






from __future__ import annotations

import math

class 		_snake_case  :






			def __init__(       self		,					a)      ->       None:
						SCREAMING_SNAKE_CASE						= size
						# approximate the overall size of segment tree with given value
						SCREAMING_SNAKE_CASE						= [0 for i in range(0		,					4 * size)]
						# create array to store lazy update
						SCREAMING_SNAKE_CASE						= [0 for i in range(0		,					4 * size)]
						SCREAMING_SNAKE_CASE						= [0 for i in range(0		,					4 * size)]  # flag for lazy update






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						return idx * 2






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						return idx * 2 + 1






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a)      ->       None:
						if left_element == right_element:
									SCREAMING_SNAKE_CASE						= a[left_element - 1]
						else:
									SCREAMING_SNAKE_CASE						= (left_element + right_element) // 2
									self.build(self.left(a)		,					a		,					a		,					a)
									self.build(self.right(a)		,					mid + 1		,					a		,					a)
									SCREAMING_SNAKE_CASE						= max(
									    self.segment_tree[self.left(a)]		,					self.segment_tree[self.right(a)])






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       bool:
						if self.flag[idx] is True:
									SCREAMING_SNAKE_CASE						= self.lazy[idx]
									SCREAMING_SNAKE_CASE						= False
									if left_element != right_element:
												SCREAMING_SNAKE_CASE						= self.lazy[idx]
												SCREAMING_SNAKE_CASE						= self.lazy[idx]
												SCREAMING_SNAKE_CASE						= True
												SCREAMING_SNAKE_CASE						= True

						if right_element < a or left_element > b:
									return True
						if left_element >= a and right_element <= b:
									SCREAMING_SNAKE_CASE						= val
									if left_element != right_element:
												SCREAMING_SNAKE_CASE						= val
												SCREAMING_SNAKE_CASE						= val
												SCREAMING_SNAKE_CASE						= True
												SCREAMING_SNAKE_CASE						= True
									return True
						SCREAMING_SNAKE_CASE						= (left_element + right_element) // 2
						self.update(self.left(a)		,					a		,					a		,					a		,					a		,					a)
						self.update(self.right(a)		,					mid + 1		,					a		,					a		,					a		,					a)
						SCREAMING_SNAKE_CASE						= max(
						    self.segment_tree[self.left(a)]		,					self.segment_tree[self.right(a)])
						return True






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a)      ->       int | float:
						if self.flag[idx] is True:
									SCREAMING_SNAKE_CASE						= self.lazy[idx]
									SCREAMING_SNAKE_CASE						= False
									if left_element != right_element:
												SCREAMING_SNAKE_CASE						= self.lazy[idx]
												SCREAMING_SNAKE_CASE						= self.lazy[idx]
												SCREAMING_SNAKE_CASE						= True
												SCREAMING_SNAKE_CASE						= True
						if right_element < a or left_element > b:
									return -math.inf
						if left_element >= a and right_element <= b:
									return self.segment_tree[idx]
						SCREAMING_SNAKE_CASE						= (left_element + right_element) // 2
						SCREAMING_SNAKE_CASE						= self.query(self.left(a)		,					a		,					a		,					a		,					a)
						SCREAMING_SNAKE_CASE						= self.query(self.right(a)		,					mid + 1		,					a		,					a		,					a)
						return max(a		,					a)







			def __str__(       self)      ->       str:
						return str([self.query(1		,					1		,					self.size		,					a		,					a) for i in range(1		,					self.size + 1)])


if __name__ == "__main__":
				a_		:						Optional[int]             =    [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
				a_		:						List[str]             =    15
				a_		:						List[str]             =    SegmentTree(size)
				segt.build(1, 1, size, A)
				print(segt.query(1, 1, size, 4, 6))
				print(segt.query(1, 1, size, 7, 11))
				print(segt.query(1, 1, size, 7, 12))
				segt.update(1, 1, size, 1, 3, 1_11)
				print(segt.query(1, 1, size, 1, 15))
				segt.update(1, 1, size, 7, 8, 2_35)
				print(segt)












import copy

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


a_		:						List[str]             =    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(A__					)
class 		_snake_case  (  A__					):
			_lowercase		:    Optional[int]			     =							'''rag'''
			_lowercase		:    Optional[int]			     =							True






			def __init__(       self		,					a=None		,					a=True		,					a=None		,					a=None		,					a=None		,					a=None		,					a=None		,					a=" / "		,					a=" // "		,					a=5		,					a=300		,					a=768		,					a=8		,					a="wiki_dpr"		,					a="train"		,					a="compressed"		,					a=None		,					a=None		,					a=False		,					a=False		,					a=0.0		,					a=True		,					a=False		,					a=False		,					a=False		,					a=True		,					a=None		,					**a		,					)      ->       Optional[Any]:
						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"
						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(a		,					**a)
						SCREAMING_SNAKE_CASE						= AutoConfig.for_model(a		,					**a)

						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'		,					a)






			@classmethod
			def    SCREAMING_SNAKE_CASE__    (       cls		,					a		,					a		,					**a)      ->       PretrainedConfig:
						return cls(question_encoder=question_encoder_config.to_dict()		,					generator=generator_config.to_dict()		,					**a)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, 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




