





import copy

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


lowerCAmelCase_						   =    r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

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



@add_start_docstrings(UpperCAmelCase_							)
class        _lowerCAmelCase    (			UpperCAmelCase_							):


		'''simple docstring'''
		a_							:       Tuple      					="""rag"""
		a_							:       str      					=True







		def __init__(      self      :					Union[str, Any]      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					List[str]=None      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Tuple=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					int=" / "      ,				UpperCamelCase      :					List[str]=" // "      ,				UpperCamelCase      :					Any=5      ,				UpperCamelCase      :					Optional[int]=3_00      ,				UpperCamelCase      :					Dict=7_68      ,				UpperCamelCase      :					List[Any]=8      ,				UpperCamelCase      :					Tuple="wiki_dpr"      ,				UpperCamelCase      :					List[Any]="train"      ,				UpperCamelCase      :					Optional[int]="compressed"      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Union[str, Any]=False      ,				UpperCamelCase      :					Tuple=False      ,				UpperCamelCase      :					Tuple=0.0      ,				UpperCamelCase      :					str=True      ,				UpperCamelCase      :					str=False      ,				UpperCamelCase      :					int=False      ,				UpperCamelCase      :					Union[str, Any]=False      ,				UpperCamelCase      :					Union[str, Any]=True      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					str      ,				):


				'''simple docstring'''





				super().__init__(
				    bos_token_id=UpperCamelCase      ,				pad_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				decoder_start_token_id=UpperCamelCase      ,				forced_eos_token_id=UpperCamelCase      ,				is_encoder_decoder=UpperCamelCase      ,				prefix=UpperCamelCase      ,				vocab_size=UpperCamelCase      ,				**UpperCamelCase      ,				)
				assert (
				    "question_encoder" in kwargs and "generator" in kwargs
				), "Config has to be initialized with question_encoder and generator config"
				_snake_case   :				Tuple														=	kwargs.pop('question_encoder'							)
				_snake_case   :				int														=	question_encoder_config.pop('model_type'							)
				_snake_case   :				Dict														=	kwargs.pop('generator'							)
				_snake_case   :				Optional[Any]														=	decoder_config.pop('model_type'							)

				from ..auto.configuration_auto import AutoConfig

				_snake_case   :				Union[str, Any]														=	AutoConfig.for_model(UpperCamelCase      ,				**UpperCamelCase							)
				_snake_case   :				Dict														=	AutoConfig.for_model(UpperCamelCase      ,				**UpperCamelCase							)

				_snake_case   :				Tuple														=	reduce_loss
				_snake_case   :				Optional[Any]														=	label_smoothing
				_snake_case   :				List[str]														=	exclude_bos_score
				_snake_case   :				int														=	do_marginalize

				_snake_case   :				Any														=	title_sep
				_snake_case   :				Optional[Any]														=	doc_sep
				_snake_case   :				Union[str, Any]														=	n_docs
				_snake_case   :				Dict														=	max_combined_length

				_snake_case   :				List[str]														=	dataset
				_snake_case   :				Union[str, Any]														=	dataset_split
				_snake_case   :				Dict														=	index_name

				_snake_case   :				Any														=	retrieval_vector_size
				_snake_case   :				List[str]														=	retrieval_batch_size
				_snake_case   :				Dict														=	passages_path
				_snake_case   :				List[Any]														=	index_path
				_snake_case   :				Tuple														=	use_dummy_dataset

				_snake_case   :				int														=	output_retrieved

				_snake_case   :				str														=	do_deduplication

				_snake_case   :				Union[str, Any]														=	use_cache

				if self.forced_eos_token_id is None:
						_snake_case   :				Dict														=	getattr(self.generator      ,				'forced_eos_token_id'      ,				UpperCamelCase							)







		@classmethod
		def UpperCamelCase_ (      cls      :					Any      ,				UpperCamelCase      :					PretrainedConfig      ,				UpperCamelCase      :					PretrainedConfig      ,				**UpperCamelCase      :					Optional[int]							):


				'''simple docstring'''





				return cls(question_encoder=question_encoder_config.to_dict()      ,				generator=generator_config.to_dict()      ,				**UpperCamelCase							)







		def UpperCamelCase_ (      self      :					Optional[int]							):


				'''simple docstring'''





				_snake_case   :				List[Any]														=	copy.deepcopy(self.__dict__							)
				_snake_case   :				Optional[int]														=	self.question_encoder.to_dict()
				_snake_case   :				int														=	self.generator.to_dict()
				_snake_case   :				str														=	self.__class__.model_type
				return output











# Function to print upper half of diamond (pyramid)


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		List[str]:
  for i in range(0    ,							lowerCAmelCase				):
    for _ in range(0    ,							n - i - 1				):  # printing spaces
      print(' '    ,							end=''				)
    for _ in range(0    ,							i + 1				):  # printing stars
      print('* '    ,							end=''				)
    print()


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[int]				)->		List[Any]:
  for i in range(lowerCAmelCase    ,							0    ,							-1				):
    for _ in range(lowerCAmelCase    ,							0    ,							-1				):  # printing stars
      print('* '    ,							end=''				)
    print()
    for _ in range(n - i + 1    ,							0    ,							-1				):  # printing spaces
      print(' '    ,							end=''				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		int:
  if n <= 0:
    print('       ...       ....        nothing printing :('				)
    return
  floyd(lowerCAmelCase				)  # upper half
  reverse_floyd(lowerCAmelCase				)  # lower half


if __name__ == "__main__":
      print(r"""| /\ | |- |  |-  |--| |\  /| |-""")
      print(r"""|/  \| |- |_ |_  |__| | \/ | |_""")
      lowerCAmelCase_						   =    1
      while K:
            lowerCAmelCase_						   =    int(input("""enter the number and , and see the magic : """))
            print()
            pretty_print(user_number)
            lowerCAmelCase_						   =    int(input("""press 0 to exit... and 1 to continue..."""))

      print("""Good Bye...""")




