
'''simple docstring'''

from typing import Mapping

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


UpperCAmelCase__ :			Tuple											=		logging.get_logger(__name__)

UpperCAmelCase__ :			List[str]											=		{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :Union[str, Any]   =  'umt5'
	snake_case__   :Any   =  ['past_key_values']





	def __init__(       self      :							List[Any]   ,				__magic_name__      :							Tuple=250112   ,				__magic_name__      :							str=512   ,				__magic_name__      :							int=64   ,				__magic_name__      :							str=1024   ,				__magic_name__      :							Tuple=8   ,				__magic_name__      :							Optional[int]=None   ,				__magic_name__      :							Optional[Any]=6   ,				__magic_name__      :							Dict=32   ,				__magic_name__      :							Optional[Any]=128   ,				__magic_name__      :							Union[str, Any]=0.1   ,				__magic_name__      :							int=1E-6   ,				__magic_name__      :							Optional[int]=1.0   ,				__magic_name__      :							Dict="gated-gelu"   ,				__magic_name__      :							List[str]=True   ,				__magic_name__      :							Tuple=True   ,				__magic_name__      :							Optional[int]="T5Tokenizer"   ,				__magic_name__      :							str=True   ,				__magic_name__      :							int=0   ,				__magic_name__      :							Union[str, Any]=1   ,				__magic_name__      :							str=0   ,				**__magic_name__      :							Any   ,				):


				"""simple docstring"""

				super().__init__(
				    is_encoder_decoder=__magic_name__   ,				tokenizer_class=__magic_name__   ,				tie_word_embeddings=__magic_name__   ,				pad_token_id=__magic_name__   ,				eos_token_id=__magic_name__   ,				decoder_start_token_id=__magic_name__   ,				**__magic_name__   ,				)
				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				d_model
				lowerCAmelCase__										=				d_kv
				lowerCAmelCase__										=				d_ff
				lowerCAmelCase__										=				num_layers
				lowerCAmelCase__										=				(
				    num_decoder_layers if num_decoder_layers is not None else self.num_layers
				)  # default = symmetry
				lowerCAmelCase__										=				num_heads
				lowerCAmelCase__										=				relative_attention_num_buckets
				lowerCAmelCase__										=				relative_attention_max_distance
				lowerCAmelCase__										=				dropout_rate
				lowerCAmelCase__										=				layer_norm_epsilon
				lowerCAmelCase__										=				initializer_factor
				lowerCAmelCase__										=				feed_forward_proj
				lowerCAmelCase__										=				use_cache

				lowerCAmelCase__										=				self.feed_forward_proj.split("-"				)
				lowerCAmelCase__										=				act_info[-1]
				lowerCAmelCase__										=				act_info[0] == "gated"

				if len(__magic_name__				) > 1 and act_info[0] != "gated" or len(__magic_name__				) > 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'"				)

				if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__										=				"gelu_new"





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]				):


				"""simple docstring"""

				return self.d_model





	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Union[str, Any]				):


				"""simple docstring"""

				return self.num_heads







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				return self.num_layers






class  A  (						SCREAMING_SNAKE_CASE__     ):





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				{
				    "input_ids": {0: "batch", 1: "encoder_sequence"},
				    "attention_mask": {0: "batch", 1: "encoder_sequence"},
				}
				if self.use_past:
							lowerCAmelCase__										=				"past_encoder_sequence + sequence"
							lowerCAmelCase__										=				{0: "batch"}
							lowerCAmelCase__										=				{0: "batch", 1: "past_decoder_sequence + sequence"}
				else:
							lowerCAmelCase__										=				{0: "batch", 1: "decoder_sequence"}
							lowerCAmelCase__										=				{0: "batch", 1: "decoder_sequence"}

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

				return common_inputs





	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return 13







	@property
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				return 5E-4









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

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		list[tuple[int, int]]

__SCREAMING_SNAKE_CASE					:				Union[str, 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],
]

__SCREAMING_SNAKE_CASE					:				Optional[int]									=		([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      List[str] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      float ,  lowerCAmelCase							:      Node | None ,  ):
													A_			    =		pos_x
													A_			    =		pos_y
													A_			    =		(pos_y, pos_x)
													A_			    =		goal_x
													A_			    =		goal_y
													A_			    =		g_cost
													A_			    =		parent
													A_			    =		self.calculate_heuristic()






						def 				_UpperCAmelCase       (  self							:      Tuple	):
													A_			    =		abs(self.pos_x - self.goal_x	)
													A_			    =		abs(self.pos_y - self.goal_y	)
													return dx + dy






						def __lt__(  self							:      Tuple ,  lowerCAmelCase							:      Any	):
													return self.f_cost < other.f_cost






class 					__lowerCAmelCase   :

						"""simple docstring"""






						def __init__(  self							:      Union[str, Any] ,  lowerCAmelCase							:      tuple[int, int] ,  lowerCAmelCase							:      tuple[int, int]	):
													A_			    =		Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  lowerCAmelCase	)
													A_			    =		Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  9_99_99 ,  lowerCAmelCase	)

													A_			    =		[self.start]
													A_			    =		[]

													A_			    =		False






						def 				_UpperCAmelCase       (  self							:      str	):
													while self.open_nodes:
																				# Open Nodes are sorted using __lt__
																				self.open_nodes.sort()
																				A_			    =		self.open_nodes.pop(0	)

																				if current_node.pos == self.target.pos:
																											A_			    =		True
																											return self.retrace_path(lowerCAmelCase	)

																				self.closed_nodes.append(lowerCAmelCase	)
																				A_			    =		self.get_successors(lowerCAmelCase	)

																				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(lowerCAmelCase	)
																											else:
																																		# retrieve the best current path
																																		A_			    =		self.open_nodes.pop(self.open_nodes.index(lowerCAmelCase	)	)

																																		if child_node.g_cost < better_node.g_cost:
																																									self.open_nodes.append(lowerCAmelCase	)
																																		else:
																																									self.open_nodes.append(lowerCAmelCase	)

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






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      Node	):
													A_			    =		[]
													for action in delta:
																				A_			    =		parent.pos_x + action[1]
																				A_			    =		parent.pos_y + action[0]

																				if not (0 <= pos_x <= len(grid[0]	) - 1 and 0 <= pos_y <= len(lowerCAmelCase	) - 1):
																											continue

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

																				successors.append(
																				    Node(
																				        lowerCAmelCase ,  lowerCAmelCase ,  self.target.pos_y ,  self.target.pos_x ,  parent.g_cost + 1 ,  lowerCAmelCase ,  )	)
													return successors






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Node | None	):
													A_			    =		node
													A_			    =		[]
													while current_node is not None:
																				path.append((current_node.pos_y, current_node.pos_x)	)
																				A_			    =		current_node.parent
													path.reverse()
													return path


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

							print('''------''')

							__SCREAMING_SNAKE_CASE					:				int									=		GreedyBestFirst(init, goal)
							__SCREAMING_SNAKE_CASE					:				Optional[Any]									=		greedy_bf.search()
							if path:
														for pos_x, pos_y in path:
																					__SCREAMING_SNAKE_CASE					:				List[Any]									=		2

														for elem in grid:
																					print(elem)





