'''simple docstring'''




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

from ..utils import logging


_a   :	List[Any] 							=   logging.get_logger(__name__)

def _lowercase	(      lowerCamelCase__=None							,    lowerCamelCase__=None						)					->       int:





       """simple docstring"""


       return field(default_factory=lambda: default							,    metadata=lowerCamelCase__						)






@dataclass
class 		__A       :
       snake_case       :List[str]				    =					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"
               )
           } ,					)

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

       snake_case       :List[int]				    =					list_field(
           default=[8, 32, 128, 512] ,					metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} ,					)

       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Use FP16 to accelerate inference."}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Benchmark training of model"}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Verbose memory tracing"}   )
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} ,					)
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={
               "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
           } ,					)
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Trace memory line by line"}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Save result to a CSV file"}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Save all print statements in a log file"}   )
       snake_case       :bool				    =					field(default=__magic_name__ ,					metadata={"help": "Whether to print environment information"}   )
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					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."
               )
           } ,					)
       snake_case       :str				    =					field(
           default=f"inference_time_{round(time()   )}.csv" ,					metadata={"help": "CSV filename used if saving time results to csv."} ,					)
       snake_case       :str				    =					field(
           default=f"inference_memory_{round(time()   )}.csv" ,					metadata={"help": "CSV filename used if saving memory results to csv."} ,					)
       snake_case       :str				    =					field(
           default=f"train_time_{round(time()   )}.csv" ,					metadata={"help": "CSV filename used if saving time results to csv for training."} ,					)
       snake_case       :str				    =					field(
           default=f"train_memory_{round(time()   )}.csv" ,					metadata={"help": "CSV filename used if saving memory results to csv for training."} ,					)
       snake_case       :str				    =					field(
           default=f"env_info_{round(time()   )}.csv" ,					metadata={"help": "CSV filename used if saving environment information."} ,					)
       snake_case       :str				    =					field(
           default=f"log_{round(time()   )}.csv" ,					metadata={"help": "Log filename used if print statements are saved in log."} ,					)
       snake_case       :int				    =					field(default=3 ,					metadata={"help": "Times an experiment will be run."}   )
       snake_case       :bool				    =					field(
           default=__magic_name__ ,					metadata={
               "help": (
                   "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                   " model weights."
               )
           } ,					)






       def 			_snake_case						(      self      ):
              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."				,     UpperCamelCase_				,     )






       def 			_snake_case						(      self      ):
              return json.dumps(dataclasses.asdict(self      )				,     indent=2      )






       @property
       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self      ):
              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







'''simple docstring'''




def _lowercase	(      lowerCamelCase__ = 100						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		(n * (n + 1) // 2) ** 2
       __UpperCAmelCase  :				Any       				=		n * (n + 1) * (2 * n + 1) // 6
       return sum_cubes - sum_squares


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






