






import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__UpperCamelCase					:		Optional[int]	  =						logging.get_logger(__name__)
def      a_ (      _A=None      ,   _A=None  )			->			List[str]:







				"""simple docstring"""
				return field(default_factory=lambda: default      ,   metadata=lowerCamelCase_  )


@dataclass
class   __SCREAMING_SNAKE_CASE:
							_UpperCAmelCase     =       list_field(
							    default=[]    ,  metadata={
							        "help": (
							            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
							            " of all available models"
							        )
							    }    ,  )

							_UpperCAmelCase     =       list_field(
							    default=[8]    ,  metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}	)

							_UpperCAmelCase     =       list_field(
							    default=[8, 3_2, 1_2_8, 5_1_2]    ,  metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}    ,  )

							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}    ,  )
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}    ,  )
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Use FP16 to accelerate inference."}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Benchmark training of model"}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Verbose memory tracing"}	)
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}    ,  )
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={
							        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
							    }    ,  )
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Trace memory line by line"}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Save result to a CSV file"}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Save all print statements in a log file"}	)
							_UpperCAmelCase     =       field(default=lowercase_    ,  metadata={"help": "Whether to print environment information"}	)
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={
							        "help": (
							            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
							            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
							            " for debugging / testing and on TPU."
							        )
							    }    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''inference_time_{round(time()	)}.csv'''    ,  metadata={"help": "CSV filename used if saving time results to csv."}    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''inference_memory_{round(time()	)}.csv'''    ,  metadata={"help": "CSV filename used if saving memory results to csv."}    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''train_time_{round(time()	)}.csv'''    ,  metadata={"help": "CSV filename used if saving time results to csv for training."}    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''train_memory_{round(time()	)}.csv'''    ,  metadata={"help": "CSV filename used if saving memory results to csv for training."}    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''env_info_{round(time()	)}.csv'''    ,  metadata={"help": "CSV filename used if saving environment information."}    ,  )
							_UpperCAmelCase     =       field(
							    default=F'''log_{round(time()	)}.csv'''    ,  metadata={"help": "Log filename used if print statements are saved in log."}    ,  )
							_UpperCAmelCase     =       field(default=3    ,  metadata={"help": "Times an experiment will be run."}	)
							_UpperCAmelCase     =       field(
							    default=lowercase_    ,  metadata={
							        "help": (
							            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
							            " model weights."
							        )
							    }    ,  )
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Dict:
											warnings.warn(
											    F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
											    ' are deprecated in general and it is advised to use external Benchmarking libraries '
											    ' to benchmark Transformer models.'       ,	lowerCamelCase_       ,	)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Dict:
											return json.dumps(dataclasses.asdict(self						)       ,	indent=2						)
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:



											if len(self.models						) <= 0:
															raise ValueError(
															    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
															    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'						)
											return self.models


							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:



											if not self.multi_process:
															return False
											elif self.is_tpu:
															logger.info('Multiprocessing is currently not possible on TPU.'						)
															return False
											else:
															return True












def      a_ (      _A = 4000000  )			->			int:







				"""simple docstring"""
				snake_case__									=     [0, 1]
				snake_case__									=     0
				while fib[i] <= n:
								fib.append(fib[i] + fib[i + 1]  )
								if fib[i + 2] > n:
												break
								i += 1
				snake_case__									=     0
				for j in range(len(_A  ) - 1  ):
								if fib[j] % 2 == 0:
												total += fib[j]

				return total


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




