

from __future__ import annotations

import time

a		=list[tuple[int, int]]

a		=[
    [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],
]

a		=[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right






class        A_		:
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				Tuple					       = pos_x
								__lowerCamelCase				:				List[str]					       = pos_y
								__lowerCamelCase				:				str					       = (pos_y, pos_x)
								__lowerCamelCase				:				str					       = goal_x
								__lowerCamelCase				:				int					       = goal_y
								__lowerCamelCase				:				List[Any]					       = parent






class        A_		:
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]   ,SCREAMING_SNAKE_CASE__		:			tuple[int, int]):
								__lowerCamelCase				:				Any					       = Node(start[1]   ,start[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = Node(goal[1]   ,goal[0]   ,goal[1]   ,goal[0]   ,SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Union[str, Any]					       = [self.start]
								__lowerCamelCase				:				List[str]					       = False
							def     lowerCAmelCase     (		self		:			List[Any]):
								while self.node_queue:
									__lowerCamelCase				:				Any					       = self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
										__lowerCamelCase				:				Dict					       = True
										return self.retrace_path(SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				Tuple					       = self.get_successors(SCREAMING_SNAKE_CASE__)

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

								if not self.reached:
									return [self.start.pos]
								return None
							def     lowerCAmelCase     (		self		:			str   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				Union[str, Any]					       = []
								for action in delta:
									__lowerCamelCase				:				Optional[Any]					       = parent.pos_x + action[1]
									__lowerCamelCase				:				Optional[int]					       = parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(SCREAMING_SNAKE_CASE__) - 1):
										continue

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

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


							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Node | None):
								__lowerCamelCase				:				List[Any]					       = node
								__lowerCamelCase				:				int					       = []
								while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									__lowerCamelCase				:				int					       = current_node.parent
								path.reverse()
								return path






class        A_		:
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				int					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = BreadthFirstSearch(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[Any]					       = False
							def     lowerCAmelCase     (		self		:			str):
								while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									__lowerCamelCase				:				Any					       = self.fwd_bfs.node_queue.pop(0)
									__lowerCamelCase				:				Any					       = self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
										__lowerCamelCase				:				List[str]					       = True
										return self.retrace_bidirectional_path(
										    SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase				:				str					       = {
									    self.fwd_bfs: self.fwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									    self.bwd_bfs: self.bwd_bfs.get_successors(SCREAMING_SNAKE_CASE__),
									}

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

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


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Node   ,SCREAMING_SNAKE_CASE__		:			Node):
								__lowerCamelCase				:				List[Any]					       = self.fwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				str					       = self.bwd_bfs.retrace_path(SCREAMING_SNAKE_CASE__)
								bwd_path.pop()
								bwd_path.reverse()
								__lowerCamelCase				:				List[Any]					       = fwd_path + bwd_path
								return path


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

					doctest.testmod()
					a		=(0, 0)
					a		=(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					a		=time.time()
					a		=BreadthFirstSearch(init, goal)
					a		=bfs.search()
					a		=time.time() - start_bfs_time

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

					a		=time.time()
					a		=BidirectionalBreadthFirstSearch(init, goal)
					a		=bd_bfs.search()
					a		=time.time() - start_bd_bfs_time

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












from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a		:       Tuple			     = logging.get_logger(__name__)

a		:       Dict			     = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : int							       =	't5'
      a  : Dict							       =	['past_key_values']
      a  : Tuple							       =	{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
      def __init__(      self					:			str		,      __lowercase					:			Optional[int]=32128		,      __lowercase					:			Optional[int]=512		,      __lowercase					:			int=64		,      __lowercase					:			Any=2048		,      __lowercase					:			Tuple=6		,      __lowercase					:			Tuple=None		,      __lowercase					:			int=8		,      __lowercase					:			List[Any]=32		,      __lowercase					:			Dict=128		,      __lowercase					:			Optional[int]=0.1		,      __lowercase					:			int=1e-6		,      __lowercase					:			List[str]=1.0		,      __lowercase					:			List[str]="relu"		,      __lowercase					:			Dict=True		,      __lowercase					:			Optional[Any]=True		,      __lowercase					:			Tuple=0		,      __lowercase					:			List[str]=1		,      **__lowercase					:			Any		,      )							->      str:
        __UpperCAmelCase							:	Tuple           =  vocab_size
        __UpperCAmelCase							:	Optional[int]           =  d_model
        __UpperCAmelCase							:	Union[str, Any]           =  d_kv
        __UpperCAmelCase							:	Union[str, Any]           =  d_ff
        __UpperCAmelCase							:	int           =  num_layers
        __UpperCAmelCase							:	Any           =  (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        __UpperCAmelCase							:	Dict           =  num_heads
        __UpperCAmelCase							:	List[Any]           =  relative_attention_num_buckets
        __UpperCAmelCase							:	List[str]           =  relative_attention_max_distance
        __UpperCAmelCase							:	Union[str, Any]           =  dropout_rate
        __UpperCAmelCase							:	List[str]           =  layer_norm_epsilon
        __UpperCAmelCase							:	str           =  initializer_factor
        __UpperCAmelCase							:	Dict           =  feed_forward_proj
        __UpperCAmelCase							:	Optional[int]           =  use_cache

        __UpperCAmelCase							:	List[Any]           =  self.feed_forward_proj.split("""-"""					)
        __UpperCAmelCase							:	Tuple           =  act_info[-1]
        __UpperCAmelCase							:	int           =  act_info[0] == """gated"""

        if len(__lowercase					) > 1 and act_info[0] != "gated" or len(__lowercase					) > 2:
          raise ValueError(
              f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
              """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
              """'gated-gelu' or 'relu'"""					)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
          __UpperCAmelCase							:	Dict           =  """gelu_new"""

        super().__init__(
            pad_token_id=__lowercase		,      eos_token_id=__lowercase		,      is_encoder_decoder=__lowercase		,      **__lowercase		,      )


class     a    ( lowercase__   ):



      """simple docstring"""


      @property
      def 	UpperCAmelCase      (      self					:			str					)							->      Mapping[str, Mapping[int, str]]:
        __UpperCAmelCase							:	Union[str, Any]           =  {
            """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
            """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
        }
        if self.use_past:
          __UpperCAmelCase							:	List[Any]           =  """past_encoder_sequence + sequence"""
          __UpperCAmelCase							:	Optional[int]           =  {0: """batch"""}
          __UpperCAmelCase							:	Union[str, Any]           =  {0: """batch""", 1: """past_decoder_sequence + sequence"""}
        else:
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}
          __UpperCAmelCase							:	str           =  {0: """batch""", 1: """decoder_sequence"""}

        if self.use_past:
          self.fill_with_past_key_values_(__lowercase		,      direction="""inputs"""					)

        return common_inputs




      @property
      def 	UpperCAmelCase      (      self					:			int					)							->      int:
        return 13


