
"""simple docstring"""




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def   __magic_name__     (				*__snake_case    :	Tuple  )			->    Optional[Any]:
       lowercase   :    List[Any]			=							torch.cuda.device_count()
       print(f"""Device count: {num_processes}"""  )
       with patch_environment(
           world_size=__snake_case					,			master_addr="127.0.01"					,			master_port="29500"					,			mixed_precision=args[1].mixed_precision  ):
              lowercase   :    List[Any]			=							PrepareForLaunch(__snake_case					,			distributed_type="MULTI_GPU"  )
              torch.multiprocessing.start_processes(__snake_case					,			args=__snake_case					,			nprocs=__snake_case					,			start_method="spawn"  )


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
  # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

  # on-demand GPU
  # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
  _A      :	Dict       		=	rh.cluster(name="""rh-cluster""", instance_type="""V100:4""", provider="""cheapest""", use_spot=False)  # multi GPU
  gpu.up_if_not()

  # on-prem GPU
  # gpu = rh.cluster(
  #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
  #       )

  # Set up remote function
  _A      :	Optional[int]       		=	[
      """pip:./""",
      """transformers""",
      """datasets""",
      """evaluate""",
      """tqdm""",
      """scipy""",
      """scikit-learn""",
      """tensorboard""",
      """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
  ]
  _A      :	Any       		=	rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

  # Define train args/config, run train function
  _A      :	Tuple       		=	argparse.Namespace(cpu=False, mixed_precision="""fp16""")
  _A      :	Any       		=	{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  launch_train_gpu(config, train_args, stream_logs=True)

  # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
  # gpu.install_packages(reqs)
  # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])




"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 		a__						(				a_,      unittest.TestCase						):
   __lowerCAmelCase						=    BertweetTokenizer
   __lowerCAmelCase						=    False
   def       __magic_name__      (      self    ):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase   :    Tuple			=							["I", "m", "V@@", "R@@", "r", "e@@"]
          lowercase   :    List[Any]			=							dict(zip(_a     ,   range(len(_a    )    )    )    )
          lowercase   :    Union[str, Any]			=							["#version: 0.2", "a m</w>"]
          lowercase   :    List[Any]			=							{"unk_token": "<unk>"}

          lowercase   :    Optional[Any]			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]    )
          lowercase   :    Any			=							os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]    )
          with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 for token in vocab_tokens:
                        fp.write(f"""{token} {vocab_tokens[token]}\n"""    )
          with open(self.merges_file     ,   "w"     ,   encoding="utf-8"    ) as fp:
                 fp.write("\n".join(_a    )    )
   def       __magic_name__      (      self     ,   **_a    ):
          kwargs.update(self.special_tokens_map    )
          return BertweetTokenizer.from_pretrained(self.tmpdirname     ,   **_a    )
   def       __magic_name__      (      self     ,   _a    ):
          lowercase   :    Union[str, Any]			=							"I am VinAI Research"
          lowercase   :    str			=							"I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
          return input_text, output_text

   def       __magic_name__      (      self    ):
          lowercase   :    List[Any]			=							BertweetTokenizer(self.vocab_file     ,   self.merges_file     ,   **self.special_tokens_map    )
          lowercase   :    int			=							"I am VinAI Research"
          lowercase   :    Dict			=							"I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
          lowercase   :    Tuple			=							tokenizer.tokenize(_a    )
          self.assertListEqual(_a     ,   _a    )

          lowercase   :    Any			=							tokens + [tokenizer.unk_token]

          lowercase   :    List[Any]			=							[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a    )     ,   _a    )


