






"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase_       			=   25_0004
lowerCamelCase_       			=   25_0020







@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       MBartTokenizer
	__magic_name__			   =       MBartTokenizerFast
	__magic_name__			   =       True
	__magic_name__			   =       True





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    int:
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCAmelCase_  :				Optional[int]      =      MBartTokenizer(lowerCAmelCase_			,			keep_accents=lowerCAmelCase_		)
				tokenizer.save_pretrained(self.tmpdirname		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				UpperCAmelCase_  :				Optional[int]      =      MBartTokenizer(lowerCAmelCase_			,			keep_accents=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[Any]      =      tokenizer.tokenize("This is a test"		)
				self.assertListEqual(lowerCAmelCase_			,			["▁This", "▁is", "▁a", "▁t", "est"]		)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)			,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,			)

				UpperCAmelCase_  :				Any      =      tokenizer.tokenize("I was born in 92000, and this is falsé."		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "9",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "é",
				        ".",
				    ]			,			)
				UpperCAmelCase_  :				Optional[int]      =      tokenizer.convert_tokens_to_ids(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
				        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
				    ]			,			)

				UpperCAmelCase_  :				List[Any]      =      tokenizer.convert_ids_to_tokens(lowerCAmelCase_		)
				self.assertListEqual(
				    lowerCAmelCase_			,			[
				        SPIECE_UNDERLINE + "I",
				        SPIECE_UNDERLINE + "was",
				        SPIECE_UNDERLINE + "b",
				        "or",
				        "n",
				        SPIECE_UNDERLINE + "in",
				        SPIECE_UNDERLINE + "",
				        "<unk>",
				        "2",
				        "0",
				        "0",
				        "0",
				        ",",
				        SPIECE_UNDERLINE + "and",
				        SPIECE_UNDERLINE + "this",
				        SPIECE_UNDERLINE + "is",
				        SPIECE_UNDERLINE + "f",
				        "al",
				        "s",
				        "<unk>",
				        ".",
				    ]			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Optional[Any]:
				if not self.test_slow_tokenizer:
							# as we don't have a slow version, we can't compare the outputs between slow and fast versions
							return

				UpperCAmelCase_  :				Dict      =      (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
										UpperCAmelCase_  :				Optional[Any]      =      self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_		)
										UpperCAmelCase_  :				Optional[Any]      =      self.tokenizer_class.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_		)

										UpperCAmelCase_  :				Any      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				Dict      =      tokenizer_r.save_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				Any      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it save with the same files + the tokenizer.json file for the fast one
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)
										UpperCAmelCase_  :				Optional[int]      =      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f		)
										self.assertSequenceEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Any      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				Any      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

										shutil.rmtree(lowerCAmelCase_		)

										# Save tokenizer rust, legacy_format=True
										UpperCAmelCase_  :				Optional[int]      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				Optional[Any]      =      tokenizer_r.save_pretrained(lowerCAmelCase_			,			legacy_format=lowerCAmelCase_		)
										UpperCAmelCase_  :				List[Any]      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it save with the same files
										self.assertSequenceEqual(lowerCAmelCase_			,			lowerCAmelCase_		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Union[str, Any]      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				Optional[Any]      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)

										shutil.rmtree(lowerCAmelCase_		)

										# Save tokenizer rust, legacy_format=False
										UpperCAmelCase_  :				List[str]      =      tempfile.mkdtemp()

										UpperCAmelCase_  :				Union[str, Any]      =      tokenizer_r.save_pretrained(lowerCAmelCase_			,			legacy_format=lowerCAmelCase_		)
										UpperCAmelCase_  :				Optional[Any]      =      tokenizer_p.save_pretrained(lowerCAmelCase_		)

										# Checks it saved the tokenizer.json file
										self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files		)		)

										# Checks everything loads correctly in the same way
										UpperCAmelCase_  :				Union[str, Any]      =      tokenizer_r.from_pretrained(lowerCAmelCase_		)
										UpperCAmelCase_  :				Tuple      =      tokenizer_p.from_pretrained(lowerCAmelCase_		)

										# Check special tokens are set accordingly on Rust and Python
										for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(lowerCAmelCase_			,			lowerCAmelCase_		)		)

										shutil.rmtree(lowerCAmelCase_		)







@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase_ (unittest.TestCase  ):
	__magic_name__			   =       '''facebook/mbart-large-en-ro'''
	__magic_name__			   =       [
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
	]
	__magic_name__			   =       [
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
	    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
	    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
	]
	__magic_name__			   =       [82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				List[Any]		)		->    Dict:
				UpperCAmelCase_  :				MBartTokenizer      =      MBartTokenizer.from_pretrained(
				    cls.checkpoint_name			,			src_lang="en_XX"			,			tgt_lang="ro_RO"		)
				UpperCAmelCase_  :				Tuple      =      1
				return cls





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]			,			250_001		)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]			,			250_004		)
				self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]			,			250_020		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Union[str, Any]:
				UpperCAmelCase_  :				List[str]      =      self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
				self.assertListEqual(self.expected_src_tokens			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				self.assertIn(lowerCAmelCase_			,			self.tokenizer.all_special_ids		)
				UpperCAmelCase_  :				Optional[Any]      =      [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
				UpperCAmelCase_  :				Any      =      self.tokenizer.decode(lowerCAmelCase_			,			skip_special_tokens=lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      self.tokenizer.decode(generated_ids[1:]			,			skip_special_tokens=lowerCAmelCase_		)
				self.assertEqual(lowerCAmelCase_			,			lowerCAmelCase_		)
				self.assertNotIn(self.tokenizer.eos_token			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				List[str]      =      ["this is gunna be a long sentence " * 20]
				assert isinstance(src_text[0]			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[int]      =      10
				UpperCAmelCase_  :				List[str]      =      self.tokenizer(lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			truncation=lowerCAmelCase_		).input_ids[0]
				self.assertEqual(ids[-2]			,			2		)
				self.assertEqual(ids[-1]			,			lowerCAmelCase_		)
				self.assertEqual(len(lowerCAmelCase_		)			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Tuple:
				self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]		)			,			[250_026, 250_001]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				Dict      =      tempfile.mkdtemp()
				UpperCAmelCase_  :				Optional[Any]      =      self.tokenizer.fairseq_tokens_to_ids
				self.tokenizer.save_pretrained(lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      MBartTokenizer.from_pretrained(lowerCAmelCase_		)
				self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,			lowerCAmelCase_		)





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    List[str]:
				UpperCAmelCase_  :				List[str]      =      self.tokenizer(self.src_text			,			text_target=self.tgt_text			,			padding=lowerCAmelCase_			,			return_tensors="pt"		)
				UpperCAmelCase_  :				List[Any]      =      shift_tokens_right(batch["labels"]			,			self.tokenizer.pad_token_id		)

				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
				assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
				assert batch.decoder_input_ids[1][-1] == 2
				assert batch.labels[1][-2:].tolist() == [2, RO_CODE]





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    List[Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      self.tokenizer(
				    self.src_text			,			text_target=self.tgt_text			,			padding=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=len(self.expected_src_tokens		)			,			return_tensors="pt"			,			)

				UpperCAmelCase_  :				Optional[int]      =      shift_tokens_right(batch["labels"]			,			self.tokenizer.pad_token_id		)

				self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_		)

				self.assertEqual((2, 14)			,			batch.input_ids.shape		)
				self.assertEqual((2, 14)			,			batch.attention_mask.shape		)
				UpperCAmelCase_  :				Optional[Any]      =      batch.input_ids.tolist()[0]
				self.assertListEqual(self.expected_src_tokens			,			lowerCAmelCase_		)
				self.assertEqual(2			,			batch.decoder_input_ids[0, -1]		)  # EOS
				# Test that special tokens are reset
				self.assertEqual(self.tokenizer.prefix_tokens			,			[]		)
				self.assertEqual(self.tokenizer.suffix_tokens			,			[self.tokenizer.eos_token_id, EN_CODE]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    str:
				UpperCAmelCase_  :				Union[str, Any]      =      self.tokenizer(self.src_text			,			padding=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=3			,			return_tensors="pt"		)
				UpperCAmelCase_  :				int      =      self.tokenizer(
				    text_target=self.tgt_text			,			padding=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=10			,			return_tensors="pt"		)
				UpperCAmelCase_  :				Any      =      targets["input_ids"]
				UpperCAmelCase_  :				Optional[int]      =      shift_tokens_right(lowerCAmelCase_			,			self.tokenizer.pad_token_id		)

				self.assertEqual(batch.input_ids.shape[1]			,			3		)
				self.assertEqual(batch.decoder_input_ids.shape[1]			,			10		)





	@require_torch
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Any:
				UpperCAmelCase_  :				str      =      self.tokenizer._build_translation_inputs(
				    "A test"			,			return_tensors="pt"			,			src_lang="en_XX"			,			tgt_lang="ar_AR"		)

				self.assertEqual(
				    nested_simplify(lowerCAmelCase_		)			,			{
				        # A, test, EOS, en_XX
				        "input_ids": [[62, 3_034, 2, 250_004]],
				        "attention_mask": [[1, 1, 1, 1]],
				        # ar_AR
				        "forced_bos_token_id": 250_001,
				    }			,			)











"""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)



