
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


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

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

_snake_case      = 250_004
_snake_case      = 250_020
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase  (		__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Any       						=MBartaaTokenizer
 SCREAMING_SNAKE_CASE_				:					Dict       						=MBartaaTokenizerFast
 SCREAMING_SNAKE_CASE_				:					Union[str, Any]       						=True
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=True

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase						    =					MBartaaTokenizer(SCREAMING_SNAKE_CASE__					, src_lang='en_XX'					, tgt_lang='ro_RO'					, keep_accents=SCREAMING_SNAKE_CASE__   )
      tokenizer.save_pretrained(self.tmpdirname   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					'<s>'
      UpperCamelCase						    =					0

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

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0]					, '<s>'   )
      self.assertEqual(vocab_keys[1]					, '<pad>'   )
      self.assertEqual(vocab_keys[-1]					, '<mask>'   )
      self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, 10_54   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      self.assertEqual(self.get_tokenizer().vocab_size					, 10_54   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					MBartaaTokenizer(SCREAMING_SNAKE_CASE__					, src_lang='en_XX'					, tgt_lang='ro_RO'					, keep_accents=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					tokenizer.tokenize('This is a test'   )
      self.assertListEqual(SCREAMING_SNAKE_CASE__					, ['▁This', '▁is', '▁a', '▁t', 'est']   )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					, [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					, )

      UpperCamelCase						    =					tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
      self.assertListEqual(
          SCREAMING_SNAKE_CASE__					, [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						    =					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )
      self.assertListEqual(
          SCREAMING_SNAKE_CASE__					, [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
          ]					, )

      UpperCamelCase						    =					tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__   )
      self.assertListEqual(
          SCREAMING_SNAKE_CASE__					, [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>', '.']					, )

 @slow
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					{'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], '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, 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], [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, 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=SCREAMING_SNAKE_CASE__					, model_name='facebook/mbart-large-50'					, revision='d3913889c59cd5c9e456b269c376325eabad57e2'					, )






 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      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						    =					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
                UpperCamelCase						    =					self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

                UpperCamelCase						    =					tempfile.mkdtemp()

                UpperCamelCase						    =					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__   )

                # 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						    =					tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f   )
                self.assertSequenceEqual(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

                # Checks everything loads correctly in the same way
                UpperCamelCase						    =					tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__   )

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

                shutil.rmtree(SCREAMING_SNAKE_CASE__   )

                # Save tokenizer rust, legacy_format=True
                UpperCamelCase						    =					tempfile.mkdtemp()

                UpperCamelCase						    =					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__					, legacy_format=SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__   )

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

                # Checks everything loads correctly in the same way
                UpperCamelCase						    =					tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__   )

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

                shutil.rmtree(SCREAMING_SNAKE_CASE__   )

                # Save tokenizer rust, legacy_format=False
                UpperCamelCase						    =					tempfile.mkdtemp()

                UpperCamelCase						    =					tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__					, legacy_format=SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__   )

                # 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						    =					tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__   )
                UpperCamelCase						    =					tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__   )

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

                shutil.rmtree(SCREAMING_SNAKE_CASE__   )




@require_torch
@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					str       						="facebook/mbart-large-50-one-to-many-mmt"
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=[
     " 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.",
 ]
 SCREAMING_SNAKE_CASE_				:					Dict       						=[
     "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.",
 ]
 SCREAMING_SNAKE_CASE_				:					List[str]       						=[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]

 @classmethod
 def      __lowerCAmelCase  (						cls			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					MBartaaTokenizer.from_pretrained(
          cls.checkpoint_name					, src_lang='en_XX'					, tgt_lang='ro_RO'   )
      UpperCamelCase						    =					1
      return cls

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']					, 25_00_01   )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']					, 25_00_04   )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']					, 25_00_20   )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']					, 25_00_38   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      self.assertIn(SCREAMING_SNAKE_CASE__					, self.tokenizer.all_special_ids   )
      UpperCamelCase						    =					[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
      UpperCamelCase						    =					self.tokenizer.decode(SCREAMING_SNAKE_CASE__					, skip_special_tokens=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					self.tokenizer.decode(generated_ids[1:]					, skip_special_tokens=SCREAMING_SNAKE_CASE__   )
      self.assertEqual(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )
      self.assertNotIn(self.tokenizer.eos_token					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					['this is gunna be a long sentence ' * 20]
      assert isinstance(src_text[0]					, SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					10
      UpperCamelCase						    =					self.tokenizer(SCREAMING_SNAKE_CASE__					, max_length=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__   ).input_ids[0]
      self.assertEqual(ids[0]					, SCREAMING_SNAKE_CASE__   )
      self.assertEqual(ids[-1]					, 2   )
      self.assertEqual(len(SCREAMING_SNAKE_CASE__   )					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']   )					, [25_00_53, 25_00_01]   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					tempfile.mkdtemp()
      UpperCamelCase						    =					self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					MBartaaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__   )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids					, SCREAMING_SNAKE_CASE__   )

 @require_torch
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.tokenizer(self.src_text					, text_target=self.tgt_text					, padding=SCREAMING_SNAKE_CASE__					, return_tensors='pt'   )
      UpperCamelCase						    =					shift_tokens_right(batch['labels']					, self.tokenizer.pad_token_id   )

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

 @require_torch
 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.tokenizer(
          self.src_text					, text_target=self.tgt_text					, padding=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, max_length=len(self.expected_src_tokens   )					, return_tensors='pt'					, )
      UpperCamelCase						    =					shift_tokens_right(batch['labels']					, self.tokenizer.pad_token_id   )

      self.assertIsInstance(SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__   )

      self.assertEqual((2, 14)					, batch.input_ids.shape   )
      self.assertEqual((2, 14)					, batch.attention_mask.shape   )
      UpperCamelCase						    =					batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens					, SCREAMING_SNAKE_CASE__   )
      self.assertEqual(2					, batch.decoder_input_ids[0, 0]   )  # decoder_start_token_id
      # Test that special tokens are reset
      self.assertEqual(self.tokenizer.prefix_tokens					, [EN_CODE]   )
      self.assertEqual(self.tokenizer.suffix_tokens					, [self.tokenizer.eos_token_id]   )

 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      UpperCamelCase						    =					self.tokenizer(self.src_text					, padding=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, max_length=3					, return_tensors='pt'   )
      UpperCamelCase						    =					self.tokenizer(
          text_target=self.tgt_text					, padding=SCREAMING_SNAKE_CASE__					, truncation=SCREAMING_SNAKE_CASE__					, max_length=10					, return_tensors='pt'   )
      UpperCamelCase						    =					targets['input_ids']
      UpperCamelCase						    =					shift_tokens_right(SCREAMING_SNAKE_CASE__					, 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      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					self.tokenizer._build_translation_inputs(
          'A test'					, return_tensors='pt'					, src_lang='en_XX'					, tgt_lang='ar_AR'   )

      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE__   )					, {
              # en_XX, A, test, EOS
              'input_ids': [[25_00_04, 62, 30_34, 2]],
              'attention_mask': [[1, 1, 1, 1]],
              # ar_AR
              'forced_bos_token_id': 25_00_01,
          }					, )




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


