

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

from ..utils import logging


__lowercase				      =						logging.get_logger(__name__)







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None   ):
      '''simple docstring'''







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





@dataclass
class    _lowercase				:
   _lowercase     :       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'
           )
       },)

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

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

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

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         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     UpperCamelCase					(				self	:    Tuple       ) ->			Union[str, Any]:
         """simple docstring"""





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

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         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     UpperCamelCase					(				self	:    List[Any]       ) ->			List[Any]:
         """simple docstring"""





         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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







      return sum(2 * a * ((a - 1) // 2) for a in range(3		,				n + 1   )   )


if __name__ == "__main__":
      print(solution())





