




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						:Dict		,      __magic_name__						:Dict				):







								'''simple docstring'''







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







								'''simple docstring'''







								a										=							"""sshleifer/tiny-gpt2"""
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__				)
								a										=							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'''







								a										=							"""sgugger/tiny-distilbert-classification"""
								a										=							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__		,      )
								a										=							PyTorchBenchmark(__magic_name__				)
								a										=							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'''







								a										=							"""sshleifer/tiny-gpt2"""
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      torchscript=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__				)
								a										=							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						:str				):







								'''simple docstring'''







								a										=							"""sshleifer/tiny-gpt2"""
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      fpaa=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__				)
								a										=							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				):







								'''simple docstring'''







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







								'''simple docstring'''







								a										=							"""sshleifer/tiny-gpt2"""
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__				)
								a										=							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				):







								'''simple docstring'''







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







								'''simple docstring'''







								a										=							"""sshleifer/tiny-gpt2"""
								a										=							AutoConfig.from_pretrained(__magic_name__				)
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__		,      configs=[config]				)
								a										=							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]				):







								'''simple docstring'''







								a										=							"""sshleifer/tinier_bart"""
								a										=							AutoConfig.from_pretrained(__magic_name__				)
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__		,      configs=[config]				)
								a										=							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				):







								'''simple docstring'''







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







								'''simple docstring'''







								a										=							"""sshleifer/tinier_bart"""
								a										=							AutoConfig.from_pretrained(__magic_name__				)
								a										=							PyTorchBenchmarkArguments(
								    models=[MODEL_ID]		,      training=__magic_name__		,      inference=__magic_name__		,      sequence_lengths=[8]		,      batch_sizes=[1]		,      multi_process=__magic_name__		,      )
								a										=							PyTorchBenchmark(__magic_name__		,      configs=[config]				)
								a										=							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'''







								a										=							"""sshleifer/tiny-gpt2"""
								with tempfile.TemporaryDirectory() as tmp_dir:
									a										=							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__		,      )
									a										=							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						:Tuple				):







								'''simple docstring'''







								a										=							"""sshleifer/tiny-gpt2"""

								def _check_summary_is_not_empty(__magic_name__						:Tuple				):
									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:
									a										=							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__		,      )
									a										=							PyTorchBenchmark(__magic_name__				)
									a										=							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()				)







def 		__A		(	__lowerCamelCase    )      ->       int:
	a										=							0
	while num > 0:
		digit_sum += num % 10
		num //= 10
	return digit_sum





def 		__A		(	__lowerCamelCase = 100    )      ->       int:
	a										=							1
	a										=							2

	for i in range(2							,			max_n + 1    ):
		a										=							pre_numerator
		a										=							2 * i // 3 if i % 3 == 0 else 1
		a										=							cur_numerator
		a										=							e_cont * pre_numerator + temp

	return sum_digits(__lowerCamelCase    )


if __name__ == "__main__":
		print(F'{solution() = }')

