




from __future__ import annotations

import time

lowerCamelCase	:	List[Any]				=   list[tuple[int, int]]

lowerCamelCase	:	Any				=   [
    [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],
]

lowerCamelCase	:	int				=   [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A     )		->    Any:
         snake_case				:    Any           =	pos_x
         snake_case				:    Any           =	pos_y
         snake_case				:    Optional[Any]           =	(pos_y, pos_x)
         snake_case				:    Any           =	goal_x
         snake_case				:    int           =	goal_y
         snake_case				:    str           =	parent




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A     )		->    Tuple:
         snake_case				:    Optional[int]           =	Node(start[1]    ,				start[0]    ,				goal[1]    ,				goal[0]    ,				A     )
         snake_case				:    Optional[int]           =	Node(goal[1]    ,				goal[0]    ,				goal[1]    ,				goal[0]    ,				A     )

         snake_case				:    List[str]           =	[self.start]
         snake_case				:    Optional[Any]           =	False

      def      UpperCAmelCase				(				self     )		->    Path | None:
         while self.node_queue:
            snake_case				:    int           =	self.node_queue.pop(0     )

            if current_node.pos == self.target.pos:
               snake_case				:    Optional[int]           =	True
               return self.retrace_path(A     )

            snake_case				:    List[Any]           =	self.get_successors(A     )

            for node in successors:
               self.node_queue.append(A     )

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

      def      UpperCAmelCase				(				self    ,				A     )		->    list[Node]:
         snake_case				:    List[str]           =	[]
         for action in delta:
            snake_case				:    Union[str, Any]           =	parent.pos_x + action[1]
            snake_case				:    str           =	parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]     ) - 1 and 0 <= pos_y <= len(A     ) - 1):
               continue

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

            successors.append(
                Node(A    ,				A    ,				self.target.pos_y    ,				self.target.pos_x    ,				A     )     )
         return successors

      def      UpperCAmelCase				(				self    ,				A     )		->    Path:
         snake_case				:    List[Any]           =	node
         snake_case				:    int           =	[]
         while current_node is not None:
            path.append((current_node.pos_y, current_node.pos_x)     )
            snake_case				:    Optional[int]           =	current_node.parent
         path.reverse()
         return path




class 						__lowercase		:




      """simple docstring"""





      def __init__(				self    ,				A    ,				A     )		->    Optional[int]:
         snake_case				:    Any           =	BreadthFirstSearch(A    ,				A     )
         snake_case				:    Dict           =	BreadthFirstSearch(A    ,				A     )
         snake_case				:    Dict           =	False

      def      UpperCAmelCase				(				self     )		->    Path | None:
         while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
            snake_case				:    Optional[Any]           =	self.fwd_bfs.node_queue.pop(0     )
            snake_case				:    Optional[int]           =	self.bwd_bfs.node_queue.pop(0     )

            if current_bwd_node.pos == current_fwd_node.pos:
               snake_case				:    List[Any]           =	True
               return self.retrace_bidirectional_path(
                   A    ,				A     )

            snake_case				:    Optional[int]           =	current_bwd_node
            snake_case				:    Any           =	current_fwd_node

            snake_case				:    Dict           =	{
                self.fwd_bfs: self.fwd_bfs.get_successors(A     ),
                self.bwd_bfs: self.bwd_bfs.get_successors(A     ),
            }

            for bfs in [self.fwd_bfs, self.bwd_bfs]:
               for node in successors[bfs]:
                  bfs.node_queue.append(A     )

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

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    Path:
         snake_case				:    Optional[Any]           =	self.fwd_bfs.retrace_path(A     )
         snake_case				:    Optional[Any]           =	self.bwd_bfs.retrace_path(A     )
         bwd_path.pop()
         bwd_path.reverse()
         snake_case				:    int           =	fwd_path + bwd_path
         return path


if __name__ == "__main__":
       # all coordinates are given in format [y,x]
       import doctest

       doctest.testmod()
       lowerCamelCase	:	List[str]				=   (0, 0)
       lowerCamelCase	:	Optional[int]				=   (len(grid) - 1, len(grid[0]) - 1)
       for elem in grid:
              print(elem)

       lowerCamelCase	:	Optional[Any]				=   time.time()
       lowerCamelCase	:	str				=   BreadthFirstSearch(init, goal)
       lowerCamelCase	:	Optional[int]				=   bfs.search()
       lowerCamelCase	:	Dict				=   time.time() - start_bfs_time

       print('Unidirectional BFS computation time : ', bfs_time)

       lowerCamelCase	:	Optional[int]				=   time.time()
       lowerCamelCase	:	List[str]				=   BidirectionalBreadthFirstSearch(init, goal)
       lowerCamelCase	:	int				=   bd_bfs.search()
       lowerCamelCase	:	List[str]				=   time.time() - start_bd_bfs_time

       print('Bidirectional BFS computation time : ', bd_bfs_time)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	str				=   {
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[Any]				=   ['RoFormerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RoFormerForCausalLM',
           'RoFormerForMaskedLM',
           'RoFormerForMultipleChoice',
           'RoFormerForQuestionAnswering',
           'RoFormerForSequenceClassification',
           'RoFormerForTokenClassification',
           'RoFormerLayer',
           'RoFormerModel',
           'RoFormerPreTrainedModel',
           'load_tf_weights_in_roformer',
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRoFormerForCausalLM',
           'TFRoFormerForMaskedLM',
           'TFRoFormerForMultipleChoice',
           'TFRoFormerForQuestionAnswering',
           'TFRoFormerForSequenceClassification',
           'TFRoFormerForTokenClassification',
           'TFRoFormerLayer',
           'TFRoFormerModel',
           'TFRoFormerPreTrainedModel',
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Union[str, Any]				=   [
           'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'FlaxRoFormerForMaskedLM',
           'FlaxRoFormerForMultipleChoice',
           'FlaxRoFormerForQuestionAnswering',
           'FlaxRoFormerForSequenceClassification',
           'FlaxRoFormerForTokenClassification',
           'FlaxRoFormerModel',
           'FlaxRoFormerPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
       from .tokenization_roformer import RoFormerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roformer_fast import RoFormerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roformer import (
                  ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RoFormerForCausalLM,
                  RoFormerForMaskedLM,
                  RoFormerForMultipleChoice,
                  RoFormerForQuestionAnswering,
                  RoFormerForSequenceClassification,
                  RoFormerForTokenClassification,
                  RoFormerLayer,
                  RoFormerModel,
                  RoFormerPreTrainedModel,
                  load_tf_weights_in_roformer,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roformer import (
                  TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRoFormerForCausalLM,
                  TFRoFormerForMaskedLM,
                  TFRoFormerForMultipleChoice,
                  TFRoFormerForQuestionAnswering,
                  TFRoFormerForSequenceClassification,
                  TFRoFormerForTokenClassification,
                  TFRoFormerLayer,
                  TFRoFormerModel,
                  TFRoFormerPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roformer import (
                  FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaxRoFormerForMaskedLM,
                  FlaxRoFormerForMultipleChoice,
                  FlaxRoFormerForQuestionAnswering,
                  FlaxRoFormerForSequenceClassification,
                  FlaxRoFormerForTokenClassification,
                  FlaxRoFormerModel,
                  FlaxRoFormerPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase	:	Optional[int]				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





