


'''simple docstring'''







from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowerCAmelCase     :  Tuple											=    0

lowerCAmelCase     :  Dict											=    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCAmelCase     :  str											=    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowerCAmelCase     :  Optional[Any]											=    tuple[int, int]
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							pos_x
            _lowerCAmelCase     :				Optional[int]       =							pos_y
            _lowerCAmelCase     :				Union[str, Any]       =							(pos_y, pos_x)
            _lowerCAmelCase     :				Dict       =							goal_x
            _lowerCAmelCase     :				Any       =							goal_y
            _lowerCAmelCase     :				Dict       =							g_cost
            _lowerCAmelCase     :				List[str]       =							parent
            _lowerCAmelCase     :				Union[str, Any]       =							self.calculate_heuristic()
            _lowerCAmelCase     :				Dict       =							self.g_cost + self.h_cost







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.pos_x - self.goal_x
            _lowerCAmelCase     :				Any       =							self.pos_y - self.goal_y
            if HEURISTIC == 1:
                   return abs(snake_case__ ) + abs(snake_case__ )
            else:
                   return sqrt(dy**2 + dx**2 )







     def __lt__(			self   ,						snake_case__ ):

            '''simple docstring'''

            return self.f_cost < other.f_cost
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							Node(start[1]   ,						start[0]   ,						goal[1]   ,						goal[0]   ,						0   ,						snake_case__ )
            _lowerCAmelCase     :				str       =							Node(goal[1]   ,						goal[0]   ,						goal[1]   ,						goal[0]   ,						9_9999   ,						snake_case__ )

            _lowerCAmelCase     :				List[str]       =							[self.start]
            _lowerCAmelCase     :				list[Node]       =							[]

            _lowerCAmelCase     :				Optional[int]       =							False







     def a						(			self ):

            '''simple docstring'''

            while self.open_nodes:
                   # Open Nodes are sorted using __lt__
                   self.open_nodes.sort()
                   _lowerCAmelCase     :				Optional[Any]       =							self.open_nodes.pop(0 )

                   if current_node.pos == self.target.pos:
                          return self.retrace_path(snake_case__ )

                   self.closed_nodes.append(snake_case__ )
                   _lowerCAmelCase     :				int       =							self.get_successors(snake_case__ )

                   for child_node in successors:
                          if child_node in self.closed_nodes:
                                 continue

                          if child_node not in self.open_nodes:
                                 self.open_nodes.append(snake_case__ )
                          else:
                                 # retrieve the best current path
                                 _lowerCAmelCase     :				Optional[Any]       =							self.open_nodes.pop(self.open_nodes.index(snake_case__ ) )

                                 if child_node.g_cost < better_node.g_cost:
                                        self.open_nodes.append(snake_case__ )
                                 else:
                                        self.open_nodes.append(snake_case__ )

            return [self.start.pos]







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							[]
            for action in delta:
                   _lowerCAmelCase     :				Optional[Any]       =							parent.pos_x + action[1]
                   _lowerCAmelCase     :				int       =							parent.pos_y + action[0]
                   if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(snake_case__ ) - 1):
                          continue

                   if grid[pos_y][pos_x] != 0:
                          continue

                   successors.append(
                       Node(
                           snake_case__   ,						snake_case__   ,						self.target.pos_y   ,						self.target.pos_x   ,						parent.g_cost + 1   ,						snake_case__   ,						) )
            return successors







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							node
            _lowerCAmelCase     :				Any       =							[]
            while current_node is not None:
                   path.append((current_node.pos_y, current_node.pos_x) )
                   _lowerCAmelCase     :				int       =							current_node.parent
            path.reverse()
            return path
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							AStar(snake_case__   ,						snake_case__ )
            _lowerCAmelCase     :				int       =							AStar(snake_case__   ,						snake_case__ )
            _lowerCAmelCase     :				Any       =							False







     def a						(			self ):

            '''simple docstring'''

            while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                   self.fwd_astar.open_nodes.sort()
                   self.bwd_astar.open_nodes.sort()
                   _lowerCAmelCase     :				List[Any]       =							self.fwd_astar.open_nodes.pop(0 )
                   _lowerCAmelCase     :				List[Any]       =							self.bwd_astar.open_nodes.pop(0 )

                   if current_bwd_node.pos == current_fwd_node.pos:
                          return self.retrace_bidirectional_path(
                              snake_case__   ,						snake_case__ )

                   self.fwd_astar.closed_nodes.append(snake_case__ )
                   self.bwd_astar.closed_nodes.append(snake_case__ )

                   _lowerCAmelCase     :				Union[str, Any]       =							current_bwd_node
                   _lowerCAmelCase     :				Optional[int]       =							current_fwd_node

                   _lowerCAmelCase     :				int       =							{
                       self.fwd_astar: self.fwd_astar.get_successors(snake_case__ ),
                       self.bwd_astar: self.bwd_astar.get_successors(snake_case__ ),
                   }

                   for astar in [self.fwd_astar, self.bwd_astar]:
                          for child_node in successors[astar]:
                                 if child_node in astar.closed_nodes:
                                        continue

                                 if child_node not in astar.open_nodes:
                                        astar.open_nodes.append(snake_case__ )
                                 else:
                                        # retrieve the best current path
                                        _lowerCAmelCase     :				Optional[Any]       =							astar.open_nodes.pop(
                                            astar.open_nodes.index(snake_case__ ) )

                                        if child_node.g_cost < better_node.g_cost:
                                               astar.open_nodes.append(snake_case__ )
                                        else:
                                               astar.open_nodes.append(snake_case__ )

            return [self.fwd_astar.start.pos]







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.fwd_astar.retrace_path(snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							self.bwd_astar.retrace_path(snake_case__ )
            bwd_path.pop()
            bwd_path.reverse()
            _lowerCAmelCase     :				Any       =							fwd_path + bwd_path
            return path


if __name__ == "__main__":
       # all coordinates are given in format [y,x]
       lowerCAmelCase     :  int											=    (0, 0)
       lowerCAmelCase     :  str											=    (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       lowerCAmelCase     :  str											=    time.time()
       lowerCAmelCase     :  Tuple											=    AStar(init, goal)
       lowerCAmelCase     :  List[str]											=    a_star.search()
       lowerCAmelCase     :  Union[str, Any]											=    time.time() - start_time
       print(F'''AStar execution time = {end_time:f} seconds''')

       lowerCAmelCase     :  Tuple											=    time.time()
       lowerCAmelCase     :  List[Any]											=    BidirectionalAStar(init, goal)
       lowerCAmelCase     :  Tuple											=    time.time() - bd_start_time
       print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





'''simple docstring'''







import copy

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


lowerCAmelCase     :  Tuple											=    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(SCREAMING_SNAKE_CASE_						)
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "rag"
     __magic_name__           =     True







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=" / "   ,						snake_case__=" // "   ,						snake_case__=5   ,						snake_case__=300   ,						snake_case__=768   ,						snake_case__=8   ,						snake_case__="wiki_dpr"   ,						snake_case__="train"   ,						snake_case__="compressed"   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=0.0   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__=None   ,						**snake_case__   ,						):

            '''simple docstring'''

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

            from ..auto.configuration_auto import AutoConfig

            _lowerCAmelCase     :				Union[str, Any]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )
            _lowerCAmelCase     :				List[str]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							reduce_loss
            _lowerCAmelCase     :				Optional[Any]       =							label_smoothing
            _lowerCAmelCase     :				Dict       =							exclude_bos_score
            _lowerCAmelCase     :				Optional[int]       =							do_marginalize

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

            _lowerCAmelCase     :				Dict       =							dataset
            _lowerCAmelCase     :				Optional[int]       =							dataset_split
            _lowerCAmelCase     :				str       =							index_name

            _lowerCAmelCase     :				Tuple       =							retrieval_vector_size
            _lowerCAmelCase     :				Any       =							retrieval_batch_size
            _lowerCAmelCase     :				Any       =							passages_path
            _lowerCAmelCase     :				Tuple       =							index_path
            _lowerCAmelCase     :				Any       =							use_dummy_dataset

            _lowerCAmelCase     :				Union[str, Any]       =							output_retrieved

            _lowerCAmelCase     :				List[str]       =							do_deduplication

            _lowerCAmelCase     :				Optional[int]       =							use_cache

            if self.forced_eos_token_id is None:
                   _lowerCAmelCase     :				Tuple       =							getattr(self.generator   ,						'forced_eos_token_id'   ,						snake_case__ )







     @classmethod
     def a						(			cls   ,						snake_case__   ,						snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

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

