
from typing import Any


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):
      _validation(
          UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		)
      # Creates data structures and fill initial step
      UpperCamelCase__    :   dict					=				{}
      UpperCamelCase__    :   dict					=				{}
      for state in states_space:
            UpperCamelCase__    :   Dict					=				observations_space[0]
            UpperCamelCase__    :   List[str]					=				(
                initial_probabilities[state] * emission_probabilities[state][observation]
            )
            UpperCamelCase__    :   Union[str, Any]					=				None

      # Fills the data structure with the probabilities of
      # different transitions and pointers to previous states
      for o in range(1 ,		len(UpperCamelCase__		)		):
            UpperCamelCase__    :   Dict					=				observations_space[o]
            UpperCamelCase__    :   Optional[int]					=				observations_space[o - 1]
            for state in states_space:
                  # Calculates the argmax for probability function
                  UpperCamelCase__    :   str					=				''''''
                  UpperCamelCase__    :   List[Any]					=				-1
                  for k_state in states_space:
                        UpperCamelCase__    :   Optional[int]					=				(
                            probabilities[(k_state, prior_observation)]
                            * transition_probabilities[k_state][state]
                            * emission_probabilities[state][observation]
                        )
                        if probability > max_probability:
                              UpperCamelCase__    :   Dict					=				probability
                              UpperCamelCase__    :   List[Any]					=				k_state

            # Update probabilities and pointers dicts
                  UpperCamelCase__    :   int					=				(
                      probabilities[(arg_max, prior_observation)]
                      * transition_probabilities[arg_max][state]
                      * emission_probabilities[state][observation]
                  )

                  UpperCamelCase__    :   Any					=				arg_max

    # The final observation
      UpperCamelCase__    :   Optional[Any]					=				observations_space[len(UpperCamelCase__		) - 1]

      # argmax for given final observation
      UpperCamelCase__    :   Union[str, Any]					=				''''''
      UpperCamelCase__    :   str					=				-1
      for k_state in states_space:
            UpperCamelCase__    :   Tuple					=				probabilities[(k_state, final_observation)]
            if probability > max_probability:
                  UpperCamelCase__    :   Union[str, Any]					=				probability
                  UpperCamelCase__    :   Optional[Any]					=				k_state
      UpperCamelCase__    :   List[str]					=				arg_max

      # Process pointers backwards
      UpperCamelCase__    :   str					=				last_state
      UpperCamelCase__    :   List[str]					=				[]
      for o in range(len(UpperCamelCase__		) - 1 ,		-1 ,		-1		):
            result.append(UpperCamelCase__		)
            UpperCamelCase__    :   List[str]					=				pointers[previous, observations_space[o]]
      result.reverse()

      return result


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):
      _validate_not_empty(
          UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		)
      _validate_lists(UpperCamelCase__ ,		UpperCamelCase__		)
      _validate_dicts(
          UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):
      if not all(
          [
              observations_space,
              states_space,
              initial_probabilities,
              transition_probabilities,
              emission_probabilities,
          ]		):
            raise ValueError('''There\'s an empty parameter'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      _validate_list(UpperCamelCase__ ,		'''observations_space'''		)
      _validate_list(UpperCamelCase__ ,		'''states_space'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      if not isinstance(_object ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[str]					=				f'''{var_name} must be a list'''
            raise ValueError(UpperCamelCase__		)
      else:
            for x in _object:
                  if not isinstance(UpperCamelCase__ ,		UpperCamelCase__		):
                        UpperCamelCase__    :   Optional[Any]					=				f'''{var_name} must be a list of strings'''
                        raise ValueError(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		):
      _validate_dict(UpperCamelCase__ ,		'''initial_probabilities''' ,		UpperCamelCase__		)
      _validate_nested_dict(UpperCamelCase__ ,		'''transition_probabilities'''		)
      _validate_nested_dict(UpperCamelCase__ ,		'''emission_probabilities'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      _validate_dict(_object ,		UpperCamelCase__ ,		UpperCamelCase__		)
      for x in _object.values():
            _validate_dict(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = False		):
      if not isinstance(_object ,		UpperCamelCase__		):
            UpperCamelCase__    :   List[str]					=				f'''{var_name} must be a dict'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__ ,		UpperCamelCase__		) for x in _object		):
            UpperCamelCase__    :   Tuple					=				f'''{var_name} all keys must be strings'''
            raise ValueError(UpperCamelCase__		)
      if not all(isinstance(UpperCamelCase__ ,		UpperCamelCase__		) for x in _object.values()		):
            UpperCamelCase__    :   Optional[Any]					=				'''nested dictionary ''' if nested else ''''''
            UpperCamelCase__    :   Optional[Any]					=				f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
            raise ValueError(UpperCamelCase__		)


if __name__ == "__main__":
    from doctest import testmod

    testmod()




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)


