



'''simple docstring'''





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
SCREAMING_SNAKE_CASE__								=     0

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

SCREAMING_SNAKE_CASE__								=     tuple[int, int]


class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							None:
           lowercase__		       =						pos_x
           lowercase__		       =						pos_y
           lowercase__		       =						(pos_y, pos_x)
           lowercase__		       =						goal_x
           lowercase__		       =						goal_y
           lowercase__		       =						g_cost
           lowercase__		       =						parent
           lowercase__		       =						self.calculate_heuristic()
           lowercase__		       =						self.g_cost + self.h_cost



       def 							_a      (				self      )  ->							float:
           lowercase__		       =						self.pos_x - self.goal_x
           lowercase__		       =						self.pos_y - self.goal_y
           if HEURISTIC == 1:
               return abs(UpperCAmelCase_      ) + abs(UpperCAmelCase_      )
           else:
               return sqrt(dy**2 + dx**2      )







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





class 				snake_case :



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

           lowercase__		       =						[self.start]
           lowercase__		       =						[]

           lowercase__		       =						False



       def 							_a      (				self      )  ->							list[TPosition]:

           while self.open_nodes:
               # Open Nodes are sorted using __lt__
               self.open_nodes.sort()
               lowercase__		       =						self.open_nodes.pop(0      )

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

               self.closed_nodes.append(UpperCAmelCase_      )
               lowercase__		       =						self.get_successors(UpperCAmelCase_      )

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

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

           return [self.start.pos]



       def 							_a      (				self     ,UpperCAmelCase_      )  ->							list[Node]:
           lowercase__		       =						[]
           for action in delta:
               lowercase__		       =						parent.pos_x + action[1]
               lowercase__		       =						parent.pos_y + action[0]
               if not (0 <= pos_x <= len(grid[0]      ) - 1 and 0 <= pos_y <= len(UpperCAmelCase_      ) - 1):
                   continue

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

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







       def 							_a      (				self     ,UpperCAmelCase_      )  ->							list[TPosition]:
           lowercase__		       =						node
           lowercase__		       =						[]
           while current_node is not None:
               path.append((current_node.pos_y, current_node.pos_x)      )
               lowercase__		       =						current_node.parent
           path.reverse()
           return path





class 				snake_case :



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							None:
           lowercase__		       =						AStar(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						AStar(UpperCAmelCase_     ,UpperCAmelCase_      )
           lowercase__		       =						False



       def 							_a      (				self      )  ->							list[TPosition]:

           while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
               self.fwd_astar.open_nodes.sort()
               self.bwd_astar.open_nodes.sort()
               lowercase__		       =						self.fwd_astar.open_nodes.pop(0      )
               lowercase__		       =						self.bwd_astar.open_nodes.pop(0      )

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

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

               lowercase__		       =						current_bwd_node
               lowercase__		       =						current_fwd_node

               lowercase__		       =						{
                   self.fwd_astar: self.fwd_astar.get_successors(UpperCAmelCase_      ),
                   self.bwd_astar: self.bwd_astar.get_successors(UpperCAmelCase_      ),
               }

               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(UpperCAmelCase_      )
                       else:
                           # retrieve the best current path
                           lowercase__		       =						astar.open_nodes.pop(
                               astar.open_nodes.index(UpperCAmelCase_      )      )

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

           return [self.fwd_astar.start.pos]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							list[TPosition]:
           lowercase__		       =						self.fwd_astar.retrace_path(UpperCAmelCase_      )
           lowercase__		       =						self.bwd_astar.retrace_path(UpperCAmelCase_      )
           bwd_path.pop()
           bwd_path.reverse()
           lowercase__		       =						fwd_path + bwd_path
           return path


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

 SCREAMING_SNAKE_CASE__								=     time.time()
 SCREAMING_SNAKE_CASE__								=     AStar(init, goal)
 SCREAMING_SNAKE_CASE__								=     a_star.search()
 SCREAMING_SNAKE_CASE__								=     time.time() - start_time
 print(f'''AStar execution time = {end_time:f} seconds''')

 SCREAMING_SNAKE_CASE__								=     time.time()
 SCREAMING_SNAKE_CASE__								=     BidirectionalAStar(init, goal)
 SCREAMING_SNAKE_CASE__								=     time.time() - bd_start_time
 print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')









'''simple docstring'''





import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

SCREAMING_SNAKE_CASE__      					=     _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)




def        lowerCamelCase  (				_snake_case  :						str       ):





    '''simple docstring'''


    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowercase__		       =						model_type_to_module_name(_snake_case       )

            lowercase__		       =						importlib.import_module(f'''.{module_name}'''     ,"transformers.models"       )
            try:
                return getattr(_snake_case     ,_snake_case       )
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_snake_case     ,"__name__"     ,_snake_case       ) == class_name:
            return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
    lowercase__		       =						importlib.import_module("transformers"       )
    if hasattr(_snake_case     ,_snake_case       ):
        return getattr(_snake_case     ,_snake_case       )

    return None




def        lowerCamelCase  (				_snake_case  :						Union[str, os.PathLike]     ,_snake_case  :						Optional[Union[str, os.PathLike]] = None     ,_snake_case  :						bool = False     ,_snake_case  :						bool = False     ,_snake_case  :						Optional[Dict[str, str]] = None     ,_snake_case  :						Optional[Union[bool, str]] = None     ,_snake_case  :						Optional[str] = None     ,_snake_case  :						bool = False     ,**_snake_case  :						Union[str, Any]     ,):





    '''simple docstring'''


    lowercase__		       =						get_file_from_repo(
        _snake_case     ,_snake_case     ,cache_dir=_snake_case     ,force_download=_snake_case     ,resume_download=_snake_case     ,proxies=_snake_case     ,use_auth_token=_snake_case     ,revision=_snake_case     ,local_files_only=_snake_case     ,)
    if resolved_config_file is None:
        logger.info(
            "Could not locate the feature extractor configuration file, will try to use the model config instead."       )
        return {}

    with open(_snake_case     ,encoding="utf-8"       ) as reader:
        return json.load(_snake_case       )



class 				snake_case :



       def __init__(				self      )  ->							List[str]:
           raise EnvironmentError(
               "AutoFeatureExtractor is designed to be instantiated "
               "using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."      )



       @classmethod
       @replace_list_option_in_docstrings(UpperCAmelCase_      )
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						kwargs.pop("config"     ,UpperCAmelCase_      )
           lowercase__		       =						kwargs.pop("trust_remote_code"     ,UpperCAmelCase_      )
           lowercase__		       =						True

           lowercase__       ,       lowercase__		       =						FeatureExtractionMixin.get_feature_extractor_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						config_dict.get("feature_extractor_type"     ,UpperCAmelCase_      )
           lowercase__		       =						None
           if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,{}      ):
               lowercase__		       =						config_dict["auto_map"]["AutoFeatureExtractor"]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(UpperCAmelCase_     ,UpperCAmelCase_      ):
                   lowercase__		       =						AutoConfig.from_pretrained(UpperCAmelCase_     ,**UpperCAmelCase_      )
               # It could be in `config.feature_extractor_type``
               lowercase__		       =						getattr(UpperCAmelCase_     ,"feature_extractor_type"     ,UpperCAmelCase_      )
               if hasattr(UpperCAmelCase_     ,"auto_map"      ) and "AutoFeatureExtractor" in config.auto_map:
                   lowercase__		       =						config.auto_map["AutoFeatureExtractor"]

           if feature_extractor_class is not None:
               lowercase__		       =						feature_extractor_class_from_name(UpperCAmelCase_      )

           lowercase__		       =						feature_extractor_auto_map is not None
           lowercase__		       =						feature_extractor_class is not None or type(UpperCAmelCase_      ) in FEATURE_EXTRACTOR_MAPPING
           lowercase__		       =						resolve_trust_remote_code(
               UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )

           if has_remote_code and trust_remote_code:
               lowercase__		       =						get_class_from_dynamic_module(
                   UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )
               lowercase__		       =						kwargs.pop("code_revision"     ,UpperCAmelCase_      )
               if os.path.isdir(UpperCAmelCase_      ):
                   feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(UpperCAmelCase_      ) in FEATURE_EXTRACTOR_MAPPING:
               lowercase__		       =						FEATURE_EXTRACTOR_MAPPING[type(UpperCAmelCase_      )]
               return feature_extractor_class.from_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()      )}'''      )







       @staticmethod
       def 							_a      (				UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Any:
           FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase_     ,UpperCAmelCase_      )




