



"""simple docstring"""







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       _a  :



			def __init__(			self							:				str   ,	_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				int   ,	_lowercase							:				float   ,	_lowercase							:				Node | None   ,	)							->					List[str]:
									snake_case   :			Optional[int]						    = pos_x
									snake_case   :			str						    = pos_y
									snake_case   :			Optional[int]						    = (pos_y, pos_x)
									snake_case   :			Any						    = goal_x
									snake_case   :			int						    = goal_y
									snake_case   :			str						    = g_cost
									snake_case   :			Tuple						    = parent
									snake_case   :			Any						    = self.calculate_heuristic()



			def 				__lowercase					(			self							:				List[Any]		)							->					float:
									snake_case   :			int						    = abs(self.pos_x - self.goal_x		)
									snake_case   :			List[str]						    = abs(self.pos_y - self.goal_y		)
									return dx + dy



			def __lt__(			self							:				List[str]   ,	_lowercase							:				str		)							->					bool:
									return self.f_cost < other.f_cost



class       _a  :



			def __init__(			self							:				List[str]   ,	_lowercase							:				tuple[int, int]   ,	_lowercase							:				tuple[int, int]		)							->					Optional[Any]:
									snake_case   :			Tuple						    = Node(start[1]   ,	start[0]   ,	goal[1]   ,	goal[0]   ,	0   ,	_lowercase		)
									snake_case   :			int						    = Node(goal[1]   ,	goal[0]   ,	goal[1]   ,	goal[0]   ,	99999   ,	_lowercase		)

									snake_case   :			str						    = [self.start]
									snake_case   :			list[Node]						    = []

									snake_case   :			Dict						    = False



			def 				__lowercase					(			self							:				Any		)							->					Path | None:
									while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															snake_case   :			Optional[int]						    = self.open_nodes.pop(0		)

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

															self.closed_nodes.append(_lowercase		)
															snake_case   :			Any						    = 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
																											snake_case   :			Optional[int]						    = 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 				__lowercase					(			self							:				Optional[int]   ,	_lowercase							:				Node		)							->					list[Node]:
									snake_case   :			int						    = []
									for action in delta:
															snake_case   :			Dict						    = 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(_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 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				Node | None		)							->					Path:
									snake_case   :			Tuple						    = node
									snake_case   :			List[Any]						    = []
									while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															snake_case   :			Optional[Any]						    = 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)








"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


A		=						logging.get_logger(__name__)

A		=						{
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """bloom"""
			__magic_name__     					=     ["""past_key_values"""]
			__magic_name__     					=     {
			    """num_hidden_layers""": """n_layer""",
			    """num_attention_heads""": """n_head""",
			}



			def __init__(			self							:				Tuple   ,	_lowercase							:				List[Any]=250880   ,	_lowercase							:				int=64   ,	_lowercase							:				Optional[Any]=2   ,	_lowercase							:				Dict=8   ,	_lowercase							:				List[str]=1E-5   ,	_lowercase							:				List[Any]=0.02   ,	_lowercase							:				Optional[Any]=True   ,	_lowercase							:				Dict=1   ,	_lowercase							:				Union[str, Any]=2   ,	_lowercase							:				str=False   ,	_lowercase							:				List[Any]=0.0   ,	_lowercase							:				Tuple=0.0   ,	_lowercase							:				Dict=1   ,	_lowercase							:				int=False   ,	**_lowercase							:				int   ,	)							->					List[str]:
									snake_case   :			Any						    = vocab_size
									# Backward compatibility with n_embed kwarg
									snake_case   :			Any						    = kwargs.pop("n_embed"   ,	_lowercase		)
									snake_case   :			Tuple						    = hidden_size if n_embed is None else n_embed
									snake_case   :			Optional[Any]						    = n_layer
									snake_case   :			Optional[Any]						    = n_head
									snake_case   :			Union[str, Any]						    = layer_norm_epsilon
									snake_case   :			int						    = initializer_range
									snake_case   :			int						    = use_cache
									snake_case   :			int						    = pretraining_tp
									snake_case   :			Tuple						    = apply_residual_connection_post_layernorm
									snake_case   :			Union[str, Any]						    = hidden_dropout
									snake_case   :			Optional[Any]						    = attention_dropout

									snake_case   :			List[Any]						    = bos_token_id
									snake_case   :			Any						    = eos_token_id
									snake_case   :			Optional[Any]						    = slow_but_exact

									super().__init__(bos_token_id=_lowercase   ,	eos_token_id=_lowercase   ,	**_lowercase		)



class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     version.parse("""1.12""")



			def __init__(			self							:				int   ,	_lowercase							:				PretrainedConfig   ,	_lowercase							:				str = "default"   ,	_lowercase							:				List[PatchingSpec] = None   ,	_lowercase							:				bool = False   ,	)							->					Dict:
									super().__init__(_lowercase   ,	task=_lowercase   ,	patching_specs=_lowercase   ,	use_past=_lowercase		)
									if not getattr(self._config   ,	"pad_token_id"   ,	_lowercase		):
															# TODO: how to do that better?
															snake_case   :			int						    = 0



			@property
			def 				__lowercase					(			self							:				str		)							->					Mapping[str, Mapping[int, str]]:
									snake_case   :			int						    = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
									if self.use_past:
															# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
															self.fill_with_past_key_values_(_lowercase   ,	direction="inputs"   ,	inverted_values_shape=_lowercase		)
															snake_case   :			Optional[int]						    = {0: "batch", 1: "past_sequence + sequence"}
									else:
															snake_case   :			Dict						    = {0: "batch", 1: "sequence"}

									return common_inputs



			@property
			def 				__lowercase					(			self							:				int		)							->					int:
									return self._config.n_layer



			@property
			def 				__lowercase					(			self							:				Dict		)							->					int:
									return self._config.n_head



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					float:
									return 1E-3



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				"PreTrainedTokenizer"   ,	_lowercase							:				int = -1   ,	_lowercase							:				int = -1   ,	_lowercase							:				bool = False   ,	_lowercase							:				Optional["TensorType"] = None   ,	)							->					Mapping[str, Any]:
									snake_case   :			int						    = super(_lowercase   ,	self		).generate_dummy_inputs(
									    _lowercase   ,	batch_size=_lowercase   ,	seq_length=_lowercase   ,	is_pair=_lowercase   ,	framework=_lowercase		)

									# We need to order the input in the way they appears in the forward()
									snake_case   :			Any						    = OrderedDict({"input_ids": common_inputs["input_ids"]}		)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
															else:
																					import torch

																					snake_case    ,			snake_case   :			List[str]						    = common_inputs["input_ids"].shape
																					# Not using the same length for past_key_values
																					snake_case   :			Optional[Any]						    = seqlen + 2
																					snake_case   :			Optional[int]						    = self._config.hidden_size // self.num_attention_heads
																					snake_case   :			List[str]						    = (
																					    batch * self.num_attention_heads,
																					    head_dim,
																					    past_key_values_length,
																					)
																					snake_case   :			Any						    = (
																					    batch * self.num_attention_heads,
																					    past_key_values_length,
																					    head_dim,
																					)
																					snake_case   :			Dict						    = [
																					    (torch.zeros(_lowercase		), torch.zeros(_lowercase		)) for _ in range(self.num_layers		)
																					]

									snake_case   :			Tuple						    = common_inputs["attention_mask"]
									if self.use_past:
															snake_case   :			Any						    = ordered_inputs["attention_mask"].dtype
															snake_case   :			List[str]						    = torch.cat(
															    [ordered_inputs["attention_mask"], torch.ones(_lowercase   ,	_lowercase   ,	dtype=_lowercase		)]   ,	dim=1		)

									return ordered_inputs



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



