import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
						import tensorflow as tf

						from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments






@require_tf
class      __lowercase					(    unittest.TestCase    ):

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Any   )							->		List[Any]:


									'''simple docstring'''

									for model_result in results.values():
												for batch_size, sequence_length in zip(model_result['''bs''']							,							model_result['''ss''']   ):
															lowercase							=		model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__lowerCamelCase   )

						def        __a		(	self				:      Optional[Any]   )							->		Any:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							eager_mode=__lowerCamelCase							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      List[str]   )							->		int:


									'''simple docstring'''

									lowercase							=		'''sgugger/tiny-distilbert-classification'''
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							only_pretrain_model=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      Tuple   )							->		int:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		AutoConfig.from_pretrained(__lowerCamelCase   )
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							eager_mode=__lowerCamelCase							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase							,							[config]   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      List[str]   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		AutoConfig.from_pretrained(__lowerCamelCase   )
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase							,							[config]   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      Union[str, Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )

						def        __a		(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		AutoConfig.from_pretrained(__lowerCamelCase   )
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase							,							[config]   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_train_result   )
									self.check_results_dict_not_empty(results.memory_train_result   )

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		'''patrickvonplaten/t5-tiny-random'''
									lowercase							=		AutoConfig.from_pretrained(__lowerCamelCase   )
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase							,							configs=[config]   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''   )   ) == 0							,							'''Cannot do xla on CPU.'''   )
						def        __a		(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									lowercase							=		TensorFlowBenchmarkArguments(
									    models=[MODEL_ID]							,							training=__lowerCamelCase							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							use_xla=__lowerCamelCase							,							multi_process=__lowerCamelCase							,							)
									lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
									lowercase							=		benchmark.run()
									self.check_results_dict_not_empty(results.time_inference_result   )
									self.check_results_dict_not_empty(results.memory_inference_result   )

						def        __a		(	self				:      Optional[int]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''
									with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase							=		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]							,							inference=__lowerCamelCase							,							save_to_csv=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							inference_time_csv_file=os.path.join(__lowerCamelCase							,							'''inf_time.csv'''   )							,							inference_memory_csv_file=os.path.join(__lowerCamelCase							,							'''inf_mem.csv'''   )							,							env_info_csv_file=os.path.join(__lowerCamelCase							,							'''env.csv'''   )							,							multi_process=__lowerCamelCase							,							)
												lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
												benchmark.run()
												self.assertTrue(Path(os.path.join(__lowerCamelCase							,							'''inf_time.csv'''   )   ).exists()   )
												self.assertTrue(Path(os.path.join(__lowerCamelCase							,							'''inf_mem.csv'''   )   ).exists()   )
												self.assertTrue(Path(os.path.join(__lowerCamelCase							,							'''env.csv'''   )   ).exists()   )



						def        __a		(	self				:      int   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		'''sshleifer/tiny-gpt2'''

									def _check_summary_is_not_empty(__lowerCamelCase				:      int   ):
												self.assertTrue(hasattr(__lowerCamelCase							,							'''sequential'''   )   )
												self.assertTrue(hasattr(__lowerCamelCase							,							'''cumulative'''   )   )
												self.assertTrue(hasattr(__lowerCamelCase							,							'''current'''   )   )
												self.assertTrue(hasattr(__lowerCamelCase							,							'''total'''   )   )

									with tempfile.TemporaryDirectory() as tmp_dir:
												lowercase							=		TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]							,							inference=__lowerCamelCase							,							sequence_lengths=[8]							,							batch_sizes=[1]							,							log_filename=os.path.join(__lowerCamelCase							,							'''log.txt'''   )							,							log_print=__lowerCamelCase							,							trace_memory_line_by_line=__lowerCamelCase							,							eager_mode=__lowerCamelCase							,							multi_process=__lowerCamelCase							,							)
												lowercase							=		TensorFlowBenchmark(__lowerCamelCase   )
												lowercase							=		benchmark.run()
												_check_summary_is_not_empty(result.inference_summary   )
												self.assertTrue(Path(os.path.join(__lowerCamelCase							,							'''log.txt'''   )   ).exists()   )






from __future__ import annotations

import math






def        __UpperCAmelCase    (       UpperCAmelCase       )->       list[int]:



			"""simple docstring"""







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

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

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

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

						start += 1

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

			return prime


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





