




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


__lowerCAmelCase	:       List[Any]     =	logging.get_logger(__name__)


__lowerCAmelCase	:       List[Any]     =	TypeVar('DatasetType', Dataset, IterableDataset)




def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[List[float]] = None,						A	:      Optional[int] = None,						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      Literal["first_exhausted", "all_exhausted"] = "first_exhausted",						):






			'''simple docstring'''







			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(A     ):
						if not isinstance(A,						(Dataset, IterableDataset)     ):
									if isinstance(A,						(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(A     )}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(A     )     )}']"""     )
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A     ).__name__}."""     )
						if i == 0:
									a					,	a					=    (
									    (Dataset, IterableDataset) if isinstance(A,						A     ) else (IterableDataset, Dataset)
									)
						elif not isinstance(A,						A     ):
									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(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )
			else:
						return _interleave_iterable_datasets(
						    A,						A,						A,						info=A,						split=A,						stopping_strategy=A     )






def     __magic_name__       (				A	:      List[DatasetType],						A	:      Optional[DatasetInfo] = None,						A	:      Optional[NamedSplit] = None,						A	:      int = 0,						):






			'''simple docstring'''







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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCAmelCase	:       Union[str, Any]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






