
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml




class 				_lowerCamelCase (      yaml.SafeLoader ):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__    :   int					=				[self.constructed_objects[key_node] for key_node, _ in node.value]
           UpperCamelCase__    :   int					=				[tuple(__SCREAMING_SNAKE_CASE  ) if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ) else key for key in keys]
           UpperCamelCase__    :   str					=				Counter(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   List[str]					=				[key for key in counter if counter[key] > 1]
           if duplicate_keys:
                 raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=False  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   str					=				super().construct_mapping(__SCREAMING_SNAKE_CASE						,						deep=__SCREAMING_SNAKE_CASE  )
           self._check_no_duplicates_on_constructed_node(__SCREAMING_SNAKE_CASE  )
           return mapping


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   int					=				list(readme_content.splitlines()		)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCamelCase__    :   Union[str, Any]					=				full_content[1:].index('''---'''		) + 1
            UpperCamelCase__    :   int					=				'''\n'''.join(full_content[1:sep_idx]		)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]		)

      return None, "\n".join(__A		)




class 				_lowerCamelCase (      __A ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls						,						__SCREAMING_SNAKE_CASE  )  ->   "DatasetMetadata":




           """simple docstring"""







           with open(__SCREAMING_SNAKE_CASE						,						encoding='''utf-8'''  ) as readme_file:
                 UpperCamelCase__    :   List[str]					=				_split_yaml_from_readme(readme_file.read()  )
           if yaml_string is not None:
                 return cls.from_yaml_string(__SCREAMING_SNAKE_CASE  )
           else:
                 return cls()






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           if path.exists():
                 with open(__SCREAMING_SNAKE_CASE						,						encoding='''utf-8'''  ) as readme_file:
                       UpperCamelCase__    :   Tuple					=				readme_file.read()
           else:
                 UpperCamelCase__    :   str					=				None
           UpperCamelCase__    :   Dict					=				self._to_readme(__SCREAMING_SNAKE_CASE  )
           with open(__SCREAMING_SNAKE_CASE						,						'''w'''						,						encoding='''utf-8'''  ) as readme_file:
                 readme_file.write(__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE = None  )  ->   str:




           """simple docstring"""







           if readme_content is not None:
                 UpperCamelCase__    :   List[Any]					=				_split_yaml_from_readme(__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Optional[Any]					=				'''---\n''' + self.to_yaml_string() + '''---\n''' + content
           else:
                 UpperCamelCase__    :   Tuple					=				'''---\n''' + self.to_yaml_string() + '''---\n'''
           return full_content






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls						,						__SCREAMING_SNAKE_CASE  )  ->   "DatasetMetadata":




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				yaml.load(__SCREAMING_SNAKE_CASE						,						Loader=_NoDuplicateSafeLoader  ) or {}

           # Convert the YAML keys to DatasetMetadata fields
           UpperCamelCase__    :   Tuple					=				{
               (key.replace('''-'''						,						'''_'''  ) if key.replace('''-'''						,						'''_'''  ) in cls._FIELDS_WITH_DASHES else key): value
               for key, value in metadata_dict.items()
           }
           return cls(**__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           return yaml.safe_dump(
               {
                   (key.replace('''_'''						,						'''-'''  ) if key in self._FIELDS_WITH_DASHES else key): value
                   for key, value in self.items()
               }						,						sort_keys=__SCREAMING_SNAKE_CASE						,						allow_unicode=__SCREAMING_SNAKE_CASE						,						encoding='''utf-8'''						,						).decode('''utf-8'''  )


lowerCamelCase      ={
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
    from argparse import ArgumentParser

    lowerCamelCase      =ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
    ap.add_argument("readme_filepath")
    lowerCamelCase      =ap.parse_args()

    lowerCamelCase      =Path(args.readme_filepath)
    lowerCamelCase      =DatasetMetadata.from_readme(readme_filepath)
    print(dataset_metadata)
    dataset_metadata.to_readme(readme_filepath)




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


