





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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							for model_result in results.values():
											for batch_size, sequence_length in zip(model_result['''bs'''] ,							model_result['''ss''']   ):
															snake_case_						:   Union[str, Any]           =  model_result['''result'''][batch_size][sequence_length]
															self.assertIsNotNone(__magic_name__   )






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Optional[int]           =  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[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''sgugger/tiny-distilbert-classification'''
							snake_case_						:   List[Any]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							only_pretrain_model=__magic_name__ ,							)
							snake_case_						:   Optional[Any]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   List[Any]           =  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]:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   List[str]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							torchscript=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Dict           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Dict           =  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   )					->       int:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							fpaa=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[str]           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   List[Any]           =  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[str]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   int           =  AutoConfig.from_pretrained(__magic_name__   )
							# set architectures equal to `None`
							snake_case_						:   List[str]           =  None
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[str]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[Any]           =  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[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   int           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Optional[Any]           =  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   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Union[str, Any]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							fpaa=__magic_name__ ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Dict           =  PyTorchBenchmark(__magic_name__   )
							snake_case_						:   Union[str, Any]           =  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   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   Dict           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[int]           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   List[Any]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[Any]           =  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   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''sshleifer/tinier_bart'''
							snake_case_						:   Tuple           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   Any           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[int]           =  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]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  '''sshleifer/tiny-gpt2'''
							snake_case_						:   str           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   int           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   str           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[Any]           =  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[Any]:


							'''simple docstring'''



							snake_case_						:   int           =  '''sshleifer/tinier_bart'''
							snake_case_						:   Optional[int]           =  AutoConfig.from_pretrained(__magic_name__   )
							snake_case_						:   str           =  PyTorchBenchmarkArguments(
							    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							multi_process=__magic_name__ ,							)
							snake_case_						:   Optional[Any]           =  PyTorchBenchmark(__magic_name__ ,							configs=[config]   )
							snake_case_						:   Optional[int]           =  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   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Dict           =  '''sshleifer/tiny-gpt2'''
							with tempfile.TemporaryDirectory() as tmp_dir:
											snake_case_						:   Any           =  PyTorchBenchmarkArguments(
											    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							save_to_csv=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							inference_time_csv_file=os.path.join(__magic_name__ ,							'''inf_time.csv'''   ) ,							train_memory_csv_file=os.path.join(__magic_name__ ,							'''train_mem.csv'''   ) ,							inference_memory_csv_file=os.path.join(__magic_name__ ,							'''inf_mem.csv'''   ) ,							train_time_csv_file=os.path.join(__magic_name__ ,							'''train_time.csv'''   ) ,							env_info_csv_file=os.path.join(__magic_name__ ,							'''env.csv'''   ) ,							multi_process=__magic_name__ ,							)
											snake_case_						:   Union[str, Any]           =  PyTorchBenchmark(__magic_name__   )
											benchmark.run()
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''inf_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''train_time.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''inf_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''train_mem.csv'''   )   ).exists()   )
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''env.csv'''   )   ).exists()   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  '''sshleifer/tiny-gpt2'''

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

							with tempfile.TemporaryDirectory() as tmp_dir:
											snake_case_						:   Dict           =  PyTorchBenchmarkArguments(
											    models=[MODEL_ID] ,							training=__magic_name__ ,							inference=__magic_name__ ,							sequence_lengths=[8] ,							batch_sizes=[1] ,							log_filename=os.path.join(__magic_name__ ,							'''log.txt'''   ) ,							log_print=__magic_name__ ,							trace_memory_line_by_line=__magic_name__ ,							multi_process=__magic_name__ ,							)
											snake_case_						:   int           =  PyTorchBenchmark(__magic_name__   )
											snake_case_						:   Union[str, Any]           =  benchmark.run()
											_check_summary_is_not_empty(result.inference_summary   )
											_check_summary_is_not_empty(result.train_summary   )
											self.assertTrue(Path(os.path.join(__magic_name__ ,							'''log.txt'''   )   ).exists()   )







import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=1_000			)		->						str:






				"""simple docstring"""


				if n < 2:
								return False

				if n % 2 == 0:
								return n == 2

				# this means n is odd
				snake_case_						:   int           =  n - 1
				snake_case_						:   Union[str, Any]           =  0
				while d % 2 == 0:
								d /= 2
								exp += 1

				# n - 1=d*(2**exp)
				snake_case_						:   List[Any]           =  0
				while count < prec:
								snake_case_						:   Tuple           =  random.randint(2			,    n - 1			)
								snake_case_						:   List[Any]           =  bin_exp_mod(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								if b != 1:
												snake_case_						:   Any           =  True
												for _ in range(_UpperCamelCase			):
																if b == n - 1:
																				snake_case_						:   Tuple           =  False
																				break
																snake_case_						:   List[Any]           =  b * b
																b %= n
												if flag:
																return False
												count += 1
				return True


if __name__ == "__main__":
	lowerCAmelCase_    =    abs(int(input('''Enter bound : ''').strip()))
	print('''Here\'s the list of primes:''')
	print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))
