






'''simple docstring'''



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      :       List[Any]      			=      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      :       Union[str, Any]      			=      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      :       str      			=      rh.cluster(
      name="""rh-cluster""", ips=[args.host], ssh_creds={"""ssh_user""": args.user, """ssh_private_key""": args.key_path}
  )
 else:
  __UpperCamelCase      :       List[str]      			=      rh.cluster(
      name="""rh-cluster""", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
  )
 __UpperCamelCase      :       Union[str, Any]      			=      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)








'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Optional[int]      			=      {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="xmod"




      def __init__(  self ,   lowerCamelCase=3_0522 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=512 ,   lowerCamelCase=2 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-12 ,   lowerCamelCase=1 ,   lowerCamelCase=0 ,   lowerCamelCase=2 ,   lowerCamelCase="absolute" ,   lowerCamelCase=True ,   lowerCamelCase=None ,   lowerCamelCase=False ,   lowerCamelCase=2 ,   lowerCamelCase=False ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=("en_XX",) ,   lowerCamelCase=None ,   **lowerCamelCase ,   )							->Any:



         '''simple docstring'''
         super().__init__(pad_token_id=lowerCamelCase ,   bos_token_id=lowerCamelCase ,   eos_token_id=lowerCamelCase ,   **lowerCamelCase      )

         __a				     =    vocab_size
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    hidden_act
         __a				     =    intermediate_size
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    max_position_embeddings
         __a				     =    type_vocab_size
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    position_embedding_type
         __a				     =    use_cache
         __a				     =    classifier_dropout
         __a				     =    pre_norm
         __a				     =    adapter_reduction_factor
         __a				     =    adapter_layer_norm
         __a				     =    adapter_reuse_layer_norm
         __a				     =    ln_before_adapter
         __a				     =    list(lowerCamelCase      )
         __a				     =    default_language







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @property
      def  __UpperCamelCase							(  self      )							->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         if self.task == "multiple-choice":
            __a				     =    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
            __a				     =    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
             ]      )