
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


UpperCAmelCase__		:     Optional[int]     			=      logging.get_logger(__name__)


UpperCAmelCase__		:     Dict     			=      TypeVar('DatasetType', Dataset, IterableDataset)






def   lowerCamelCase__							(					a		,		a = None		,		a = None		,		a = None		,		a = None		,		a = "first_exhausted"		,		)      ->   DatasetType:
   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:  List[str]					=       (
             (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   lowerCamelCase__							(					a		,		a = None		,		a = None		,		a = 0		,		)      ->   DatasetType:

   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:  List[str]					=       (
             (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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



