




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__    			=			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__			,	UpperCamelCase__    			=			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__    			=			rh.cluster(
										    name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
										)
					else:
										UpperCamelCase__    			=			rh.cluster(
										    name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
										)
					UpperCamelCase__    			=			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 typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, 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 			a__    (					snake_case__      ):






						def __init__(   self		,			*_A		,			_A=None		,			_A=None		,			**_A			):




											"""simple docstring"""



											super().__init__(*_A		,			**_A			)
											__lowerCAmelCase				   =       eval_examples
											__lowerCAmelCase				   =       post_process_function






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A = None		,			_A=None		,			_A = None		,			_A = "eval"		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       gen_kwargs.copy()
											__lowerCAmelCase				   =       (
											    gen_kwargs["max_length"] if gen_kwargs.get("max_length"			) is not None else self.args.generation_max_length
											)
											__lowerCAmelCase				   =       (
											    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"			) is not None else self.args.generation_num_beams
											)
											__lowerCAmelCase				   =       gen_kwargs

											__lowerCAmelCase				   =       self.eval_dataset if eval_dataset is None else eval_dataset
											__lowerCAmelCase				   =       self.get_eval_dataloader(_A			)
											__lowerCAmelCase				   =       self.eval_examples if eval_examples is None else eval_examples

											# Temporarily disable metric computation, we will do it in the loop here.
											__lowerCAmelCase				   =       self.compute_metrics
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       time.time()
											__lowerCAmelCase				   =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																__lowerCAmelCase				   =       eval_loop(
																    _A		,			description="Evaluation"		,			prediction_loss_only=True if compute_metrics is None else None		,			ignore_keys=_A		,			metric_key_prefix=_A		,			)
											finally:
																__lowerCAmelCase				   =       compute_metrics
											__lowerCAmelCase				   =       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
																__lowerCAmelCase				   =       self.post_process_function(_A		,			_A		,			_A			)
																__lowerCAmelCase				   =       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}_"""			):
																										__lowerCAmelCase				   =       metrics.pop(_A			)

																metrics.update(output.metrics			)
											else:
																__lowerCAmelCase				   =       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()			)

											__lowerCAmelCase				   =       self.callback_handler.on_evaluate(self.args		,			self.state		,			self.control		,			_A			)
											return metrics






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A=None		,			_A = "test"		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       gen_kwargs.copy()

											__lowerCAmelCase				   =       self.get_test_dataloader(_A			)

											# Temporarily disable metric computation, we will do it in the loop here.
											__lowerCAmelCase				   =       self.compute_metrics
											__lowerCAmelCase				   =       None
											__lowerCAmelCase				   =       time.time()
											__lowerCAmelCase				   =       self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
											try:
																__lowerCAmelCase				   =       eval_loop(
																    _A		,			description="Prediction"		,			prediction_loss_only=True if compute_metrics is None else None		,			ignore_keys=_A		,			metric_key_prefix=_A		,			)
											finally:
																__lowerCAmelCase				   =       compute_metrics

											__lowerCAmelCase				   =       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

											__lowerCAmelCase				   =       self.post_process_function(_A		,			_A		,			_A		,			"predict"			)
											__lowerCAmelCase				   =       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}_"""			):
																					__lowerCAmelCase				   =       metrics.pop(_A			)
											metrics.update(output.metrics			)
											return PredictionOutput(predictions=predictions.predictions		,			label_ids=predictions.label_ids		,			metrics=_A			)




