


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


_SCREAMING_SNAKE_CASE      :			List[str]      				=   TypeVar("DatasetType", Dataset, IterableDataset)
def       UpperCAmelCase__						(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:
               snake_case			,  snake_case					=    (
                   (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       UpperCAmelCase__						(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:
               snake_case			,  snake_case					=    (
                   (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_    )








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE      :			Dict      				=   {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE      :			Dict      				=   [
     "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "VanForImageClassification",
     "VanModel",
     "VanPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_van import (
      VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      VanForImageClassification,
      VanModel,
      VanPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE      :			str      				=   _LazyModule(__name__, globals()["__file__"], _import_structure)




