A     :  Dict					     =       0  # The first color of the flag.
A     :  List[str]					     =       1  # The second color of the flag.
A     :  int					     =       2  # The third color of the flag.
A     :  Optional[Any]					     =       (red, white, blue)


def      a__ (		__UpperCamelCase				):
      if not sequence:
            return []
      if len(__UpperCamelCase				) == 1:
            return list(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  len(__UpperCamelCase				) - 1
      SCREAMING_SNAKE_CASE_          =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  SCREAMING_SNAKE_CASE_          =  F'''The elements inside the sequence must contains only {colors} values'''
                  raise ValueError(__UpperCamelCase				)
      return sequence


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      A     :  Tuple					     =       input("Enter numbers separated by commas:\n").strip()
      A     :  Tuple					     =       [int(item.strip()) for item in user_input.split(",")]
      print(f"{dutch_national_flag_sort(unsorted)}")



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A     :  Tuple					     =       r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"


@add_start_docstrings(SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''rag'''
  lowerCamelCase__     =						True







  def __init__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Any=" / "  ,     __magic_name__ :       int=" // "  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Dict=300  ,     __magic_name__ :       Optional[Any]=768  ,     __magic_name__ :       str=8  ,     __magic_name__ :       List[Any]="wiki_dpr"  ,     __magic_name__ :       Any="train"  ,     __magic_name__ :       Any="compressed"  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       Union[str, Any]=False  ,     __magic_name__ :       List[str]=0.0  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       str=False  ,     __magic_name__ :       int=False  ,     __magic_name__ :       Tuple=False  ,     __magic_name__ :       Tuple=True  ,     __magic_name__ :       Dict=None  ,     **__magic_name__ :       int  ,     )  ->	List[str]:
        super().__init__(
            bos_token_id=__magic_name__  ,     pad_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     decoder_start_token_id=__magic_name__  ,     forced_eos_token_id=__magic_name__  ,     is_encoder_decoder=__magic_name__  ,     prefix=__magic_name__  ,     vocab_size=__magic_name__  ,     **__magic_name__  ,     )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("question_encoder"   )
        SCREAMING_SNAKE_CASE_          =  question_encoder_config.pop("model_type"   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("generator"   )
        SCREAMING_SNAKE_CASE_          =  decoder_config.pop("model_type"   )

        from ..auto.configuration_auto import AutoConfig

        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  reduce_loss
        SCREAMING_SNAKE_CASE_          =  label_smoothing
        SCREAMING_SNAKE_CASE_          =  exclude_bos_score
        SCREAMING_SNAKE_CASE_          =  do_marginalize

        SCREAMING_SNAKE_CASE_          =  title_sep
        SCREAMING_SNAKE_CASE_          =  doc_sep
        SCREAMING_SNAKE_CASE_          =  n_docs
        SCREAMING_SNAKE_CASE_          =  max_combined_length

        SCREAMING_SNAKE_CASE_          =  dataset
        SCREAMING_SNAKE_CASE_          =  dataset_split
        SCREAMING_SNAKE_CASE_          =  index_name

        SCREAMING_SNAKE_CASE_          =  retrieval_vector_size
        SCREAMING_SNAKE_CASE_          =  retrieval_batch_size
        SCREAMING_SNAKE_CASE_          =  passages_path
        SCREAMING_SNAKE_CASE_          =  index_path
        SCREAMING_SNAKE_CASE_          =  use_dummy_dataset

        SCREAMING_SNAKE_CASE_          =  output_retrieved

        SCREAMING_SNAKE_CASE_          =  do_deduplication

        SCREAMING_SNAKE_CASE_          =  use_cache

        if self.forced_eos_token_id is None:
              SCREAMING_SNAKE_CASE_          =  getattr(self.generator  ,     "forced_eos_token_id"  ,     __magic_name__   )







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       PretrainedConfig  ,     __magic_name__ :       PretrainedConfig  ,     **__magic_name__ :       List[str]   )  ->	PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()  ,     generator=generator_config.to_dict()  ,     **__magic_name__   )







  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.question_encoder.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.generator.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output


