






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




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = None   ,      __snake_case = None   ,      __snake_case = None   ,      __snake_case = None   ,      __snake_case = "first_exhausted"   ,      )				->						Union[str, Any]:
       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(__snake_case ):
              if not isinstance(__snake_case   ,      (Dataset, IterableDataset) ):
                     if isinstance(__snake_case   ,      (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(__snake_case )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__snake_case ) )}']""" )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__snake_case ).__name__}.""" )
              if i == 0:
                     _UpperCAmelCase							=    (
                         (Dataset, IterableDataset) if isinstance(__snake_case   ,      __snake_case ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(__snake_case   ,      __snake_case ):
                     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(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      info=__snake_case   ,      split=__snake_case   ,      stopping_strategy=__snake_case )
       else:
              return _interleave_iterable_datasets(
                  __snake_case   ,      __snake_case   ,      __snake_case   ,      info=__snake_case   ,      split=__snake_case   ,      stopping_strategy=__snake_case )





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case = None   ,      __snake_case = None   ,      __snake_case = 0   ,      )				->						Optional[Any]:

       if not dsets:
              raise ValueError("""Unable to concatenate an empty list of datasets.""" )
       for i, dataset in enumerate(__snake_case ):
              if not isinstance(__snake_case   ,      (Dataset, IterableDataset) ):
                     if isinstance(__snake_case   ,      (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(__snake_case )}\n"""
                                f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__snake_case ) )}']""" )
                     raise ValueError(
                         f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__snake_case ).__name__}.""" )
              if i == 0:
                     _UpperCAmelCase							=    (
                         (Dataset, IterableDataset) if isinstance(__snake_case   ,      __snake_case ) else (IterableDataset, Dataset)
                     )
              elif not isinstance(__snake_case   ,      __snake_case ):
                     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(__snake_case   ,      info=__snake_case   ,      split=__snake_case   ,      axis=__snake_case )
       else:
              return _concatenate_iterable_datasets(__snake_case   ,      info=__snake_case   ,      split=__snake_case   ,      axis=__snake_case )





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """xlm-roberta-xl"""

						def __init__(		self :Dict     ,			__snake_case :Optional[Any]=25_08_80     ,			__snake_case :List[Any]=25_60     ,			__snake_case :Optional[Any]=36     ,			__snake_case :Any=32     ,			__snake_case :int=1_02_40     ,			__snake_case :List[Any]="gelu"     ,			__snake_case :Union[str, Any]=0.1     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :str=5_14     ,			__snake_case :Union[str, Any]=1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :str=1E-05     ,			__snake_case :str=1     ,			__snake_case :int=0     ,			__snake_case :Tuple=2     ,			__snake_case :Optional[int]="absolute"     ,			__snake_case :str=True     ,			__snake_case :Any=None     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(pad_token_id=__snake_case     ,			bos_token_id=__snake_case     ,			eos_token_id=__snake_case     ,			**__snake_case			)
										__magic_name__						:						List[str]										=vocab_size
										__magic_name__						:						List[str]										=hidden_size
										__magic_name__						:						Union[str, Any]										=num_hidden_layers
										__magic_name__						:						Any										=num_attention_heads
										__magic_name__						:						Any										=hidden_act
										__magic_name__						:						List[str]										=intermediate_size
										__magic_name__						:						Any										=hidden_dropout_prob
										__magic_name__						:						Union[str, Any]										=attention_probs_dropout_prob
										__magic_name__						:						Any										=max_position_embeddings
										__magic_name__						:						Any										=type_vocab_size
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						Optional[int]										=layer_norm_eps
										__magic_name__						:						Dict										=position_embedding_type
										__magic_name__						:						Any										=use_cache
										__magic_name__						:						Dict										=classifier_dropout




class 		__A   (      UpperCamelCase__      ):

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

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




