"""simple docstring"""







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


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE									=						TypeVar('DatasetType', Dataset, IterableDataset)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = "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(__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:
			_lowercase				,      _lowercase :    List[str]							=							(
			    (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      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase = None     ,__UpperCAmelCase = None     ,__UpperCAmelCase = 0     ,):






	"""simple docstring"""





	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:
			_lowercase				,      _lowercase :    List[Any]							=							(
			    (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			)




"""simple docstring"""







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



