





from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


lowercase      :       Tuple									=  logging.get_logger(__name__)





class  A__    :




   """simple docstring"""



   __A   :				str
   __A   :				str         =  None

   @staticmethod
   def 							__lowercase		(					)			->						Union[str, Any]:







          '''simple docstring'''

          raise NotImplementedError

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						Optional[int]:







          '''simple docstring'''

          raise NotImplementedError

   def 							__lowercase		(					self					,  lowercase)			->						str:







          '''simple docstring'''

          raise NotImplementedError

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          if not self.is_available():
                 raise RuntimeError(
                     F'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.')

   @classmethod
   def 							__lowercase		(					cls)			->						int:







          '''simple docstring'''

          return F'`pip install {cls.pip_package or cls.name}`'





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Dict         =  '''optuna'''

   @staticmethod
   def 							__lowercase		(					)			->						Optional[Any]:







          '''simple docstring'''

          return is_optuna_available()

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						List[str]:







          '''simple docstring'''

          return run_hp_search_optuna(lowercase					,  lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          return default_hp_space_optuna(lowercase)





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Any         =  '''ray'''
   __A   :				Optional[int]         =  '''\'ray[tune]\''''

   @staticmethod
   def 							__lowercase		(					)			->						List[Any]:







          '''simple docstring'''

          return is_ray_available()

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						Any:







          '''simple docstring'''

          return run_hp_search_ray(lowercase					,  lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          return default_hp_space_ray(lowercase)





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  '''sigopt'''

   @staticmethod
   def 							__lowercase		(					)			->						List[Any]:







          '''simple docstring'''

          return is_sigopt_available()

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          return run_hp_search_sigopt(lowercase					,  lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          return default_hp_space_sigopt(lowercase)





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  '''wandb'''

   @staticmethod
   def 							__lowercase		(					)			->						Union[str, Any]:







          '''simple docstring'''

          return is_wandb_available()

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          return run_hp_search_wandb(lowercase					,  lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self					,  lowercase)			->						Tuple:







          '''simple docstring'''

          return default_hp_space_wandb(lowercase)

lowercase      :       List[str]									=  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}



def A_	(		)  ->		str:
       a__						:	Union[str, Any] =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
       if len(A__				) > 0:
              a__						:	Any =				available_backends[0].name
              if len(A__				) > 1:
                     logger.info(
                         F'{len(A__				)} hyperparameter search backends available. Using {name} as the default.'				)
              return name
       raise RuntimeError(
           'No hyperparameter search backend available.\n'
           + '\n'.join(
               F' - To install {backend.name} run {backend.pip_install()}'
               for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()				)				)









'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
