
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)




import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


__A							={
    '''cola''': 2,
    '''mnli''': 3,
    '''mrpc''': 2,
    '''sst-2''': 2,
    '''sts-b''': 1,
    '''qqp''': 2,
    '''qnli''': 2,
    '''rte''': 2,
    '''wnli''': 2,
}


logging.set_verbosity_info()





def      lowerCamelCase_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None       ):
				# Initialise PyTorch model
				lowerCamelCase_         =     XLNetConfig.from_json_file(lowerCamelCase__       )

				lowerCamelCase_         =     finetuning_task.lower() if finetuning_task is not None else ""
				if finetuning_task in GLUE_TASKS_NUM_LABELS:
								print(F'Building PyTorch XLNetForSequenceClassification model from configuration: {config}'       )
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     GLUE_TASKS_NUM_LABELS[finetuning_task]
								lowerCamelCase_         =     XLNetForSequenceClassification(lowerCamelCase__       )
				elif "squad" in finetuning_task:
								lowerCamelCase_         =     finetuning_task
								lowerCamelCase_         =     XLNetForQuestionAnswering(lowerCamelCase__       )
				else:
								lowerCamelCase_         =     XLNetLMHeadModel(lowerCamelCase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_xlnet(lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__       )

				# Save pytorch-model
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				lowerCamelCase_         =     os.path.join(lowerCamelCase__      ,					lowerCamelCase__       )
				print(F'Save PyTorch model to {os.path.abspath(lowerCamelCase__       )}'       )
				torch.save(model.state_dict()      ,					lowerCamelCase__       )
				print(F'Save configuration file to {os.path.abspath(lowerCamelCase__       )}'       )
				with open(lowerCamelCase__      ,					"w"      ,					encoding="utf-8"       ) as f:
								f.write(config.to_json_string()       )


if __name__ == "__main__":
			__A							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--xlnet_config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained XLNet model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=str,
			    required=True,
			    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
			)
			parser.add_argument(
			    '''--finetuning_task''',
			    default=None,
			    type=str,
			    help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
			)
			__A							=parser.parse_args()
			print(args)

			convert_xlnet_checkpoint_to_pytorch(
			    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
			)


