o Ti @sddlZddlZddlZddlmZddlmZddlmZm Z e r'ddl Z er.ddl Z ej dede dedfd d Zd edefd d ZdS)N) Generator)Trainer)is_mlflow_availableis_wandb_availabletrainernamereturn)NNNccst}dVt}||}d|jjd||i}d|jjvr0tjdur0|jj r0t |d|jjvrJt jdurL|jj rNt j ||j jddSdSdSdS)aB A context manager function for profiling a block of code. Results are logged to Weights & Biases or MLflow depending on the trainer's configuration. Args: trainer (`~transformers.Trainer`): Trainer object. name (`str`): Name of the block to be profiled. Used as a key in the logged dictionary. Example: ```python from transformers import Trainer from trl.extras.profiling import profiling_context class MyTrainer(Trainer): def some_method(self): A = np.random.rand(1000, 1000) B = np.random.rand(1000, 1000) with profiling_context(self, "matrix_multiplication"): # Code to profile: simulate a computationally expensive operation result = A @ B # Matrix multiplication ``` Nzprofiling/Time taken: .wandbmlflow)step)time perf_counter __class____name__args report_tor run acceleratoris_main_processlogr log_metricsstate global_step)rr start_timeend_timedurationprofiling_metricsrH/home/ubuntu/.local/lib/python3.10/site-packages/trl/extras/profiling.pyprofiling_contexts  r funccstfdd}|S)aV Decorator to profile a function and log execution time using [`extras.profiling.profiling_context`]. Args: func (`callable`): Function to be profiled. Example: ```python from transformers import Trainer from trl.extras.profiling import profiling_decorator class MyTrainer(Trainer): @profiling_decorator def some_method(self): A = np.random.rand(1000, 1000) B = np.random.rand(1000, 1000) # Code to profile: simulate a computationally expensive operation result = A @ B ``` csFt|j|g|Ri|WdS1swYdS)N)r r)selfrkwargsr!rrwrapper_s$z$profiling_decorator..wrapper) functoolswraps)r!r%rr$rprofiling_decoratorGsr() contextlibr&r collections.abcr transformersrtransformers.integrationsrrr r contextmanagerstrr callabler(rrrrs  '