
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A							=       logging.get_logger(__name__)

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					int    =     ["""input_ids""", """attention_mask"""]

 def __init__(   self  ,						__UpperCamelCase="[PAD]"  ,						__UpperCamelCase="[BOS]"  ,						__UpperCamelCase="[EOS]"  ,						__UpperCamelCase="[MASK]"  ,						__UpperCamelCase="[CLS]"  ,						__UpperCamelCase="[SEP]"  ,						__UpperCamelCase=2_0_4_8  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else pad_token
        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else bos_token
        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else eos_token
        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else mask_token
        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else cls_token
        UpperCamelCase_											=							AddedToken(__UpperCamelCase  ,						lstrip=__UpperCamelCase  ,						rstrip=__UpperCamelCase  ) if isinstance(__UpperCamelCase  ,						__UpperCamelCase  ) else sep_token

        super().__init__(
            pad_token=__UpperCamelCase  ,						bos_token=__UpperCamelCase  ,						eos_token=__UpperCamelCase  ,						mask_token=__UpperCamelCase  ,						cls_token=__UpperCamelCase  ,						sep_token=__UpperCamelCase  ,						model_max_length=__UpperCamelCase  ,						**__UpperCamelCase  ,						)

        UpperCamelCase_											=							2**8  # utf is 8 bits

        # define special tokens dict
        UpperCamelCase_											=							{
            self.pad_token: 0,
            self.bos_token: 1,
            self.eos_token: 2,
            self.mask_token: 3,
            self.cls_token: 4,
            self.sep_token: 5,
        }
        UpperCamelCase_											=							len(self.special_tokens_encoder  )
        UpperCamelCase_											=							{v: k for k, v in self.special_tokens_encoder.items()}

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.special_tokens_encoder.copy()
        vocab.update(self.added_tokens_encoder  )
        for i in range(self._utf_vocab_size  ):
               UpperCamelCase_											=							chr(__UpperCamelCase  )
               UpperCamelCase_											=							i + len(self.special_tokens_encoder  )
        return vocab

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self._utf_vocab_size + self._num_special_tokens

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ,						__UpperCamelCase = False  ):




        """simple docstring"""


        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCamelCase  ,						token_ids_a=__UpperCamelCase  ,						already_has_special_tokens=__UpperCamelCase  )

        # normal case: some special tokens
        if token_ids_a is None:
               return [1] + [0] * len(__UpperCamelCase  ) + [1]
        return [1] + ([0] * len(__UpperCamelCase  )) + [1] + ([0] * len(__UpperCamelCase  )) + [1]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        else:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							[chr(__UpperCamelCase  ) for i in text.encode("""utf-8"""  )]
        return tokens

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        if token in self.special_tokens_encoder:
               UpperCamelCase_											=							self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
               UpperCamelCase_											=							self.added_tokens_encoder[token]
        elif len(__UpperCamelCase  ) != 1:
               UpperCamelCase_											=							self.unk_token_id
        else:
               UpperCamelCase_											=							ord(__UpperCamelCase  ) + self._num_special_tokens
        return token_id

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        if index in self.special_tokens_decoder:
               UpperCamelCase_											=							self.special_tokens_decoder[index]
        elif index in self.added_tokens_decoder:
               UpperCamelCase_											=							self.added_tokens_decoder[index]
        else:
               UpperCamelCase_											=							chr(index - self._num_special_tokens  )
        return token

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							b""""""
        for token in tokens:
               if token in self.special_tokens_decoder:
                      UpperCamelCase_											=							self.special_tokens_decoder[token].encode("""utf-8"""  )
               elif token in self.added_tokens_decoder:
                      UpperCamelCase_											=							self.special_tokens_decoder[token].encode("""utf-8"""  )
               elif token in self.special_tokens_encoder:
                      UpperCamelCase_											=							token.encode("""utf-8"""  )
               elif token in self.added_tokens_encoder:
                      UpperCamelCase_											=							token.encode("""utf-8"""  )
               else:
                      UpperCamelCase_											=							bytes([ord(__UpperCamelCase  )]  )
               bstring += tok_string
        UpperCamelCase_											=							bstring.decode("""utf-8"""  ,						errors="""replace"""  )
        return string

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase = None  ):




        """simple docstring"""


        return ()





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   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							=       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							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''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'])



