


'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :List[str]             =     logging.get_logger(__name__)

lowerCAmelCase :int             =     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class _lowerCamelCase			(		_lowercase				):



		'''simple docstring'''







		A_      :     Optional[int]        =							'''luke'''







		def __init__(   self						:		List[str]    ,	_A						:		List[Any]=50267    ,	_A						:		Union[str, Any]=500000    ,	_A						:		Dict=768    ,	_A						:		int=256    ,	_A						:		Union[str, Any]=12    ,	_A						:		Union[str, Any]=12    ,	_A						:		Union[str, Any]=3072    ,	_A						:		Union[str, Any]="gelu"    ,	_A						:		Optional[int]=0.1    ,	_A						:		Optional[Any]=0.1    ,	_A						:		Dict=512    ,	_A						:		Union[str, Any]=2    ,	_A						:		Union[str, Any]=0.02    ,	_A						:		Optional[int]=1E-12    ,	_A						:		Dict=True    ,	_A						:		Tuple=None    ,	_A						:		Optional[int]=1    ,	_A						:		Dict=0    ,	_A						:		str=2    ,	**_A						:		int    ,	)    ->					List[Any]:
			super().__init__(pad_token_id=_A    ,	bos_token_id=_A    ,	eos_token_id=_A    ,	**_A     )

			__magic_name__			:       str	      =	vocab_size
			__magic_name__			:       Optional[Any]	      =	entity_vocab_size
			__magic_name__			:       Union[str, Any]	      =	hidden_size
			__magic_name__			:       Dict	      =	entity_emb_size
			__magic_name__			:       List[Any]	      =	num_hidden_layers
			__magic_name__			:       Dict	      =	num_attention_heads
			__magic_name__			:       Any	      =	hidden_act
			__magic_name__			:       Any	      =	intermediate_size
			__magic_name__			:       str	      =	hidden_dropout_prob
			__magic_name__			:       Union[str, Any]	      =	attention_probs_dropout_prob
			__magic_name__			:       Dict	      =	max_position_embeddings
			__magic_name__			:       str	      =	type_vocab_size
			__magic_name__			:       Union[str, Any]	      =	initializer_range
			__magic_name__			:       List[Any]	      =	layer_norm_eps
			__magic_name__			:       str	      =	use_entity_aware_attention
			__magic_name__			:       str	      =	classifier_dropout




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       )





