


'''simple docstring'''







from __future__ import annotations

import time

lowerCAmelCase     :  Union[str, Any]											=    list[tuple[int, int]]

lowerCAmelCase     :  Optional[Any]											=    [
    [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     :  Optional[int]											=    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right
class 						UpperCamelCase__       :



     """simple docstring"""






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

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							pos_x
            _lowerCAmelCase     :				List[Any]       =							pos_y
            _lowerCAmelCase     :				Union[str, Any]       =							(pos_y, pos_x)
            _lowerCAmelCase     :				int       =							goal_x
            _lowerCAmelCase     :				Any       =							goal_y
            _lowerCAmelCase     :				int       =							parent
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							Node(start[1]   ,						start[0]   ,						goal[1]   ,						goal[0]   ,						snake_case__ )
            _lowerCAmelCase     :				str       =							Node(goal[1]   ,						goal[0]   ,						goal[1]   ,						goal[0]   ,						snake_case__ )

            _lowerCAmelCase     :				Dict       =							[self.start]
            _lowerCAmelCase     :				str       =							False







     def a						(			self ):

            '''simple docstring'''

            while self.node_queue:
                   _lowerCAmelCase     :				Any       =							self.node_queue.pop(0 )

                   if current_node.pos == self.target.pos:
                          _lowerCAmelCase     :				Optional[int]       =							True
                          return self.retrace_path(snake_case__ )

                   _lowerCAmelCase     :				Union[str, Any]       =							self.get_successors(snake_case__ )

                   for node in successors:
                          self.node_queue.append(snake_case__ )

            if not self.reached:
                   return [self.start.pos]
            return None







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							[]
            for action in delta:
                   _lowerCAmelCase     :				int       =							parent.pos_x + action[1]
                   _lowerCAmelCase     :				Any       =							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   ,						snake_case__ ) )
            return successors







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

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



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							BreadthFirstSearch(snake_case__   ,						snake_case__ )
            _lowerCAmelCase     :				Optional[int]       =							BreadthFirstSearch(snake_case__   ,						snake_case__ )
            _lowerCAmelCase     :				Dict       =							False







     def a						(			self ):

            '''simple docstring'''

            while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
                   _lowerCAmelCase     :				Union[str, Any]       =							self.fwd_bfs.node_queue.pop(0 )
                   _lowerCAmelCase     :				Optional[Any]       =							self.bwd_bfs.node_queue.pop(0 )

                   if current_bwd_node.pos == current_fwd_node.pos:
                          _lowerCAmelCase     :				List[Any]       =							True
                          return self.retrace_bidirectional_path(
                              snake_case__   ,						snake_case__ )

                   _lowerCAmelCase     :				Union[str, Any]       =							current_bwd_node
                   _lowerCAmelCase     :				List[Any]       =							current_fwd_node

                   _lowerCAmelCase     :				Optional[Any]       =							{
                       self.fwd_bfs: self.fwd_bfs.get_successors(snake_case__ ),
                       self.bwd_bfs: self.bwd_bfs.get_successors(snake_case__ ),
                   }

                   for bfs in [self.fwd_bfs, self.bwd_bfs]:
                          for node in successors[bfs]:
                                 bfs.node_queue.append(snake_case__ )

            if not self.reached:
                   return [self.fwd_bfs.start.pos]
            return None







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.fwd_bfs.retrace_path(snake_case__ )
            _lowerCAmelCase     :				List[Any]       =							self.bwd_bfs.retrace_path(snake_case__ )
            bwd_path.pop()
            bwd_path.reverse()
            _lowerCAmelCase     :				Union[str, Any]       =							fwd_path + bwd_path
            return path


if __name__ == "__main__":
       # all coordinates are given in format [y,x]
       import doctest

       doctest.testmod()
       lowerCAmelCase     :  Union[str, Any]											=    (0, 0)
       lowerCAmelCase     :  int											=    (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       lowerCAmelCase     :  List[str]											=    time.time()
       lowerCAmelCase     :  Any											=    BreadthFirstSearch(init, goal)
       lowerCAmelCase     :  List[str]											=    bfs.search()
       lowerCAmelCase     :  List[Any]											=    time.time() - start_bfs_time

       print("""Unidirectional BFS computation time : """, bfs_time)

       lowerCAmelCase     :  Dict											=    time.time()
       lowerCAmelCase     :  Union[str, Any]											=    BidirectionalBreadthFirstSearch(init, goal)
       lowerCAmelCase     :  Any											=    bd_bfs.search()
       lowerCAmelCase     :  List[Any]											=    time.time() - start_bd_bfs_time

       print("""Bidirectional BFS computation time : """, bd_bfs_time)





'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

