





'''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   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       A       :       str							=						pos_x
       A       :       Optional[int]							=						pos_y
       A       :       Optional[int]							=						(pos_y, pos_x)
       A       :       List[str]							=						goal_x
       A       :       Any							=						goal_y
       A       :       Any							=						g_cost
       A       :       int							=						parent
       A       :       List[str]							=						self.calculate_heuristic()


   def 					snake_case				( self					):
       A       :       Dict							=						abs(self.pos_x - self.goal_x					)
       A       :       int							=						abs(self.pos_y - self.goal_y					)
       return dx + dy


   def __lt__( self    ,   _UpperCAmelCase					):
       return self.f_cost < other.f_cost







class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       int							=						Node(start[1]    ,   start[0]    ,   goal[1]    ,   goal[0]    ,   0    ,   _lowercase					)
       A       :       List[str]							=						Node(goal[1]    ,   goal[0]    ,   goal[1]    ,   goal[0]    ,   99_999    ,   _lowercase					)

       A       :       int							=						[self.start]
       A       :       list[Node]							=						[]

       A       :       Optional[int]							=						False


   def 					snake_case				( self					):

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

           if current_node.pos == self.target.pos:
               A       :       List[Any]							=						True
               return self.retrace_path(_lowercase					)

           self.closed_nodes.append(_lowercase					)
           A       :       Dict							=						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
                   A       :       List[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 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       Any							=						[]
       for action in delta:
           A       :       Union[str, Any]							=						parent.pos_x + action[1]
           A       :       List[str]							=						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    ,   _UpperCAmelCase					):
       A       :       int							=						node
       A       :       Optional[int]							=						[]
       while current_node is not None:
           path.append((current_node.pos_y, current_node.pos_x)					)
           A       :       List[str]							=						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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


snake_case_     =	{
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	["""SpeechT5Tokenizer"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SpeechT5ForSpeechToText""",
         """SpeechT5ForSpeechToSpeech""",
         """SpeechT5ForTextToSpeech""",
         """SpeechT5Model""",
         """SpeechT5PreTrainedModel""",
         """SpeechT5HifiGan""",
     ]

if TYPE_CHECKING:
     from .configuration_speechta import (
         SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
         SpeechTaConfig,
         SpeechTaHifiGanConfig,
     )
     from .feature_extraction_speechta import SpeechTaFeatureExtractor
     from .processing_speechta import SpeechTaProcessor

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_speechta import SpeechTaTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_speechta import (
              SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
              SpeechTaForSpeechToSpeech,
              SpeechTaForSpeechToText,
              SpeechTaForTextToSpeech,
              SpeechTaHifiGan,
              SpeechTaModel,
              SpeechTaPreTrainedModel,
          )

else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
