

"""simple docstring"""





import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a							=							get_tests_dir('fixtures/spiece.model')




@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     UpperCamelCase_					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     AlbertTokenizer
 __UpperCAmelCase     : Optional[int]       		=     AlbertTokenizerFast
 __UpperCAmelCase     : Dict       		=     True
 __UpperCAmelCase     : Union[str, Any]       		=     True
 __UpperCAmelCase     : int       		=     True


 def  _lowercase			(      self							:      Optional[int]     ):
    super().setUp()

    # We have a SentencePiece fixture for testing
    __lowercase								=						AlbertTokenizer(_a     )
    tokenizer.save_pretrained(self.tmpdirname     )


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      Optional[int]     ):
    __lowercase								=						"""this is a test"""
    __lowercase								=						"""this is a test"""
    return input_text, output_text


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						"""<pad>"""
    __lowercase								=						0

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


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						list(self.get_tokenizer().get_vocab().keys()     )

    self.assertEqual(vocab_keys[0], "<pad>"     )
    self.assertEqual(vocab_keys[1], "<unk>"     )
    self.assertEqual(vocab_keys[-1], "▁eloquent"     )
    self.assertEqual(len(_a     ), 3_0_0_0_0     )


 def  _lowercase			(      self							:      Union[str, Any]     ):
    self.assertEqual(self.get_tokenizer().vocab_size, 3_0_0_0_0     )


 def  _lowercase			(      self							:      List[Any]     ):


    if not self.test_rust_tokenizer:
       return

    __lowercase								=						self.get_tokenizer()
    __lowercase								=						self.get_rust_tokenizer()

    __lowercase								=						"""I was born in 92000, and this is falsé."""

    __lowercase								=						tokenizer.tokenize(_a     )
    __lowercase								=						rust_tokenizer.tokenize(_a     )
    self.assertListEqual(_a, _a     )

    __lowercase								=						tokenizer.encode(_a, add_special_tokens=_a     )
    __lowercase								=						rust_tokenizer.encode(_a, add_special_tokens=_a     )
    self.assertListEqual(_a, _a     )

    __lowercase								=						self.get_rust_tokenizer()
    __lowercase								=						tokenizer.encode(_a     )
    __lowercase								=						rust_tokenizer.encode(_a     )
    self.assertListEqual(_a, _a     )


 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						AlbertTokenizer(_a, keep_accents=_a     )

    __lowercase								=						tokenizer.tokenize("This is a test"     )
    self.assertListEqual(_a, ["▁this", "▁is", "▁a", "▁test"]     )

    self.assertListEqual(tokenizer.convert_tokens_to_ids(_a     ), [4_8, 2_5, 2_1, 1_2_8_9]     )

    __lowercase								=						tokenizer.tokenize("I was born in 92000, and this is falsé."     )
    self.assertListEqual(
        _a, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]     )
    __lowercase								=						tokenizer.convert_tokens_to_ids(_a     )
    self.assertListEqual(_a, [3_1, 2_3, 3_8_6, 1_9, 5_6_1, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]     )

    __lowercase								=						tokenizer.convert_ids_to_tokens(_a     )
    self.assertListEqual(
        _a, ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."], )


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						AlbertTokenizer(_a     )

    __lowercase								=						tokenizer.encode("sequence builders"     )
    __lowercase								=						tokenizer.encode("multi-sequence build"     )

    __lowercase								=						tokenizer.build_inputs_with_special_tokens(_a     )
    __lowercase								=						tokenizer.build_inputs_with_special_tokens(_a, _a     )

    assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
    assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
        tokenizer.sep_token_id
    ]



 @slow
 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						{"""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, 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]], """input_ids""": [[2, 2_1_9_7_0, 1_3, 5, 6_0_9_2, 1_6_7, 2_8, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 7_0_2_8, 1_2_0_5_1, 1_8, 1_7, 7_1_0_3, 2_1_5_3, 6_7_3, 8, 3_5_1_5, 1_8_6_8_4, 8, 4_4_6_1, 6, 1_9_2_7, 2_9_7, 8, 1_2_0_6_0, 2_6_0_7, 1_8, 1_3, 5, 4_4_6_1, 1_5, 1_0_5_3_8, 3_8, 8, 1_3_5, 1_5, 8_2_2, 5_8, 1_5, 9_9_3, 1_0_3_6_3, 1_5, 1_4_6_0, 8_0_0_5, 4_4_6_1, 1_5, 9_9_3, 2_5_5, 2_3_2_8, 9, 9, 9, 6, 2_6, 1_1_1_2, 8_1_6, 3_2_6_0, 1_3, 5, 1_0_3, 2_3_7_7, 6, 1_7, 1_1_1_2, 8_1_6, 2_7_8_2, 1_3, 5, 1_0_3, 1_0_6_4_1, 6, 2_9, 8_4, 2_5_1_2, 2_4_3_0, 7_8_2, 1_8_6_8_4, 2_7_6_1, 1_9, 8_0_8, 2_4_3_0, 2_5_5_6, 1_7, 8_5_5, 1_4_8_0, 9_4_7_7, 4_0_9_1, 1_2_8, 1_1_7_1_2, 1_5, 7_1_0_3, 2_1_5_3, 6_7_3, 1_7, 2_4_8_8_3, 9_9_9_0, 9, 3], [2, 1_1_5_0_2, 2_5, 1_0_0_6, 2_0, 7_8_2, 8, 1_1_8_0_9, 8_5_5, 1_7_3_2, 1_9_3_9_3, 1_8_6_6_7, 3_7, 3_6_7, 2_1_0_1_8, 6_9, 1_8_5_4, 3_4, 1_1_8_6_0, 1_9_1_2_4, 2_7, 1_5_6, 2_2_5, 1_7, 1_9_3, 4_1_4_1, 1_9, 6_5, 9_1_2_4, 9, 3, 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], [2, 1_4, 2_2_3_1, 8_8_6, 2_3_8_5, 1_7_6_5_9, 8_4, 1_4, 1_6_7_9_2, 1_9_5_2, 9, 3, 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]], """token_type_ids""": [[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, 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, 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, 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=_a, model_name="albert-base-v2", revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e", )







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




