


"""simple docstring"""







from dataclasses import dataclass, field
from typing import Optional






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Model name or path of model to be trained."}			)
 __lowerCAmelCase 					=  field(
     default="./",    metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot-clean-train",    metadata={"help": "Name or path of training dataset."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot-clean-valid",    metadata={"help": "Name or path of validation dataset."}			)
 __lowerCAmelCase 					=  field(default=2,    metadata={"help": "Batch size for training."}			)
 __lowerCAmelCase 					=  field(default=2,    metadata={"help": "Batch size for evaluation."}			)
 __lowerCAmelCase 					=  field(default=0.1,    metadata={"help": "Value of weight decay."}			)
 __lowerCAmelCase 					=  field(
     default=10000,    metadata={"help": "Size of buffer used to shuffle streaming dataset."}			)
 __lowerCAmelCase 					=  field(default=2e-4,    metadata={"help": "Learning rate fo training."}			)
 __lowerCAmelCase 					=  field(default="cosine",    metadata={"help": "Learning rate."}			)
 __lowerCAmelCase 					=  field(
     default=750,    metadata={"help": "Number of warmup steps in the learning rate schedule."}			)
 __lowerCAmelCase 					=  field(
     default=16,    metadata={"help": "Number of gradient accumulation steps."}			)
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Use gradient checkpointing to reduce memory footprint."}			)
 __lowerCAmelCase 					=  field(default=50000,    metadata={"help": "Maximum number of training steps."}			)
 __lowerCAmelCase 					=  field(
     default=-1,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}			)
 __lowerCAmelCase 					=  field(default=1024,    metadata={"help": "Sequence lengths used for training."}			)
 __lowerCAmelCase 					=  field(default=1,    metadata={"help": "Training seed."}			)
 __lowerCAmelCase 					=  field(
     default=1024,    metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."},    )
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "States path if the training should continue from a checkpoint folder."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "If True the data is pretokenized."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Model name or path of model to be evaluated."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot-clean-valid",    metadata={"help": "Name or path of validation dataset."}			)
 __lowerCAmelCase 					=  field(default=2,    metadata={"help": "Batch size used for evaluation."}			)
 __lowerCAmelCase 					=  field(
     default=-1,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}			)
 __lowerCAmelCase 					=  field(default=1024,    metadata={"help": "Length of sequences to be evaluated."}			)
 __lowerCAmelCase 					=  field(default=1,    metadata={"help": "Random seed used for evaluation."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Model name or path of model to be evaluated."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Number of workers used for code evaluation."}			)
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."},    )
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Sample from the language model's output distribution."}			)
 __lowerCAmelCase 					=  field(default=0.2,    metadata={"help": "Sampling temperature used for generation."}			)
 __lowerCAmelCase 					=  field(default=256,    metadata={"help": "Maximum number of newly generated tokens."}			)
 __lowerCAmelCase 					=  field(default=0,    metadata={"help": "Top-k parameter used for generation."}			)
 __lowerCAmelCase 					=  field(default=0.9_5,    metadata={"help": "Top-p parameter used for nucleus sampling."}			)
 __lowerCAmelCase 					=  field(default=10,    metadata={"help": "Number of generations to run in parallel."}			)
 __lowerCAmelCase 					=  field(
     default=200,    metadata={"help": "Number of completions to generate for each sample."}			)
 __lowerCAmelCase 					=  field(default=1,    metadata={"help": "Random seed used for evaluation."}			)
 __lowerCAmelCase 					=  field(
     default="eval_results.json",    metadata={"help": "Random seed used for evaluation."}			)
 __lowerCAmelCase 					=  field(
     default="0",    metadata={"help": "Allow `code_eval` to execute Python code on machine"}			)
 __lowerCAmelCase 					=  field(
     default=-1,    metadata={
         "help": (
             "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
             " number corresponds to which GPU device id to run on."
         )
     },    )






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={
         "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
     },    )
 __lowerCAmelCase 					=  field(
     default="transformersbook/codeparrot",    metadata={"help": "Folder or name of dataset to process."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot-clean",    metadata={"help": "Folder to save processed processed dataset."}			)
 __lowerCAmelCase 					=  field(
     default=100000,    metadata={"help": "Number of files to save per JSON output file."}			)
 __lowerCAmelCase 					=  field(default="content",    metadata={"help": "Column containing text data to process."}			)
 __lowerCAmelCase 					=  field(
     default=1000,    metadata={"help": "Maximum line length in file, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=100,    metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=0.2_5,    metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=1.5,    metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}			)
 __lowerCAmelCase 					=  field(
     default=0.7,    metadata={"help": "Probability for filtering config, test and uncommon files."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Name or path to the tokenizer."},    )
 __lowerCAmelCase 					=  field(
     default=_SCREAMING_SNAKE_CASE,    metadata={"help": "If True, near-duplicate samples are removed."}			)
 __lowerCAmelCase 					=  field(
     default=0.8_5,    metadata={"help": "Jaccard threshold for near-duplicate samples."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="gpt2",    metadata={"help": "Base tokenizer to build new tokenizer from."}			)
 __lowerCAmelCase 					=  field(
     default="transformersbook/codeparrot-train",    metadata={"help": "Dataset to train tokenizer on."}			)
 __lowerCAmelCase 					=  field(default="content",    metadata={"help": "Column containing text data to process."}			)
 __lowerCAmelCase 					=  field(default=200000,    metadata={"help": "Number of examples to train tokenizer on."}			)
 __lowerCAmelCase 					=  field(
     default=32768,    metadata={"help": "Number of examples to train the tokenizer on."}			)
 __lowerCAmelCase 					=  field(default="codeparrot",    metadata={"help": "Name of new tokenizer."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Push saved tokenizer to the hub."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Name or path to the tokenizer."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot-clean-train",    metadata={"help": "Name or path to the dataset to pretokenize."}			)
 __lowerCAmelCase 					=  field(
     default="tokenized-codeparrot-train",    metadata={"help": "Repo name of the pretokenized data."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Number of workers used for code evaluation."}			)






@dataclass
class       __A				:






 """simple docstring"""




 __lowerCAmelCase 					=  field(
     default="gpt2-large",    metadata={"help": "Configuration to use for model initialization."}			)
 __lowerCAmelCase 					=  field(
     default="codeparrot/codeparrot",    metadata={"help": "Tokenizer attached to model."}			)
 __lowerCAmelCase 					=  field(default="codeparrot",    metadata={"help": "Name of the created model."}			)
 __lowerCAmelCase 					=  field(default=_SCREAMING_SNAKE_CASE,    metadata={"help": "Push saved tokenizer to the hub."}			)



'''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__    					=      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)
