no_past['''hidden_states'''][0]
									SCREAMING_SNAKE_CASE_    			=			model(
									    SCREAMING_SNAKE_CASE_							,							attention_mask=SCREAMING_SNAKE_CASE_							,							past_key_values=SCREAMING_SNAKE_CASE_							,							output_hidden_states=SCREAMING_SNAKE_CASE_							,							)['''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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							atol=1e-3     )     )







							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			GPTNeoXModelTester(self     )
									SCREAMING_SNAKE_CASE_    			=			ConfigTester(self							,							config_class=SCREAMING_SNAKE_CASE_							,							hidden_size=64							,							num_attention_heads=8     )

							def _lowercase       (self     ):

									"""simple docstring"""







									self.config_tester.run_common_tests()

							def _lowercase       (self     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_     )

							@unittest.skip(reason='''Feed forward chunking is not implemented'''     )
							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							@parameterized.expand([('''linear''',), ('''dynamic''',)]     )
							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_     )
									original_model.to(SCREAMING_SNAKE_CASE_     )
									original_model.eval()
									SCREAMING_SNAKE_CASE_    			=			original_model(SCREAMING_SNAKE_CASE_     ).last_hidden_state
									SCREAMING_SNAKE_CASE_    			=			original_model(SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_     )
									scaled_model.to(SCREAMING_SNAKE_CASE_     )
									scaled_model.eval()
									SCREAMING_SNAKE_CASE_    			=			scaled_model(SCREAMING_SNAKE_CASE_     ).last_hidden_state
									SCREAMING_SNAKE_CASE_    			=			scaled_model(SCREAMING_SNAKE_CASE_     ).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(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							atol=1e-5     )     )
									else:
											self.assertFalse(torch.allclose(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							atol=1e-5     )     )

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







@require_torch
class 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									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(SCREAMING_SNAKE_CASE_     )

											SCREAMING_SNAKE_CASE_    			=			tokenizer('''My favorite food is'''							,							return_tensors='''pt'''     ).to(SCREAMING_SNAKE_CASE_     )
											# 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(**SCREAMING_SNAKE_CASE_							,							do_sample=SCREAMING_SNAKE_CASE_							,							max_new_tokens=20     )
											SCREAMING_SNAKE_CASE_    			=			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     )[0]

											self.assertEqual(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

"""simple docstring"""




from __future__ import annotations

import math



def 							_lowerCamelCase			(			__a     ):

		if num <= 0:
				SCREAMING_SNAKE_CASE_    			=			F'{num}: Invalid input, please enter a positive integer.'
				raise ValueError(__a     )

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

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

						# Set multiples of start be False
						for i in range(start * start,				num + 1,				__a     ):
								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(__a     )

		return prime


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