




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_A     =			logging.get_logger(__name__)


@dataclass
class  A		:
		__snake_case									=   field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()     )}     )
		__snake_case									=   field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}     )
		__snake_case									=   field(
		    default=128      ,				metadata={
		        'help': (
		            'The maximum total input sequence length after tokenization. Sequences longer '
		            'than this will be truncated, sequences shorter will be padded.'
		        )
		    }      ,				)
		__snake_case									=   field(
		    default=__UpperCamelCase      ,				metadata={'help': 'Overwrite the cached training and evaluation sets'}     )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.task_name.lower()







class  A		(  __UpperCamelCase     ):
		__snake_case									=   'train'
		__snake_case									=   'dev'
		__snake_case									=   'test'







class  A		(  __UpperCamelCase     ):
		__snake_case									=   42
		__snake_case									=   42
		__snake_case									=   42



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = Split.train,	UpperCamelCase__ = None,	):







								"""simple docstring"""



								warnings.warn(
								    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
								    '''library. You can have a look at this example script for pointers: '''
								    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''',	a_,	)
								lowerCAmelCase_      			=  args
								lowerCAmelCase_      			=  glue_processors[args.task_name]()
								lowerCAmelCase_      			=  glue_output_modes[args.task_name]
								if isinstance(a_,	a_   ):
														try:
																				lowerCAmelCase_      			=  Split[mode]
														except KeyError:
																				raise KeyError('''mode is not a valid split name'''   )
        # Load data features from cache or dataset file
								lowerCAmelCase_      			=  os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir,	f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",	)
								lowerCAmelCase_      			=  self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														lowerCAmelCase_      			=  label_list[2], label_list[1]
								lowerCAmelCase_      			=  label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								lowerCAmelCase_      			=  cached_features_file + '''.lock'''
								with FileLock(a_   ):
														if os.path.exists(a_   ) and not args.overwrite_cache:
																				lowerCAmelCase_      			=  time.time()
																				lowerCAmelCase_      			=  torch.load(a_   )
																				logger.info(
																				    f"Loading features from cached file {cached_features_file} [took %.3f s]",	time.time() - start   )
														else:
																				logger.info(f"Creating features from dataset file at {args.data_dir}"   )

																				if mode == Split.dev:
																										lowerCAmelCase_      			=  self.processor.get_dev_examples(args.data_dir   )
																				elif mode == Split.test:
																										lowerCAmelCase_      			=  self.processor.get_test_examples(args.data_dir   )
																				else:
																										lowerCAmelCase_      			=  self.processor.get_train_examples(args.data_dir   )
																				if limit_length is not None:
																										lowerCAmelCase_      			=  examples[:limit_length]
																				lowerCAmelCase_      			=  glue_convert_examples_to_features(
																				    a_,	a_,	max_length=args.max_seq_length,	label_list=a_,	output_mode=self.output_mode,	)
																				lowerCAmelCase_      			=  time.time()
																				torch.save(self.features,	a_   )
																				# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																				logger.info(
																				    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"   )



		def __len__(     self   ):







								"""simple docstring"""



								return len(self.features   )



		def __getitem__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return self.features[i]







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self.label_list









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
				__A   			=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")
				__A			,			__A   			=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")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=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)





