





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


lowerCAmelCase_        =							logging.get_logger(__name__)



class    __lowerCAmelCase			:
			lowerCamelCase_	:	str
			lowerCamelCase_	:	str			     = None






			@staticmethod
			def      lowerCamelCase    ()	->  Any:


							'''simple docstring'''



							raise NotImplementedError






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Dict:


							'''simple docstring'''



							raise NotImplementedError






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							raise NotImplementedError






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


							'''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      lowerCamelCase    (cls       )	->  List[Any]:


							'''simple docstring'''



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




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Optional[int]			     = '''optuna'''






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


							'''simple docstring'''



							return is_optuna_available()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							return run_hp_search_optuna(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )






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


							'''simple docstring'''



							return default_hp_space_optuna(__magic_name__       )




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Any			     = '''ray'''
			lowerCamelCase_	:	List[str]			     = '''\'ray[tune]\''''






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


							'''simple docstring'''



							return is_ray_available()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return run_hp_search_ray(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )






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


							'''simple docstring'''



							return default_hp_space_ray(__magic_name__       )




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Tuple			     = '''sigopt'''






			@staticmethod
			def      lowerCamelCase    ()	->  Optional[int]:


							'''simple docstring'''



							return is_sigopt_available()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  List[str]:


							'''simple docstring'''



							return run_hp_search_sigopt(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  int:


							'''simple docstring'''



							return default_hp_space_sigopt(__magic_name__       )




class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	Tuple			     = '''wandb'''






			@staticmethod
			def      lowerCamelCase    ()	->  Dict:


							'''simple docstring'''



							return is_wandb_available()






			def      lowerCamelCase    (self   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return run_hp_search_wandb(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							return default_hp_space_wandb(__magic_name__       )




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






def 	lowerCamelCase_		(							)						->		str:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
				if len(_UpperCamelCase					) > 0:
								snake_case_		:       Dict           =    available_backends[0].name
								if len(_UpperCamelCase					) > 1:
												logger.info(
												    f'''{len(_UpperCamelCase					)} 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()					)					)





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


a_      :      Any    =     logging.get_logger(__name__)


a_      :      str    =     TypeVar('DatasetType', Dataset, IterableDataset)






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = "first_exhausted"							,  ):
 from .arrow_dataset import Dataset
 from .iterable_dataset import IterableDataset

 if not datasets:
  raise ValueError('''Unable to interleave an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
  raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."       )
 if dataset_type is Dataset:
  return _interleave_map_style_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )
 else:
  return _interleave_iterable_datasets(
      __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  stopping_strategy=__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None							,  __UpperCAmelCase = None							,  __UpperCAmelCase = 0							,  ):

 if not dsets:
  raise ValueError('''Unable to concatenate an empty list of datasets.'''       )
 for i, dataset in enumerate(__UpperCAmelCase       ):
  if not isinstance(__UpperCAmelCase							,  (Dataset, IterableDataset)       ):
   if isinstance(__UpperCAmelCase							,  (DatasetDict, IterableDatasetDict)       ):
    if not dataset:
     raise ValueError(
         f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
         '''is an empty dataset dictionary.'''       )
    raise ValueError(
        f"Dataset at position {i} has at least one split: {list(__UpperCAmelCase       )}\n"
        f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__UpperCAmelCase       )       )}']"       )
   raise ValueError(
       f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__UpperCAmelCase       ).__name__}."       )
  if i == 0:
   a__							,			a__												= (
       (Dataset, IterableDataset) if isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ) else (IterableDataset, Dataset)
   )
  elif not isinstance(__UpperCAmelCase							,  __UpperCAmelCase       ):
   raise ValueError(
       f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."       )
 if dataset_type is Dataset:
  return _concatenate_map_style_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )
 else:
  return _concatenate_iterable_datasets(__UpperCAmelCase							,  info=__UpperCAmelCase							,  split=__UpperCAmelCase							,  axis=__UpperCAmelCase       )





