





"""simple docstring"""





from __future__ import annotations

SCREAMING_SNAKE_CASE__					=   list[tuple[int, int]]

SCREAMING_SNAKE_CASE__					=   [
    [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],
]

SCREAMING_SNAKE_CASE__					=   ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       pos_x
            lowerCAmelCase  						=       pos_y
            lowerCAmelCase  						=       (pos_y, pos_x)
            lowerCAmelCase  						=       goal_x
            lowerCAmelCase  						=       goal_y
            lowerCAmelCase  						=       g_cost
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       self.calculate_heuristic()





      def     _snake_case			(       self )		->     float:
            lowerCAmelCase  						=       abs(self.pos_x - self.goal_x )
            lowerCAmelCase  						=       abs(self.pos_y - self.goal_y )
            return dx + dy





      def __lt__(       self      ,						lowercase )		->     bool:
            return self.f_cost < other.f_cost







class 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase )		->     List[Any]:
            lowerCAmelCase  						=       Node(start[1]      ,						start[0]      ,						goal[1]      ,						goal[0]      ,						0      ,						lowercase )
            lowerCAmelCase  						=       Node(goal[1]      ,						goal[0]      ,						goal[1]      ,						goal[0]      ,						99_999      ,						lowercase )

            lowerCAmelCase  						=       [self.start]
            lowerCAmelCase  						=       []

            lowerCAmelCase  						=       False





      def     _snake_case			(       self )		->     Path | None:
            while self.open_nodes:
                  # Open Nodes are sorted using __lt__
                  self.open_nodes.sort()
                  lowerCAmelCase  						=       self.open_nodes.pop(0 )

                  if current_node.pos == self.target.pos:
                        lowerCAmelCase  						=       True
                        return self.retrace_path(lowercase )

                  self.closed_nodes.append(lowercase )
                  lowerCAmelCase  						=       self.get_successors(lowercase )

                  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(lowercase )
                        else:
                              # retrieve the best current path
                              lowerCAmelCase  						=       self.open_nodes.pop(self.open_nodes.index(lowercase ) )

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

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





      def     _snake_case			(       self      ,						lowercase )		->     list[Node]:
            lowerCAmelCase  						=       []
            for action in delta:
                  lowerCAmelCase  						=       parent.pos_x + action[1]
                  lowerCAmelCase  						=       parent.pos_y + action[0]

                  if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(lowercase ) - 1):
                        continue

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

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





      def     _snake_case			(       self      ,						lowercase )		->     Path:
            lowerCAmelCase  						=       node
            lowerCAmelCase  						=       []
            while current_node is not None:
                  path.append((current_node.pos_y, current_node.pos_x) )
                  lowerCAmelCase  						=       current_node.parent
            path.reverse()
            return path


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   (0, 0)
       SCREAMING_SNAKE_CASE__					=   (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       print("------")

       SCREAMING_SNAKE_CASE__					=   GreedyBestFirst(init, goal)
       SCREAMING_SNAKE_CASE__					=   greedy_bf.search()
       if path:
              for pos_x, pos_y in path:
                     SCREAMING_SNAKE_CASE__					=   2

              for elem in grid:
                     print(elem)













"""simple docstring"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_SNAKE_CASE			):
                  return ext

      raise Exception(
          F'Unable to determine file format from file extension {path}. '
          F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_SNAKE_CASE							,						output_path=args.output							,						input_path=args.input							,						column=args.column if args.column else nlp.default_input_names							,						overwrite=args.overwrite							,						)
      return RunCommand(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       parser.add_parser("""run"""      ,						help="""Run a pipeline through the CLI""" )
            run_parser.add_argument("""--task"""      ,						choices=get_supported_tasks()      ,						help="""Task to run""" )
            run_parser.add_argument("""--input"""      ,						type=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            run_parser.add_argument("""--overwrite"""      ,						action="""store_true"""      ,						help="""Allow overwriting the output file.""" )
            run_parser.set_defaults(func=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

            for entry in self._reader:
                  lowerCAmelCase  						=       nlp(**lowercase ) if self._reader.is_multi_columns else nlp(lowercase )
                  if isinstance(lowercase      ,						lowercase ):
                        outputs.append(lowercase )
                  else:
                        outputs += output

        # Saving data
            if self._nlp.binary_output:
                  lowerCAmelCase  						=       self._reader.save_binary(lowercase )
                  logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
            else:
                  self._reader.save(lowercase )






