





import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
      # setup instructions, if using on-demand hardware

      # If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
      # If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
      # Throw an error if user passes both BYO and on-demand cluster args
      # Otherwise, use default values
      __UpperCAmelCase	:       str    	=							argparse.ArgumentParser()
      parser.add_argument("--user", type=str, default="ubuntu")
      parser.add_argument("--host", type=str, default="localhost")
      parser.add_argument("--key_path", type=str, default=None)
      parser.add_argument("--instance", type=str, default="V100:1")
      parser.add_argument("--provider", type=str, default="cheapest")
      parser.add_argument("--use_spot", type=bool, default=False)
      parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
      __UpperCAmelCase	:       List[Any]    	=							parser.parse_known_args()
      if args.host != "localhost":
            if args.instance != "V100:1" or args.provider != "cheapest":
                  raise ValueError("Cannot specify both BYO and on-demand cluster args")
            __UpperCAmelCase	:       Optional[int]    	=							rh.cluster(
                name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
            )
      else:
            __UpperCAmelCase	:       Optional[int]    	=							rh.cluster(
                name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
            )
      __UpperCAmelCase	:       int    	=							args.example.rsplit("/", 1)[0]

      # Set up remote environment
      cluster.install_packages(["pip:./"])  # Installs transformers from local source
      # Note transformers is copied into the home directory on the remote machine, so we can install from there
      cluster.run([f'pip install -r transformers/examples/{example_dir}/requirements.txt'])
      cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

      # Run example. You can bypass the CLI wrapper and paste your own code here.
      cluster.run([f'python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'])

      # Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
      # from my_script... import train
      # reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
      # launch_train_gpu = rh.function(fn=train,
      #                                system=gpu,
      #                                reqs=reqs,
      #                                name='train_bert_glue')
      #
      # We can pass in arguments just like we would to a function:
      # launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
      #                  stream_logs=True)













import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   str							,    *A			:   Dict							,    A			:   Optional[int]=None							,    A			:   Tuple=None							,    **A			:   Optional[int]		):
         super().__init__(*A							,    **A		)
         __snake_case:						List[Any]			  =						eval_examples
         __snake_case:						str			  =						post_process_function




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Dict=None							,    A			:   int=None							,    A			:   List[Any]=None							,    A			:   str = "eval"		):
         __snake_case:						int			  =						self.eval_dataset if eval_dataset is None else eval_dataset
         __snake_case:						Any			  =						self.get_eval_dataloader(A		)
         __snake_case:						Optional[Any]			  =						self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Union[str, Any]			  =						self.compute_metrics
         __snake_case:						List[str]			  =						None
         __snake_case:						Tuple			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Tuple			  =						time.time()
         try:
           __snake_case:						Any			  =						eval_loop(
               A							,    description="""Evaluation"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						Optional[int]			  =						compute_metrics
         __snake_case:						Union[str, Any]			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)
         if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
           # Only the main node write the results by default
           __snake_case:						List[str]			  =						self.post_process_function(A							,    A							,    output.predictions		)
           __snake_case:						List[Any]			  =						self.compute_metrics(A		)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()		):
             if not key.startswith(f'''{metric_key_prefix}_'''		):
               __snake_case:						str			  =						metrics.pop(A		)
           metrics.update(output.metrics		)
         else:
           __snake_case:						List[Any]			  =						output.metrics

         if self.args.should_log:
           # Only the main node log the results by default
           self.log(A		)

         if self.args.tpu_metrics_debug or self.args.debug:
           # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
           xm.master_print(met.metrics_report()		)

         __snake_case:						str			  =						self.callback_handler.on_evaluate(self.args							,    self.state							,    self.control							,    A		)
         return metrics






       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   List[Any]							,    A			:   List[str]							,    A			:   str=None							,    A			:   str = "test"		):
         __snake_case:						Optional[Any]			  =						self.get_test_dataloader(A		)

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Optional[int]			  =						self.compute_metrics
         __snake_case:						List[Any]			  =						None
         __snake_case:						str			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Dict			  =						time.time()
         try:
           __snake_case:						str			  =						eval_loop(
               A							,    description="""Prediction"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						List[Any]			  =						compute_metrics
         __snake_case:						Dict			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)

         if self.post_process_function is None or self.compute_metrics is None:
           return output

         __snake_case:						Union[str, Any]			  =						self.post_process_function(A							,    A							,    output.predictions							,    """predict"""		)
         __snake_case:						str			  =						self.compute_metrics(A		)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()		):
           if not key.startswith(f'''{metric_key_prefix}_'''		):
             __snake_case:						List[str]			  =						metrics.pop(A		)
         metrics.update(output.metrics		)
         return PredictionOutput(predictions=predictions.predictions							,    label_ids=predictions.label_ids							,    metrics=A		)






