



'''simple docstring'''




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
						lowerCamelCase							:Tuple				    =					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''')
						lowerCamelCase    ,						lowerCamelCase							:int				    =					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''')
												lowerCamelCase							:Tuple				    =					rh.cluster(
												    name='''rh-cluster''', ips=[args.host], ssh_creds={'''ssh_user''': args.user, '''ssh_private_key''': args.key_path}
												)
						else:
												lowerCamelCase							:Union[str, Any]				    =					rh.cluster(
												    name='''rh-cluster''', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
												)
						lowerCamelCase							:str				    =					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)




'''simple docstring'''




import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 	_lowerCAmelCase							(			unittest.TestCase ):


	def     _a	(self							):
				A_	:						Optional[Any]	=     10


	def     _a	(self							):
				A_	:						Dict	=     [1, 2, 3, 4]
				A_	:						List[Any]	=     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				A_	:						Tuple	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
				A_	:						Any	=     [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
				self.assertEqual(truncate_or_pad(lowercase       ,      self.block_size       ,      0							)       ,      lowercase							)


	def     _a	(self							):
				A_	:						List[str]	=     """It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this."""
				A_,    A_	:						Dict	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[int]	=     """"""
				A_,    A_	:						List[str]	=     process_story(lowercase							)
				self.assertEqual(lowercase       ,      []							)
				self.assertEqual(lowercase       ,      []							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     (
				    """It was the year of Our Lord one thousand seven hundred and """
				    """seventy-five\n\nSpiritual revelations were conceded to England """
				    """at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
				)
				A_,    A_	:						int	=     process_story(lowercase							)

				A_	:						Optional[Any]	=     [
				    """It was the year of Our Lord one thousand seven hundred and seventy-five.""",
				    """Spiritual revelations were conceded to England at that favoured period, as at this.""",
				]
				self.assertEqual(lowercase       ,      lowercase							)

				A_	:						Dict	=     ["""It was the best of times."""]
				self.assertEqual(lowercase       ,      lowercase							)


	def     _a	(self							):
				A_	:						Optional[int]	=     torch.tensor([1, 2, 3, 4]							)
				A_	:						Dict	=     torch.tensor([1, 1, 1, 1]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      0							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						str	=     torch.tensor([1, 2, 3, 4, 23, 23, 23]							)
				A_	:						str	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      23							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						Any	=     torch.tensor([8, 2, 3, 4, 1, 1, 1]							)
				A_	:						List[str]	=     torch.tensor([1, 1, 1, 1, 0, 0, 0]							)
				np.testing.assert_array_equal(build_mask(lowercase       ,      1							).numpy()       ,      expected.numpy()							)


	def     _a	(self							):
				A_	:						List[Any]	=     101
				A_	:						List[Any]	=     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]							)
				A_	:						List[str]	=     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]							)

				A_	:						Dict	=     compute_token_type_ids(lowercase       ,      lowercase							)
				np.testing.assert_array_equal(lowercase       ,      lowercase							)