
'''simple docstring'''





import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
a__		: Optional[int]  							=							"\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
a__		: int  							=							"\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
a__		: Dict  							=							""

# TODO: Add the licence for the dataset here if you can find it
a__		: Dict  							=							""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
a__		: Optional[int]  							=							{
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}


class 			UpperCamelCase__	(     datasets.GeneratorBasedBuilder):
 UpperCAmelCase__     :     Tuple    					=       datasets.Version('1.1.0')

 # This is an example of a dataset with multiple configurations.
 # If you don't want/need to define several sub-sets in your dataset,
 # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

 # If you need to make complex sub-parts in the datasets with configurable options
 # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
 # BUILDER_CONFIG_CLASS = MyBuilderConfig

 # You will be able to load one or the other configurations in the following list with
 # data = datasets.load_dataset('my_dataset', 'first_domain')
 # data = datasets.load_dataset('my_dataset', 'second_domain')
 UpperCAmelCase__     :     Optional[int]    					=       [
     datasets.BuilderConfig(name='first_domain'   ,  version=SCREAMING_SNAKE_CASE   ,  description='This part of my dataset covers a first domain'),
     datasets.BuilderConfig(name='second_domain'   ,  version=SCREAMING_SNAKE_CASE   ,  description='This part of my dataset covers a second domain'),
 ]

 UpperCAmelCase__     :     Dict    					=       'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.






 def 						lowercase_    (      self	:str      )					->      Optional[Any]:







       '''simple docstring'''



       if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
             __A													=      datasets.Features(
                 {
                     'sentence': datasets.Value('string'      ),
                     'option1': datasets.Value('string'      ),
                     'answer': datasets.Value('string'      )
                     # These are the features of your dataset like images, labels ...
                 }      )
       else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
             __A													=      datasets.Features(
                 {
                     'sentence': datasets.Value('string'      ),
                     'option2': datasets.Value('string'      ),
                     'second_domain_answer': datasets.Value('string'      )
                     # These are the features of your dataset like images, labels ...
                 }      )
       return datasets.DatasetInfo(
           # This is the description that will appear on the datasets page.
           description=_DESCRIPTION						,					features=_A						,					homepage=_HOMEPAGE						,					license=_LICENSE						,					citation=_CITATION						,					)






 def 						lowercase_    (      self	:Optional[Any]						,					_A	:str      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      _URLS[self.config.name]
       __A													=      dl_manager.download_and_extract(_A      )
       return [
           datasets.SplitGenerator(
               name=datasets.Split.TRAIN						,					gen_kwargs={
                   'filepath': os.path.join(_A						,					'train.jsonl'      ),
                   'split': 'train',
               }						,					),
           datasets.SplitGenerator(
               name=datasets.Split.VALIDATION						,					gen_kwargs={
                   'filepath': os.path.join(_A						,					'dev.jsonl'      ),
                   'split': 'dev',
               }						,					),
           datasets.SplitGenerator(
               name=datasets.Split.TEST						,					gen_kwargs={
                   'filepath': os.path.join(_A						,					'test.jsonl'      ),
                   'split': 'test'
               }						,					),
       ]






 def 						lowercase_    (      self	:str						,					_A	:str						,					_A	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       with open(_A						,					encoding='utf-8'      ) as f:
             for key, row in enumerate(_A      ):
                   __A													=      json.loads(_A      )
                   if self.config.name == "first_domain":
                         # Yields examples as (key, example) tuples
                         yield key, {
                             "sentence": data["sentence"],
                             "option1": data["option1"],
                             "answer": "" if split == "test" else data["answer"],
                         }
                   else:
                         yield key, {
                             "sentence": data["sentence"],
                             "option2": data["option2"],
                             "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                         }








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'])



