


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase			=				logging.get_logger(__name__)

lowerCAmelCase			=				{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     List[str]     =					'''roformer'''

							def __init__(				self:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]=50_000       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      int=768       ,		UpperCamelCase_:      Any=12       ,		UpperCamelCase_:      Optional[int]=12       ,		UpperCamelCase_:      Optional[Any]=3_072       ,		UpperCamelCase_:      Optional[int]="gelu"       ,		UpperCamelCase_:      Union[str, Any]=0.1       ,		UpperCamelCase_:      Optional[int]=0.1       ,		UpperCamelCase_:      Tuple=1_536       ,		UpperCamelCase_:      Any=2       ,		UpperCamelCase_:      Dict=0.02       ,		UpperCamelCase_:      Any=1E-1_2       ,		UpperCamelCase_:      Optional[Any]=0       ,		UpperCamelCase_:      str=False       ,		UpperCamelCase_:      Optional[int]=True       ,		**UpperCamelCase_:      Dict       ,		)     ->  List[str]:





									"""simple docstring"""







									super().__init__(pad_token_id=UpperCamelCase_       ,		**UpperCamelCase_				)

									lowercase__				= vocab_size
									lowercase__				= hidden_size if embedding_size is None else embedding_size
									lowercase__				= hidden_size
									lowercase__				= num_hidden_layers
									lowercase__				= num_attention_heads
									lowercase__				= hidden_act
									lowercase__				= intermediate_size
									lowercase__				= hidden_dropout_prob
									lowercase__				= attention_probs_dropout_prob
									lowercase__				= max_position_embeddings
									lowercase__				= type_vocab_size
									lowercase__				= initializer_range
									lowercase__				= layer_norm_eps
									lowercase__				= rotary_value
									lowercase__				= use_cache


class 			_a				(     UpperCamelCase__     ):

							@property
							def  lowerCamelCase_					(				self:      str				)     ->  Mapping[str, Mapping[int, str]]:





									"""simple docstring"""







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








'''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


UpperCamelCase_    	=				logging.get_logger(__name__)


UpperCamelCase_    	=				TypeVar("DatasetType", Dataset, IterableDataset)




def   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[List[float]] = None      ,__UpperCamelCase:					Optional[int] = None      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					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(__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:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[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   lowercase__(			__UpperCamelCase:					List[DatasetType]      ,__UpperCamelCase:					Optional[DatasetInfo] = None      ,__UpperCamelCase:					Optional[NamedSplit] = None      ,__UpperCamelCase:					int = 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:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						(
                      (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    )


