



from typing import Any



class 				__snake_case						:




							def __init__(       self     :       Optional[int]				,				A_     :       Any):
									lowerCAmelCase_			:     int									= data
									lowerCAmelCase_			:     Any									= None







							def __repr__(       self     :       Any):
									return F"""Node({self.data})"""





class 				__snake_case						:




							def __init__(       self     :       List[str]):
									lowerCAmelCase_			:     Optional[int]									= None




							def __iter__(       self     :       Optional[int]):
									lowerCAmelCase_			:     List[Any]									= self.head
									while node:
											yield node.data
											lowerCAmelCase_			:     int									= node.next




							def __len__(       self     :       List[Any]):
									return sum(1 for _ in self)




							def __repr__(       self     :       List[str]):
									return "->".join([str(A_) for item in self])




							def __getitem__(       self     :       Optional[int]				,				A_     :       int):
									if not 0 <= index < len(self):
											raise ValueError('''list index out of range.''')
									for i, node in enumerate(self):
											if i == index:
													return node
									return None




							def __setitem__(       self     :       str				,				A_     :       int				,				A_     :       Any):
									if not 0 <= index < len(self):
											raise ValueError('''list index out of range.''')
									lowerCAmelCase_			:     Optional[Any]									= self.head
									for _ in range(A_):
											lowerCAmelCase_			:     Tuple									= current.next
									lowerCAmelCase_			:     List[str]									= data




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       Any):
									self.insert_nth(len(self)				,				A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Any):
									self.insert_nth(0				,				A_)




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       int				,				A_     :       Any):
									if not 0 <= index <= len(self):
											raise IndexError('''list index out of range''')
									lowerCAmelCase_			:     Any									= Node(A_)
									if self.head is None:
											lowerCAmelCase_			:     Any									= new_node
									elif index == 0:
											lowerCAmelCase_			:     List[Any]									= self.head  # link new_node to head
											lowerCAmelCase_			:     Union[str, Any]									= new_node
									else:
											lowerCAmelCase_			:     Optional[Any]									= self.head
											for _ in range(index - 1):
													lowerCAmelCase_			:     Union[str, Any]									= temp.next
											lowerCAmelCase_			:     str									= temp.next
											lowerCAmelCase_			:     List[str]									= new_node




							def    UpperCAmelCase__			(       self     :       List[Any]):  # print every node data
									print(self)




							def    UpperCAmelCase__			(       self     :       str):
									return self.delete_nth(0)




							def    UpperCAmelCase__			(       self     :       Any):  # delete from tail
									return self.delete_nth(len(self) - 1)




							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       int = 0):
									if not 0 <= index <= len(self) - 1:  # test if index is valid
											raise IndexError('''List index out of range.''')
									lowerCAmelCase_			:     Optional[Any]									= self.head  # default first node
									if index == 0:
											lowerCAmelCase_			:     Any									= self.head.next
									else:
											lowerCAmelCase_			:     int									= self.head
											for _ in range(index - 1):
													lowerCAmelCase_			:     Dict									= temp.next
											lowerCAmelCase_			:     Any									= temp.next
											lowerCAmelCase_			:     str									= temp.next.next
									return delete_node.data




							def    UpperCAmelCase__			(       self     :       str):
									return self.head is None







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Tuple									= None
									lowerCAmelCase_			:     List[Any]									= self.head

									while current:
											# Store the current node's next node.
											lowerCAmelCase_			:     Optional[int]									= current.next
											# Make the current node's next point backwards
											lowerCAmelCase_			:     Tuple									= prev
											# Make the previous node be the current node
											lowerCAmelCase_			:     int									= current
											# Make the current node the next node (to progress iteration)
											lowerCAmelCase_			:     List[Any]									= next_node
									# Return prev in order to put the head at the end
									lowerCAmelCase_			:     Tuple									= prev





def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     int									= LinkedList()
		assert linked_list.is_empty() is True
		assert str(__UpperCamelCase ) == ""

		try:
				linked_list.delete_head()
				raise AssertionError  # This should not happen.
		except IndexError:
				assert True  # This should happen.

		try:
				linked_list.delete_tail()
				raise AssertionError  # This should not happen.
		except IndexError:
				assert True  # This should happen.

		for i in range(10 ):
				assert len(__UpperCamelCase ) == i
				linked_list.insert_nth(__UpperCamelCase     ,i + 1 )
		assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1     ,11 ) )

		linked_list.insert_head(0 )
		linked_list.insert_tail(11 )
		assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(0     ,12 ) )

		assert linked_list.delete_head() == 0
		assert linked_list.delete_nth(9 ) == 10
		assert linked_list.delete_tail() == 11
		assert len(__UpperCamelCase ) == 9
		assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(1     ,10 ) )

		assert all(linked_list[i] == i + 1 for i in range(0     ,9 ) ) is True

		for i in range(0     ,9 ):
				lowerCAmelCase_			:     int									= -i
		assert all(linked_list[i] == -i for i in range(0     ,9 ) ) is True

		linked_list.reverse()
		assert str(__UpperCamelCase ) == "->".join(str(__UpperCamelCase ) for i in range(-8     ,1 ) )


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Dict									= [
		    -9,
		    100,
		    Node(77345112 ),
		    '''dlrow olleH''',
		    7,
		    5555,
		    0,
		    -1_9_2.5_5_5_5_5,
		    '''Hello, world!''',
		    7_7.9,
		    Node(10 ),
		    None,
		    None,
		    1_2.2_0,
		]
		lowerCAmelCase_			:     Any									= LinkedList()

		for i in test_input:
				linked_list.insert_tail(__UpperCamelCase )

		# Check if it's empty or not
		assert linked_list.is_empty() is False
		assert (
		    str(__UpperCamelCase ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
		    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
		)

		# Delete the head
		lowerCAmelCase_			:     int									= linked_list.delete_head()
		assert result == -9
		assert (
		    str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
		    "Hello, world!->77.9->Node(10)->None->None->12.2"
		)

		# Delete the tail
		lowerCAmelCase_			:     Optional[int]									= linked_list.delete_tail()
		assert result == 1_2.2
		assert (
		    str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
		    "Hello, world!->77.9->Node(10)->None->None"
		)

		# Delete a node in specific location in linked list
		lowerCAmelCase_			:     Dict									= linked_list.delete_nth(10 )
		assert result is None
		assert (
		    str(__UpperCamelCase ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
		    "Hello, world!->77.9->Node(10)->None"
		)

		# Add a Node instance to its head
		linked_list.insert_head(Node('''Hello again, world!''' ) )
		assert (
		    str(__UpperCamelCase )
		    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
		    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
		)

		# Add None to its tail
		linked_list.insert_tail(__UpperCamelCase )
		assert (
		    str(__UpperCamelCase )
		    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
		    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
		)

		# Reverse the linked list
		linked_list.reverse()
		assert (
		    str(__UpperCamelCase )
		    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
		    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
		)


def 			UpperCamelCase(  ):
		from doctest import testmod

		testmod()

		lowerCAmelCase_			:     Any									= LinkedList()
		linked_list.insert_head(input('''Inserting 1st at head ''' ).strip() )
		linked_list.insert_head(input('''Inserting 2nd at head ''' ).strip() )
		print('''\nPrint list:''' )
		linked_list.print_list()
		linked_list.insert_tail(input('''\nInserting 1st at tail ''' ).strip() )
		linked_list.insert_tail(input('''Inserting 2nd at tail ''' ).strip() )
		print('''\nPrint list:''' )
		linked_list.print_list()
		print('''\nDelete head''' )
		linked_list.delete_head()
		print('''Delete tail''' )
		linked_list.delete_tail()
		print('''\nPrint list:''' )
		linked_list.print_list()
		print('''\nReverse linked list''' )
		linked_list.reverse()
		print('''\nPrint list:''' )
		linked_list.print_list()
		print('''\nString representation of linked list:''' )
		print(__UpperCamelCase )
		print('''\nReading/changing Node data using indexing:''' )
		print(f"""Element at Position 1: {linked_list[1]}""" )
		lowerCAmelCase_			:     Optional[Any]									= input('''Enter New Value: ''' ).strip()
		print('''New list:''' )
		print(__UpperCamelCase )
		print(f"""length of linked_list is : {len(__UpperCamelCase )}""" )


if __name__ == "__main__":
						main()










import copy

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


A__  :  Union[str, Any]								=					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 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''rag'''
							_a			      = True




							def __init__(       self     :       str				,				A_     :       List[Any]=None				,				A_     :       str=True				,				A_     :       Tuple=None				,				A_     :       Union[str, Any]=None				,				A_     :       List[str]=None				,				A_     :       List[str]=None				,				A_     :       List[Any]=None				,				A_     :       Union[str, Any]=" / "				,				A_     :       Tuple=" // "				,				A_     :       Any=5				,				A_     :       Optional[Any]=3_0_0				,				A_     :       Tuple=7_6_8				,				A_     :       Union[str, Any]=8				,				A_     :       Dict="wiki_dpr"				,				A_     :       Optional[Any]="train"				,				A_     :       Dict="compressed"				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				A_     :       str=False				,				A_     :       Dict=False				,				A_     :       Dict=0.0				,				A_     :       List[str]=True				,				A_     :       List[str]=False				,				A_     :       List[Any]=False				,				A_     :       Any=False				,				A_     :       Optional[int]=True				,				A_     :       int=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    bos_token_id=A_				,				pad_token_id=A_				,				eos_token_id=A_				,				decoder_start_token_id=A_				,				forced_eos_token_id=A_				,				is_encoder_decoder=A_				,				prefix=A_				,				vocab_size=A_				,				**A_				,				)
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									lowerCAmelCase_			:     List[str]									= kwargs.pop('''question_encoder''')
									lowerCAmelCase_			:     Tuple									= question_encoder_config.pop('''model_type''')
									lowerCAmelCase_			:     Tuple									= kwargs.pop('''generator''')
									lowerCAmelCase_			:     Dict									= decoder_config.pop('''model_type''')

									from ..auto.configuration_auto import AutoConfig

									lowerCAmelCase_			:     Union[str, Any]									= AutoConfig.for_model(A_				,				**A_)
									lowerCAmelCase_			:     int									= AutoConfig.for_model(A_				,				**A_)

									lowerCAmelCase_			:     List[Any]									= reduce_loss
									lowerCAmelCase_			:     Optional[Any]									= label_smoothing
									lowerCAmelCase_			:     Union[str, Any]									= exclude_bos_score
									lowerCAmelCase_			:     List[Any]									= do_marginalize

									lowerCAmelCase_			:     int									= title_sep
									lowerCAmelCase_			:     Optional[int]									= doc_sep
									lowerCAmelCase_			:     List[str]									= n_docs
									lowerCAmelCase_			:     int									= max_combined_length

									lowerCAmelCase_			:     Union[str, Any]									= dataset
									lowerCAmelCase_			:     int									= dataset_split
									lowerCAmelCase_			:     Dict									= index_name

									lowerCAmelCase_			:     Union[str, Any]									= retrieval_vector_size
									lowerCAmelCase_			:     Optional[Any]									= retrieval_batch_size
									lowerCAmelCase_			:     List[str]									= passages_path
									lowerCAmelCase_			:     Any									= index_path
									lowerCAmelCase_			:     int									= use_dummy_dataset

									lowerCAmelCase_			:     Tuple									= output_retrieved

									lowerCAmelCase_			:     List[Any]									= do_deduplication

									lowerCAmelCase_			:     Union[str, Any]									= use_cache

									if self.forced_eos_token_id is None:
											lowerCAmelCase_			:     List[Any]									= getattr(self.generator				,				'''forced_eos_token_id'''				,				A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       str				,				A_     :       PretrainedConfig				,				A_     :       PretrainedConfig				,				**A_     :       Any):
									return cls(question_encoder=question_encoder_config.to_dict()				,				generator=generator_config.to_dict()				,				**A_)







							def    UpperCAmelCase__			(       self     :       str):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Tuple									= self.question_encoder.to_dict()
									lowerCAmelCase_			:     Dict									= self.generator.to_dict()
									lowerCAmelCase_			:     str									= self.__class__.model_type
									return output





