


'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 			lowerCamelCase_   (	__a     ,       unittest.TestCase      ):
      lowerCAmelCase__		     =     MvpTokenizer
      lowerCAmelCase__		     =     MvpTokenizerFast
      lowerCAmelCase__		     =     True
      lowerCAmelCase__		     =     filter_roberta_detectors






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          super().setUp()
          UpperCAmelCase__			:		Tuple													=				[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          UpperCAmelCase__			:		Dict													=				dict(zip(_A						,						range(len(_A ) ) ) )
          UpperCAmelCase__			:		Tuple													=				['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          UpperCAmelCase__			:		Optional[int]													=				{'''unk_token''': '''<unk>'''}

          UpperCAmelCase__			:		Any													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''vocab_file'''] )
          UpperCAmelCase__			:		int													=				os.path.join(self.tmpdirname						,						VOCAB_FILES_NAMES['''merges_file'''] )
          with open(self.vocab_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write(json.dumps(_A ) + '''\n''' )
          with open(self.merges_file						,						'''w'''						,						encoding='''utf-8''' ) as fp:
              fp.write('''\n'''.join(_A ) )






      def lowercase_							(					self				:    Tuple						,						**_A				:    Any ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    int						,						**_A				:    Union[str, Any] ):


          '''simple docstring'''


          kwargs.update(self.special_tokens_map )
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,						**_A )






      def lowercase_							(					self				:    Optional[int]						,						_A				:    List[Any] ):


          '''simple docstring'''


          return "lower newer", "lower newer"






      @cached_property
      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          return MvpTokenizer.from_pretrained('''RUCAIBox/mvp''' )






      @cached_property
      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp''' )






      @require_torch
      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          UpperCAmelCase__			:		Dict													=				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[int]													=				tokenizer(_A						,						max_length=len(_A )						,						padding=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )

              self.assertEqual((2, 9)						,						batch.input_ids.shape )
              self.assertEqual((2, 9)						,						batch.attention_mask.shape )
              UpperCAmelCase__			:		Optional[int]													=				batch.input_ids.tolist()[0]
              self.assertListEqual(_A						,						_A )
              # Test that special tokens are reset






      @require_torch
      def lowercase_							(					self				:    Dict ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(_A						,						padding=_A						,						return_tensors='''pt''' )
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''						,						_A )
              self.assertIn('''attention_mask'''						,						_A )
              self.assertNotIn('''labels'''						,						_A )
              self.assertNotIn('''decoder_attention_mask'''						,						_A )






      @require_torch
      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Tuple													=				[
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Any													=				tokenizer(text_target=_A						,						max_length=32						,						padding='''max_length'''						,						return_tensors='''pt''' )
              self.assertEqual(32						,						targets['''input_ids'''].shape[1] )






      @require_torch
      def lowercase_							(					self				:    int ):


          '''simple docstring'''


          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Optional[Any]													=				tokenizer(
                  ['''I am a small frog''' * 1_024, '''I am a small frog''']						,						padding=_A						,						truncation=_A						,						return_tensors='''pt''' )
              self.assertIsInstance(_A						,						_A )
              self.assertEqual(batch.input_ids.shape						,						(2, 1_024) )






      @require_torch
      def lowercase_							(					self				:    Union[str, Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				['''A long paragraph for summarization.''']
          UpperCAmelCase__			:		Optional[Any]													=				[
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCAmelCase__			:		Tuple													=				tokenizer(_A						,						text_target=_A						,						return_tensors='''pt''' )
              UpperCAmelCase__			:		str													=				inputs['''input_ids''']
              UpperCAmelCase__			:		str													=				inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )






      def lowercase_							(					self				:    Any ):


          '''simple docstring'''


          pass






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
                  UpperCAmelCase__			:		str													=				self.rust_tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		Union[str, Any]													=				self.tokenizer_class.from_pretrained(_A						,						**_A )
                  UpperCAmelCase__			:		Dict													=				'''A, <mask> AllenNLP sentence.'''
                  UpperCAmelCase__			:		Optional[Any]													=				tokenizer_r.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )
                  UpperCAmelCase__			:		Tuple													=				tokenizer_p.encode_plus(_A						,						add_special_tokens=_A						,						return_token_type_ids=_A )

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids'''] )						,						sum(tokens_p['''token_type_ids'''] ) )

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask'''] ) / len(tokens_r['''attention_mask'''] )						,						sum(tokens_p['''attention_mask'''] ) / len(tokens_p['''attention_mask'''] )						,						)

                  UpperCAmelCase__			:		str													=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
                  UpperCAmelCase__			:		List[Any]													=				tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''] )

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                  self.assertSequenceEqual(tokens_r['''input_ids''']						,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )
                  self.assertSequenceEqual(
                      _A						,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>'''] )




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