


from typing import Dict

from .base import GenericTensor, Pipeline
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Optional[int]=None     ,		_a:Union[str, Any]=None     ,		_a:int=None     ,		**_a:Optional[int]   ):







       if tokenize_kwargs is None:
         snake_case__						= {}

       if truncation is not None:
         if "truncation" in tokenize_kwargs:
           raise ValueError(
               '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''   )
         snake_case__						= truncation

       snake_case__						= tokenize_kwargs

       snake_case__						= {}
       if return_tensors is not None:
         snake_case__						= return_tensors

       return preprocess_params, {}, postprocess_params

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:Optional[Any]     ,		**_a:Optional[int]   ):
       snake_case__						= self.framework
       snake_case__						= self.tokenizer(_a     ,		return_tensors=_a     ,		**_a   )
       return model_inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Dict   ):
       snake_case__						= self.model(**_a   )
       return model_outputs

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:int     ,		_a:Optional[int]=False   ):







       if return_tensors:
         return model_outputs[0]
       if self.framework == "pt":
         return model_outputs[0].tolist()
       elif self.framework == "tf":
         return model_outputs[0].numpy().tolist()







     def __call__(					self:int     ,		*_a:Optional[int]     ,		**_a:int   ):
       return super().__call__(*_a     ,		**_a   )







'''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    )


