

"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 		_A       (			unittest.TestCase					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         for model_result in results.values():
            for batch_size, sequence_length in zip(model_result["bs"]    ,						model_result["ss"]		):
               lowerCamelCase__						=			model_result["result"][batch_size][sequence_length]
               self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			"sgugger/tiny-distilbert-classification"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						only_pretrain_model=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						torchscript=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      @unittest.skipIf(torch_device == "cpu"    ,						"Cant do half precision"		)
      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						fpaa=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__		)
         # set architectures equal to `None`
         lowerCamelCase__						=			None
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__    ,						configs=[config]		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)





      @unittest.skipIf(torch_device == "cpu"    ,						"Can't do half precision"		)
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						fpaa=SCREAMING_SNAKE_CASE__    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__    ,						configs=[config]		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			"sshleifer/tinier_bart"
         lowerCamelCase__						=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__    ,						configs=[config]		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_inference_result		)
         self.check_results_dict_not_empty(results.memory_inference_result		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         lowerCamelCase__						=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__    ,						configs=[config]		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			"sshleifer/tinier_bart"
         lowerCamelCase__						=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			PyTorchBenchmarkArguments(
             models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__    ,						configs=[config]		)
         lowerCamelCase__						=			benchmark.run()
         self.check_results_dict_not_empty(results.time_train_result		)
         self.check_results_dict_not_empty(results.memory_train_result		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"
         with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCamelCase__						=			PyTorchBenchmarkArguments(
                models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						save_to_csv=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__    ,						"inf_time.csv"		)    ,						train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__    ,						"train_mem.csv"		)    ,						inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__    ,						"inf_mem.csv"		)    ,						train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__    ,						"train_time.csv"		)    ,						env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__    ,						"env.csv"		)    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
            lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"inf_time.csv"		)		).exists()		)
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"train_time.csv"		)		).exists()		)
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"inf_mem.csv"		)		).exists()		)
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"train_mem.csv"		)		).exists()		)
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"env.csv"		)		).exists()		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			"sshleifer/tiny-gpt2"

         def _check_summary_is_not_empty(SCREAMING_SNAKE_CASE__		):
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,						"sequential"		)		)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,						"cumulative"		)		)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,						"current"		)		)
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__    ,						"total"		)		)

         with tempfile.TemporaryDirectory() as tmp_dir:
            lowerCamelCase__						=			PyTorchBenchmarkArguments(
                models=[MODEL_ID]    ,						training=SCREAMING_SNAKE_CASE__    ,						inference=SCREAMING_SNAKE_CASE__    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						log_filename=os.path.join(SCREAMING_SNAKE_CASE__    ,						"log.txt"		)    ,						log_print=SCREAMING_SNAKE_CASE__    ,						trace_memory_line_by_line=SCREAMING_SNAKE_CASE__    ,						multi_process=SCREAMING_SNAKE_CASE__    ,						)
            lowerCamelCase__						=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__    ,						"log.txt"		)		).exists()		)







"""simple docstring"""




def 	UpperCAmelCase__   (				A__   )      ->      list[int]:




   """simple docstring"""

   if num <= 0:
      raise ValueError("Input must be a positive integer"   )

   lowerCamelCase__						=			[True] * (num + 1)

   lowerCamelCase__						=			2
   while p * p <= num:
      if primes[p]:
         for i in range(p * p	,    num + 1	,    A__   ):
            lowerCamelCase__						=			False
      p += 1

   return [prime for prime in range(2	,    num + 1   ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a positive integer: ''').strip())
     print(prime_sieve_eratosthenes(user_num))




