U 9%eK@svddlZddlZddlZddlmZmZddlmZddlmZddlm Z e e Z d ddZ eGd d d ZdS) N) dataclassfield)time)List)loggingcstfdd|dS)NcsS)Nrdefaultrj/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/benchmark/benchmark_args_utils.pyzlist_field..)default_factorymetadata)rr rrr r list_fieldsrc@seZdZUdZegddidZeeed<edgddidZ ee ed<edd d d gdd idZ ee ed <e dddidZ eed<e dddidZeed<e dddidZeed<e dddidZeed<e dddidZeed<e dddidZeed<e dddidZeed<e dddidZeed<e ddd idZeed!<e ddd"idZeed#<e ddd$idZeed%<e ddd&idZeed'<e ddd(idZeed)<e d*eed+dd,idZeed-<e d.eed+dd/idZeed0<e d1eed+dd2idZeed3<e d4eed+dd5idZ eed6<e d7eed+dd8idZ!eed9<e d:eed+dd;idZ"eed<<e d=dd>idZ#e ed?<e ddd@idZ$eedA<dBdCZ%dDdEZ&e'eedFdGdHZ(e'dIdJZ)dKS)LBenchmarkArgumentsz BenchMarkArguments are arguments we use in our benchmark scripts **which relate to the training loop itself**. Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command line. helpz|Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version of all available modelsrmodelszKList of batch sizes for which memory and time performance will be evaluated batch_sizes izPList of sequence lengths for which memory and time performance will be evaluatedsequence_lengthsTzVWhether to benchmark inference of model. Inference can be disabled via --no-inference. inferencezMWhether to run on available cuda devices. Cuda can be disabled via --no-cuda.cudazJWhether to run on available tpu devices. TPU can be disabled via --no-tpu.tpuFz!Use FP16 to accelerate inference.fp16zBenchmark training of modeltrainingzVerbose memory tracingverbosezYWhether to perform speed measurements. Speed measurements can be disabled via --no-speed.speedz[Whether to perform memory measurements. Memory measurements can be disabled via --no-memorymemoryzTrace memory line by linetrace_memory_line_by_linezSave result to a CSV file save_to_csvz'Save all print statements in a log file log_printz(Whether to print environment information env_printzWhether 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. multi_processZinference_time_z.csvz0CSV filename used if saving time results to csv.inference_time_csv_fileZinference_memory_z2CSV filename used if saving memory results to csv.inference_memory_csv_fileZ train_time_z=CSV filename used if saving time results to csv for training.train_time_csv_fileZ train_memory_z?CSV filename used if saving memory results to csv for training.train_memory_csv_fileZ env_info_z4CSV filename used if saving environment information.env_info_csv_fileZlog_z7Log filename used if print statements are saved in log. log_filenamez Times an experiment will be run.repeatzrInstead of loading the model as defined in `config.architectures` if exists, just load the pretrain model weights.only_pretrain_modelcCstd|jdtdS)Nz The class z is deprecated. Hugging Face Benchmarking utils are deprecated in general and it is advised to use external Benchmarking libraries to benchmark Transformer models.)warningswarn __class__ FutureWarningselfrrr __post_init__s z BenchmarkArguments.__post_init__cCstjt|ddS)z< Serializes this instance to a JSON string. r)indent)jsondumps dataclassesasdictr4rrr to_json_stringsz!BenchmarkArguments.to_json_string)returncCst|jdkrtd|jS)NrzPlease make sure you provide at least one model name / model identifier, *e.g.* `--models bert-base-cased` or `args.models = ['bert-base-cased'].)lenr ValueErrorr4rrr model_namess zBenchmarkArguments.model_namescCs&|js dS|jrtddSdSdS)NFz1Multiprocessing is currently not possible on TPU.T)r&Zis_tpuloggerinfor4rrr do_multi_processings  z&BenchmarkArguments.do_multi_processingN)*__name__ __module__ __qualname____doc__rrrstr__annotations__rintrrrboolrrrrrr r!r"r#r$r%r&roundrr'r(r)r*r+r,r.r/r6r<propertyr@rCrrrr r"s     r)NN)r:r8r0rrrtypingrutilsrZ get_loggerrDrArrrrrr s