





from __future__ import annotations

_UpperCAmelCase							:			Union[str, Any]        =							[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      )     ->	tuple[list[list[int]], list[list[int]]]:
    lowerCamelCase__       : Tuple   					=						[
        [0 for col in range(len(grid[0]					)					)] for row in range(len(_UpperCAmelCase					)					)
    ]  # the reference grid
    lowerCamelCase__       : List[Any]   					=						1
    lowerCamelCase__       : Union[str, Any]   					=						[
        [0 for col in range(len(grid[0]					)					)] for row in range(len(_UpperCAmelCase					)					)
    ]  # the action grid

    lowerCamelCase__       : Union[str, Any]   					=						init[0]
    lowerCamelCase__       : int   					=						init[1]
    lowerCamelCase__       : Optional[int]   					=						0
    lowerCamelCase__       : Optional[int]   					=						g + heuristic[x][y]  # cost from starting cell to destination cell
    lowerCamelCase__       : List[Any]   					=						[[f, g, x, y]]

    lowerCamelCase__       : Tuple   					=						False  # flag that is set when search is complete
    lowerCamelCase__       : Union[str, Any]   					=						False  # flag set if we can't find expand

    while not found and not resign:
        if len(_UpperCAmelCase					) == 0:
            raise ValueError('Algorithm is unable to find solution'					)
        else:  # to choose the least costliest action so as to move closer to the goal
            cell.sort()
            cell.reverse()
            lowerCamelCase__       : List[Any]   					=						cell.pop()
            lowerCamelCase__       : Optional[int]   					=						next_cell[2]
            lowerCamelCase__       : Tuple   					=						next_cell[3]
            lowerCamelCase__       : int   					=						next_cell[1]

            if x == goal[0] and y == goal[1]:
                lowerCamelCase__       : str   					=						True
            else:
                for i in range(len(_UpperCAmelCase					)					):  # to try out different valid actions
                    lowerCamelCase__       : Dict   					=						x + DIRECTIONS[i][0]
                    lowerCamelCase__       : str   					=						y + DIRECTIONS[i][1]
                    if xa >= 0 and xa < len(_UpperCAmelCase					) and ya >= 0 and ya < len(grid[0]					):
                        if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                            lowerCamelCase__       : List[Any]   					=						g + cost
                            lowerCamelCase__       : Optional[Any]   					=						ga + heuristic[xa][ya]
                            cell.append([fa, ga, xa, ya]					)
                            lowerCamelCase__       : List[Any]   					=						1
                            lowerCamelCase__       : Optional[int]   					=						i
    lowerCamelCase__       : Union[str, Any]   					=						[]
    lowerCamelCase__       : Dict   					=						goal[0]
    lowerCamelCase__       : Optional[Any]   					=						goal[1]
    invpath.append([x, y]					)  # we get the reverse path from here
    while x != init[0] or y != init[1]:
        lowerCamelCase__       : int   					=						x - DIRECTIONS[action[x][y]][0]
        lowerCamelCase__       : Optional[Any]   					=						y - DIRECTIONS[action[x][y]][1]
        lowerCamelCase__       : Optional[Any]   					=						xa
        lowerCamelCase__       : List[str]   					=						ya
        invpath.append([x, y]					)

    lowerCamelCase__       : Optional[int]   					=						[]
    for i in range(len(_UpperCAmelCase					)					):
        path.append(invpath[len(_UpperCAmelCase					) - 1 - i]					)
    return path, action


if __name__ == "__main__":
    _UpperCAmelCase							:			Optional[int]        =							[
        [0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
        [0, 1, 0, 0, 0, 0],
        [0, 1, 0, 0, 1, 0],
        [0, 0, 0, 0, 1, 0],
    ]

    _UpperCAmelCase							:			Dict        =							[0, 0]
    # all coordinates are given in format [y,x]
    _UpperCAmelCase							:			int        =							[len(grid) - 1, len(grid[0]) - 1]
    _UpperCAmelCase							:			Dict        =							1

    # the cost map which pushes the path closer to the goal
    _UpperCAmelCase							:			Any        =							[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
    for i in range(len(grid)):
        for j in range(len(grid[0])):
            _UpperCAmelCase							:			Dict        =							abs(i - goal[0]) + abs(j - goal[1])
            if grid[i][j] == 1:
                # added extra penalty in the heuristic map
                _UpperCAmelCase							:			List[Any]        =							99

    _UpperCAmelCase  ,_UpperCAmelCase							:			Dict        =							search(grid, init, goal, cost, heuristic)

    print("""ACTION MAP""")
    for i in range(len(action)):
        print(action[i])

    for i in range(len(path)):
        print(path[i])












# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class   lowerCAmelCase				(   __UpperCamelCase     ):
 UpperCAmelCase__						=						"""philschmid/bart-large-cnn-samsum"""
 UpperCAmelCase__						=						(
     """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
     """and returns a summary of the text."""
 )
 UpperCAmelCase__						=						"""summarizer"""
 UpperCAmelCase__						=						AutoTokenizer
 UpperCAmelCase__						=						AutoModelForSeqaSeqLM

 UpperCAmelCase__						=						["""text"""]
 UpperCAmelCase__						=						["""text"""]
 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      List[str]:
     return self.pre_processor(UpperCAmelCase     ,	return_tensors='pt'     ,	truncation=UpperCAmelCase      )
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : Dict      )     ->      str:
     return self.model.generate(**UpperCAmelCase      )[0]







 def 	A_      ( self    : Tuple     ,	UpperCAmelCase    : List[Any]      )     ->      Union[str, Any]:
     return self.pre_processor.decode(UpperCAmelCase     ,	skip_special_tokens=UpperCAmelCase     ,	clean_up_tokenization_spaces=UpperCAmelCase      )





