
"""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


_SCREAMING_SNAKE_CASE													=   logging.get_logger(__name__)
def 	__UpperCamelCase (       SCREAMING_SNAKE_CASE=None				,     SCREAMING_SNAKE_CASE=None			)	->  int:







							"""simple docstring"""


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







@dataclass
class 				__magic_name__  :
					_SCREAMING_SNAKE_CASE							:       Dict  				= 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							:       Optional[Any]  				= list_field(
					    default=[8]   ,		metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'} )

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





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





					@property
					def 				lowerCAmelCase   (					self :				List[Any]     ):




												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 				lowerCAmelCase   (					self :				str     ):




												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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



