






"""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


_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A=None  ,				_A=None )     ->       Dict:
				return field(default_factory=lambda: default  ,				metadata=lowerCAmelCase__ )


@dataclass
class      UpperCamelCase		:
						_SCREAMING_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"""
						        )
						    }						,						)

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

						_SCREAMING_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"""}						,						)

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






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






						@property
						def      __snake_case      (		self  :Union[str, Any]				)      ->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      __snake_case      (		self  :Dict				)      ->Optional[int]:



										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"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



