






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_		:		Optional[Any]		     =			TypeVar('DatasetType', Dataset, IterableDataset)

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "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(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:
      UpperCamelCase_				,UpperCamelCase_: 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 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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = 0     ,   )	->    DatasetType:

  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:
      UpperCamelCase_				,UpperCamelCase_: Union[str, 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__  )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

A_		:		Optional[Any]		     =			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         if self.task == "multiple-choice":
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)