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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					Dict      )					->				Any:



												'''simple docstring'''

												for model_result in results.values():
																	for batch_size, sequence_length in zip(model_result["bs"]	,							model_result["ss"]      ):
																						SCREAMING_SNAKE_CASE    			=       model_result["result"][batch_size][sequence_length]
																						self.assertIsNotNone(_UpperCamelCase      )




							def 	__snake_case(						self   :					Any      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sgugger/tiny-distilbert-classification"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							only_pretrain_model=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							torchscript=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					str      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							fpaa=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

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




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Optional[int]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							fpaa=_UpperCamelCase	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )




							def 	__snake_case(						self   :					str      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       AutoConfig.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase	,							configs=[config]      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )




							def 	__snake_case(						self   :					Any      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tinier_bart"
												SCREAMING_SNAKE_CASE    			=       AutoConfig.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase	,							configs=[config]      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result      )
												self.check_results_dict_not_empty(results.memory_inference_result      )




							def 	__snake_case(						self   :					str      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"
												SCREAMING_SNAKE_CASE    			=       AutoConfig.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase	,							configs=[config]      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tinier_bart"
												SCREAMING_SNAKE_CASE    			=       AutoConfig.from_pretrained(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmarkArguments(
												    models=[MODEL_ID]	,							training=_UpperCamelCase	,							inference=_UpperCamelCase	,							sequence_lengths=[8]	,							batch_sizes=[1]	,							multi_process=_UpperCamelCase	,							)
												SCREAMING_SNAKE_CASE    			=       PyTorchBenchmark(_UpperCamelCase	,							configs=[config]      )
												SCREAMING_SNAKE_CASE    			=       benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result      )
												self.check_results_dict_not_empty(results.memory_train_result      )




							def 	__snake_case(						self   :					Any      )					->				List[str]:



												'''simple docstring'''

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





							def 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       "sshleifer/tiny-gpt2"

												def _check_summary_is_not_empty(_UpperCamelCase   :					Optional[int]      ):
																	self.assertTrue(hasattr(_UpperCamelCase	,							"sequential"      )      )
																	self.assertTrue(hasattr(_UpperCamelCase	,							"cumulative"      )      )
																	self.assertTrue(hasattr(_UpperCamelCase	,							"current"      )      )
																	self.assertTrue(hasattr(_UpperCamelCase	,							"total"      )      )

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  ), F"The input value of [n={number}] is not an integer"

					if number == 1:
										return 2
					elif number < 1:
										SCREAMING_SNAKE_CASE    			=       F"The input value of [n={number}] has to be > 0"
										raise ValueError(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       sylvester(number - 1  )
										SCREAMING_SNAKE_CASE    			=       num - 1
										SCREAMING_SNAKE_CASE    			=       num
										return lower * upper + 1


if __name__ == "__main__":
							print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")




