



'''simple docstring'''





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A			(  __snake_case   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       OpenAIGPTTokenizer
 __lowerCamelCase    :       Optional[int]          =       OpenAIGPTTokenizerFast
 __lowerCamelCase    :       Dict          =       True
 __lowerCamelCase    :       List[Any]          =       False



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a      =  [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          'w</w>',
          'r</w>',
          't</w>',
          'lo',
          'low',
          'er</w>',
          'low</w>',
          'lowest</w>',
          'newer</w>',
          'wider</w>',
          '<unk>',
      ]
      _a      =  dict(zip(A ,   range(len(A       )       )       )       )
      _a      =  ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

      _a      =  os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''vocab_file''']       )
      _a      =  os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''merges_file''']       )
      with open(self.vocab_file ,   '''w'''       ) as fp:
           fp.write(json.dumps(A       )       )
      with open(self.merges_file ,   '''w'''       ) as fp:
           fp.write('''\n'''.join(A       )       )



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      return "lower newer", "lower newer"



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  OpenAIGPTTokenizer(self.vocab_file ,   self.merges_file       )

      _a      =  'lower'
      _a      =  ['low', 'er</w>']
      _a      =  tokenizer.tokenize(A       )
      self.assertListEqual(A ,   A       )

      _a      =  tokens + ['<unk>']
      _a      =  [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A       ) ,   A       )



 def 					a__  (self ,   A=15       )    ->     List[str]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                _a      =  self.rust_tokenizer_class.from_pretrained(A ,   **A       )

                # Simple input
                _a      =  'This is a simple input'
                _a      =  ['This is a simple input 1', 'This is a simple input 2']
                _a      =  ('This is a simple input', 'This is a pair')
                _a      =  [
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(A ,   tokenizer_r.encode ,   A ,   max_length=A ,   padding='''max_length'''       )

                # Simple input
                self.assertRaises(A ,   tokenizer_r.encode_plus ,   A ,   max_length=A ,   padding='''max_length'''       )

                # Simple input
                self.assertRaises(
                    A ,   tokenizer_r.batch_encode_plus ,   A ,   max_length=A ,   padding='''max_length''' ,   )

                # Pair input
                self.assertRaises(A ,   tokenizer_r.encode ,   A ,   max_length=A ,   padding='''max_length'''       )

                # Pair input
                self.assertRaises(A ,   tokenizer_r.encode_plus ,   A ,   max_length=A ,   padding='''max_length'''       )

                # Pair input
                self.assertRaises(
                    A ,   tokenizer_r.batch_encode_plus ,   A ,   max_length=A ,   padding='''max_length''' ,   )



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      pass






@require_ftfy
@require_spacy
@require_tokenizers
class __A			(  __snake_case						):



 '''simple docstring'''







 pass






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)

