



'''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


_UpperCamelCase											=		logging.get_logger(__name__)

def 			a_	(				_lowerCAmelCase=None			,_lowerCAmelCase=None			) -> Dict:
  return field(default_factory=lambda: default			,metadata=_lowerCAmelCase			)






@dataclass
class    lowerCamelCase_       :
  """simple docstring"""




  a_				   =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"""
          )
      }  ,  )

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

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

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




  def 		_lowercase	(					self :      str  )   ->   int:
    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.'     ,      _a     ,      )




  def 		_lowercase	(					self :      List[str]  )   ->   List[str]:
    return json.dumps(dataclasses.asdict(self  )     ,      indent=2  )




  @property
  def 		_lowercase	(					self :      str  )   ->   List[str]:
    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 		_lowercase	(					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











'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






