



import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	{
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}





class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[Any]       					=  "xlnet"
       UpperCAmelCase__    : Optional[Any]       					=  ["mems"]
       UpperCAmelCase__    : Dict       					=  {
           "n_token": "vocab_size",  # Backward compatibility
           "hidden_size": "d_model",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }
       def __init__(      self,      SCREAMING_SNAKE_CASE_=3_2000,      SCREAMING_SNAKE_CASE_=1024,      SCREAMING_SNAKE_CASE_=24,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=4096,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_="bi",      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=1e-12,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=-1,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_="last",      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_="tanh",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=2,      **SCREAMING_SNAKE_CASE_,      )					->		int:
          UpperCamelCase   :  Union[str, Any]						     =     vocab_size
          UpperCamelCase   :  List[str]						     =     d_model
          UpperCamelCase   :  Union[str, Any]						     =     n_layer
          UpperCamelCase   :  Optional[int]						     =     n_head
          if d_model % n_head != 0:
             raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""	)
          if "d_head" in kwargs:
             if kwargs["d_head"] != d_model // n_head:
                raise ValueError(
                    F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""	)
          UpperCamelCase   :  Tuple						     =     d_model // n_head
          UpperCamelCase   :  Tuple						     =     ff_activation
          UpperCamelCase   :  str						     =     d_inner
          UpperCamelCase   :  List[str]						     =     untie_r
          UpperCamelCase   :  Optional[Any]						     =     attn_type

          UpperCamelCase   :  List[Any]						     =     initializer_range
          UpperCamelCase   :  Union[str, Any]						     =     layer_norm_eps

          UpperCamelCase   :  Any						     =     dropout
          UpperCamelCase   :  Optional[int]						     =     mem_len
          UpperCamelCase   :  Optional[int]						     =     reuse_len
          UpperCamelCase   :  List[Any]						     =     bi_data
          UpperCamelCase   :  List[str]						     =     clamp_len
          UpperCamelCase   :  Tuple						     =     same_length

          UpperCamelCase   :  List[str]						     =     summary_type
          UpperCamelCase   :  List[Any]						     =     summary_use_proj
          UpperCamelCase   :  List[str]						     =     summary_activation
          UpperCamelCase   :  Any						     =     summary_last_dropout
          UpperCamelCase   :  Any						     =     start_n_top
          UpperCamelCase   :  List[str]						     =     end_n_top

          UpperCamelCase   :  str						     =     bos_token_id
          UpperCamelCase   :  Optional[Any]						     =     pad_token_id
          UpperCamelCase   :  Optional[int]						     =     eos_token_id

          if "use_cache" in kwargs:
             warnings.warn(
                 'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                 ' instead.',      SCREAMING_SNAKE_CASE_,      )
             UpperCamelCase   :  Union[str, Any]						     =     kwargs['use_cache']

          UpperCamelCase   :  Tuple						     =     use_mems_eval
          UpperCamelCase   :  int						     =     use_mems_train
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,      bos_token_id=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       @property
       def  snake_case_	(      self	)					->		List[str]:
          logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
          return -1






       @max_position_embeddings.setter
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          # Message copied from Transformer-XL documentation
          raise NotImplementedError(
              F"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)








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



