


"""simple docstring"""






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
	from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase     =  get_tests_dir('fixtures/test_sentencepiece.model')

_lowerCamelCase     =  {'target_lang': 'fi', 'source_lang': 'en'}
_lowerCamelCase     =  '>>zh<<'
_lowerCamelCase     =  'Helsinki-NLP/'

if is_torch_available():
	_lowerCamelCase     =  'pt'
elif is_tf_available():
	_lowerCamelCase     =  'tf'
else:
	_lowerCamelCase     =  'jax'


@require_sentencepiece
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Any      							=					MarianTokenizer
				_lowerCAmelCase      :      List[str]      							=					False
				_lowerCAmelCase      :      Dict      							=					True



				def   lowerCAmelCase__		(						self     ):
									super().setUp()
									SCREAMING_SNAKE_CASE__     							=				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
									SCREAMING_SNAKE_CASE__     							=				dict(zip(UpperCAmelCase__       ,		range(len(UpperCAmelCase__     )     )     )     )
									SCREAMING_SNAKE_CASE__     							=				Path(self.tmpdirname     )
									save_json(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["vocab"]     )
									save_json(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]     )
									if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
														copyfile(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["source_spm"]     )
														copyfile(UpperCAmelCase__       ,		save_dir / VOCAB_FILES_NAMES["target_spm"]     )

									SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained(self.tmpdirname     )
									tokenizer.save_pretrained(self.tmpdirname     )



				def   lowerCAmelCase__		(						self       ,		**UpperCAmelCase__     ):
									return MarianTokenizer.from_pretrained(self.tmpdirname       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
									return (
									    "This is a test",
									    "This is a test",
									)



				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				"</s>"
									SCREAMING_SNAKE_CASE__     							=				0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase__     )       ,		UpperCAmelCase__     )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase__     )       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]       ,		"</s>"     )
									self.assertEqual(vocab_keys[1]       ,		"<unk>"     )
									self.assertEqual(vocab_keys[-1]       ,		"<pad>"     )
									self.assertEqual(len(UpperCAmelCase__     )       ,		9     )



				def   lowerCAmelCase__		(						self     ):
									self.assertEqual(self.get_tokenizer().vocab_size       ,		9     )



				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained(f'''{ORG_NAME}opus-mt-en-de'''     )
									SCREAMING_SNAKE_CASE__     							=				en_de_tokenizer(["I am a small frog"]       ,		return_tensors=UpperCAmelCase__     )
									self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
									SCREAMING_SNAKE_CASE__     							=				[38, 121, 14, 697, 3_8848, 0]
									self.assertListEqual(UpperCAmelCase__       ,		batch.input_ids[0]     )

									SCREAMING_SNAKE_CASE__     							=				tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(UpperCAmelCase__     )
									SCREAMING_SNAKE_CASE__     							=				[x.name for x in Path(UpperCAmelCase__     ).glob("*"     )]
									self.assertIn("source.spm"       ,		UpperCAmelCase__     )
									MarianTokenizer.from_pretrained(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

									SCREAMING_SNAKE_CASE__     							=				tok(
									    ["I am a small frog" * 1000, "I am a small frog"]       ,		padding=UpperCAmelCase__       ,		truncation=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__     )
									self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
									self.assertEqual(batch.input_ids.shape       ,		(2, 512)     )



				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()
									SCREAMING_SNAKE_CASE__     							=				tok(["I am a tiny frog", "I am a small frog"]       ,		padding=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__     )
									self.assertIsInstance(UpperCAmelCase__       ,		UpperCAmelCase__     )
									self.assertEqual(batch_smaller.input_ids.shape       ,		(2, 10)     )



				@slow
				def   lowerCAmelCase__		(						self     ):
									# fmt: off
									SCREAMING_SNAKE_CASE__     							=				{"input_ids": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=UpperCAmelCase__       ,		model_name="Helsinki-NLP/opus-mt-en-de"       ,		revision="1a8c2263da11e68e50938f97e10cd57820bd504c"       ,		decode_kwargs={"use_source_tokenizer": True}       ,		)






				def   lowerCAmelCase__		(						self     ):
									SCREAMING_SNAKE_CASE__     							=				MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"     )

									SCREAMING_SNAKE_CASE__     							=				"Tämä on testi"
									SCREAMING_SNAKE_CASE__     							=				"This is a test"

									SCREAMING_SNAKE_CASE__     							=				[76, 7, 2047, 2]
									SCREAMING_SNAKE_CASE__     							=				[69, 12, 11, 940, 2]

									SCREAMING_SNAKE_CASE__     							=				tokenizer(UpperCAmelCase__     ).input_ids
									self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

									SCREAMING_SNAKE_CASE__     							=				tokenizer(text_target=UpperCAmelCase__     ).input_ids
									self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

									SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__       ,		skip_special_tokens=UpperCAmelCase__     )
									self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )




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