
'''simple docstring'''
from __future__ import annotations

_snake_case         =  list[tuple[int, int]]

_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],
]

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


class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				Any      					=       pos_x
      _lowercase      :				Dict      					=       pos_y
      _lowercase      :				Union[str, Any]      					=       (pos_y, pos_x)
      _lowercase      :				Union[str, Any]      					=       goal_x
      _lowercase      :				List[Any]      					=       goal_y
      _lowercase      :				List[Any]      					=       g_cost
      _lowercase      :				Union[str, Any]      					=       parent
      _lowercase      :				Dict      					=       self.calculate_heuristic()


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       abs(self.pos_x - self.goal_x  )
      _lowercase      :				str      					=       abs(self.pos_y - self.goal_y  )
      return dx + dy






   def __lt__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      return self.f_cost < other.f_cost




class        a__  :


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       Node(start[1]      ,    start[0]      ,    goal[1]      ,    goal[0]      ,    0      ,    _lowercase  )
      _lowercase      :				Any      					=       Node(goal[1]      ,    goal[0]      ,    goal[1]      ,    goal[0]      ,    99999      ,    _lowercase  )

      _lowercase      :				Optional[int]      					=       [self.start]
      _lowercase      :				Optional[int]      					=       []

      _lowercase      :				List[str]      					=       False


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

         if current_node.pos == self.target.pos:
            _lowercase      :				Any      					=       True
            return self.retrace_path(_lowercase  )

         self.closed_nodes.append(_lowercase  )
         _lowercase      :				List[str]      					=       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
               _lowercase      :				Any      					=       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 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       []
      for action in delta:
         _lowercase      :				List[Any]      					=       parent.pos_x + action[1]
         _lowercase      :				Optional[int]      					=       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 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       node
      _lowercase      :				Optional[int]      					=       []
      while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)  )
         _lowercase      :				Tuple      					=       current_node.parent
      path.reverse()
      return path


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

    print('------')

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

        for elem in grid:
            print(elem)









'''simple docstring'''
from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






