
from collections import OrderedDict
from typing import Mapping

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


_A     =  logging.get_logger(__name__)

_A     =  {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	='''bert'''




				def __init__(      self       ,       _UpperCamelCase=30_522       ,       _UpperCamelCase=768       ,       _UpperCamelCase=12       ,       _UpperCamelCase=12       ,       _UpperCamelCase=3_072       ,       _UpperCamelCase="gelu"       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=0.1       ,       _UpperCamelCase=512       ,       _UpperCamelCase=2       ,       _UpperCamelCase=0.02       ,       _UpperCamelCase=1e-1_2       ,       _UpperCamelCase=0       ,       _UpperCamelCase="absolute"       ,       _UpperCamelCase=True       ,       _UpperCamelCase=None       ,       **_UpperCamelCase       ,       )       ->   str:
							super().__init__(pad_token_id=_UpperCamelCase       ,       **_UpperCamelCase  )

							lowerCAmelCase_      			=			vocab_size
							lowerCAmelCase_      			=			hidden_size
							lowerCAmelCase_      			=			num_hidden_layers
							lowerCAmelCase_      			=			num_attention_heads
							lowerCAmelCase_      			=			hidden_act
							lowerCAmelCase_      			=			intermediate_size
							lowerCAmelCase_      			=			hidden_dropout_prob
							lowerCAmelCase_      			=			attention_probs_dropout_prob
							lowerCAmelCase_      			=			max_position_embeddings
							lowerCAmelCase_      			=			type_vocab_size
							lowerCAmelCase_      			=			initializer_range
							lowerCAmelCase_      			=			layer_norm_eps
							lowerCAmelCase_      			=			position_embedding_type
							lowerCAmelCase_      			=			use_cache
							lowerCAmelCase_      			=			classifier_dropout





class 			_lowerCAmelCase      (			__a ):




				@property
				def   __a		(      self  )       ->   Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_      			=			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_      			=			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ]  )





from __future__ import annotations

_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 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   Tuple:
							lowerCAmelCase_      			=			pos_x
							lowerCAmelCase_      			=			pos_y
							lowerCAmelCase_      			=			(pos_y, pos_x)
							lowerCAmelCase_      			=			goal_x
							lowerCAmelCase_      			=			goal_y
							lowerCAmelCase_      			=			g_cost
							lowerCAmelCase_      			=			parent
							lowerCAmelCase_      			=			self.calculate_heuristic()




				def   __a		(      self  )       ->   float:
							lowerCAmelCase_      			=			abs(self.pos_x - self.goal_x  )
							lowerCAmelCase_      			=			abs(self.pos_y - self.goal_y  )
							return dx + dy




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





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:
							lowerCAmelCase_      			=			Node(start[1]       ,       start[0]       ,       goal[1]       ,       goal[0]       ,       0       ,       _UpperCamelCase  )
							lowerCAmelCase_      			=			Node(goal[1]       ,       goal[0]       ,       goal[1]       ,       goal[0]       ,       99_999       ,       _UpperCamelCase  )

							lowerCAmelCase_      			=			[self.start]
							lowerCAmelCase_      			=			[]

							lowerCAmelCase_      			=			False




				def   __a		(      self  )       ->   Path | None:
							while self.open_nodes:
										# Open Nodes are sorted using __lt__
										self.open_nodes.sort()
										lowerCAmelCase_      			=			self.open_nodes.pop(0  )

										if current_node.pos == self.target.pos:
													lowerCAmelCase_      			=			True
													return self.retrace_path(_UpperCamelCase  )

										self.closed_nodes.append(_UpperCamelCase  )
										lowerCAmelCase_      			=			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
																lowerCAmelCase_      			=			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  )

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




				def   __a		(      self       ,       _UpperCamelCase  )       ->   list[Node]:
							lowerCAmelCase_      			=			[]
							for action in delta:
										lowerCAmelCase_      			=			parent.pos_x + action[1]
										lowerCAmelCase_      			=			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  )       ->   Path:
							lowerCAmelCase_      			=			node
							lowerCAmelCase_      			=			[]
							while current_node is not None:
										path.append((current_node.pos_y, current_node.pos_x)  )
										lowerCAmelCase_      			=			current_node.parent
							path.reverse()
							return path


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

			print("------")

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

						for elem in grid:
									print(elem)



