

"""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       SCREAMING_SNAKE_CASE							( lowercase__=None					,					lowercase__=None   )			->						Optional[Any]:
 return field(default_factory=lambda: default					,					metadata=lowercase__   )






@dataclass
class 			__a					:





       """simple docstring"""


       __UpperCamelCase					:      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'
               )
           }       ,	)

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

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

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





        """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."			,					snake_case			,					)






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

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






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """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









"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






