






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__      :	Tuple       	=      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__      :	str       	=      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__      :	Dict       	=      rh.cluster(
      name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
  )
 else:
  snake_case__      :	Optional[Any]       	=      rh.cluster(
      name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 snake_case__      :	Dict       	=      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)













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


snake_case__      :	List[str]       	=      logging.get_logger(__name__)

snake_case__      :	Optional[int]       	=      {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case__      :	Optional[Any]       	=      {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case__      :	List[str]       	=      {
    """google/realm-cc-news-pretrained-embedder""": 5_1_2,
    """google/realm-cc-news-pretrained-encoder""": 5_1_2,
    """google/realm-cc-news-pretrained-scorer""": 5_1_2,
    """google/realm-cc-news-pretrained-openqa""": 5_1_2,
    """google/realm-orqa-nq-openqa""": 5_1_2,
    """google/realm-orqa-nq-reader""": 5_1_2,
    """google/realm-orqa-wq-openqa""": 5_1_2,
    """google/realm-orqa-wq-reader""": 5_1_2,
}

snake_case__      :	Tuple       	=      {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   VOCAB_FILES_NAMES
  A_						=   PRETRAINED_VOCAB_FILES_MAP
  A_						=   PRETRAINED_INIT_CONFIGURATION
  A_						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						=   RealmTokenizer




  def __init__(     self		,		_UpperCAmelCase=None		,		_UpperCAmelCase=None		,		_UpperCAmelCase=True		,		_UpperCAmelCase="[UNK]"		,		_UpperCAmelCase="[SEP]"		,		_UpperCAmelCase="[PAD]"		,		_UpperCAmelCase="[CLS]"		,		_UpperCAmelCase="[MASK]"		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Optional[Any]:
     super().__init__(
         _UpperCAmelCase		,		tokenizer_file=_UpperCAmelCase		,		do_lower_case=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		sep_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		cls_token=_UpperCAmelCase		,		mask_token=_UpperCAmelCase		,		tokenize_chinese_chars=_UpperCAmelCase		,		strip_accents=_UpperCAmelCase		,		**_UpperCAmelCase		,		)

     UpperCamelCase_									=     json.loads(self.backend_tokenizer.normalizer.__getstate__() )
     if (
         normalizer_state.get('lowercase'		,		_UpperCAmelCase ) != do_lower_case
         or normalizer_state.get('strip_accents'		,		_UpperCAmelCase ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'		,		_UpperCAmelCase ) != tokenize_chinese_chars
     ):
        UpperCamelCase_									=     getattr(_UpperCAmelCase		,		normalizer_state.pop('type' ) )
        UpperCamelCase_									=     do_lower_case
        UpperCamelCase_									=     strip_accents
        UpperCamelCase_									=     tokenize_chinese_chars
        UpperCamelCase_									=     normalizer_class(**_UpperCAmelCase )

     UpperCamelCase_									=     do_lower_case




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		**_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     PaddingStrategy.MAX_LENGTH

     UpperCamelCase_									=     text
     UpperCamelCase_									=     kwargs.pop('text_pair'		,		_UpperCAmelCase )
     UpperCamelCase_									=     kwargs.pop('return_tensors'		,		_UpperCAmelCase )

     UpperCamelCase_									=     {
         'input_ids': [],
         'attention_mask': [],
         'token_type_ids': [],
     }

     for idx, candidate_text in enumerate(_UpperCAmelCase ):
        if batch_text_pair is not None:
           UpperCamelCase_									=     batch_text_pair[idx]
        else:
           UpperCamelCase_									=     None

        UpperCamelCase_									=     super().__call__(_UpperCAmelCase		,		_UpperCAmelCase		,		return_tensors=_UpperCAmelCase		,		**_UpperCAmelCase )

        UpperCamelCase_									=     encoded_candidates.get('input_ids' )
        UpperCamelCase_									=     encoded_candidates.get('attention_mask' )
        UpperCamelCase_									=     encoded_candidates.get('token_type_ids' )

        if encoded_input_ids is not None:
           output_data["input_ids"].append(_UpperCAmelCase )
        if encoded_attention_mask is not None:
           output_data["attention_mask"].append(_UpperCAmelCase )
        if encoded_token_type_ids is not None:
           output_data["token_type_ids"].append(_UpperCAmelCase )

     UpperCamelCase_									=     {key: item for key, item in output_data.items() if len(_UpperCAmelCase ) != 0}

     return BatchEncoding(_UpperCAmelCase		,		tensor_type=_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> Union[str, Any]:
     UpperCamelCase_									=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

     return output




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> List[int]:
     UpperCamelCase_									=     [self.sep_token_id]
     UpperCamelCase_									=     [self.cls_token_id]
     if token_ids_a is None:
        return len(cls + token_ids_a + sep ) * [0]
     return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = None )			-> Tuple[str]:
     UpperCamelCase_									=     self._tokenizer.model.save(_UpperCAmelCase		,		name=_UpperCAmelCase )
     return tuple(_UpperCAmelCase )





