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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Optional[Any]   							=  'gpt_neo'
							lowercase_		:						Any   							=  ['past_key_values']
							lowercase_		:						Dict   							=  {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







							def __init__(						self  :  int      ,				snake_case__  :  List[str]=50_257      ,				snake_case__  :  Optional[int]=2_048      ,				snake_case__  :  Optional[Any]=2_048      ,				snake_case__  :  Dict=24      ,				snake_case__  :  int=[[["global", "local"], 12]]      ,				snake_case__  :  Union[str, Any]=16      ,				snake_case__  :  List[Any]=None      ,				snake_case__  :  List[str]=256      ,				snake_case__  :  Tuple="gelu_new"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Dict=0.0      ,				snake_case__  :  Union[str, Any]=0.0      ,				snake_case__  :  Union[str, Any]=0.1      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=True      ,				snake_case__  :  int=50_256      ,				snake_case__  :  int=50_256      ,				**snake_case__  :  List[Any]      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_layers
								__lowerCAmelCase         =							num_heads
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							window_size
								__lowerCAmelCase         =							activation_function
								__lowerCAmelCase         =							resid_dropout
								__lowerCAmelCase         =							embed_dropout
								__lowerCAmelCase         =							attention_dropout
								__lowerCAmelCase         =							classifier_dropout
								__lowerCAmelCase         =							layer_norm_epsilon
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							use_cache

								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id

								__lowerCAmelCase         =							attention_types
								__lowerCAmelCase         =							self.expand_attention_types_params(snake_case__ )

								if len(self.attention_layers ) != self.num_layers:
									raise ValueError(
									    "Configuration for convolutional module is incorrect. "
									    "It is required that `len(config.attention_layers)` == `config.num_layers` "
									    F"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
									    F"`config.num_layers = {self.num_layers}`. "
									    "`config.attention_layers` is prepared using `config.attention_types`. "
									    "Please verify the value of `config.attention_types` argument." )

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







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							[]
								for item in attention_types:
									for _ in range(item[1] ):
										attentions.extend(item[0] )
								return attentions






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     Any			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	import torch

	__lowerCAmelCase         =							input.size()
	__lowerCAmelCase         =							len(UpperCamelCase  )
	__lowerCAmelCase         =							shape[dimension]

	__lowerCAmelCase         =							torch.arange(0			,							UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							torch.div(sizedim - size			,							UpperCamelCase			,							rounding_mode="floor"  ) + 1
	__lowerCAmelCase         =							torch.arange(UpperCamelCase  ) + low_indices[:min_length][:, None]

	__lowerCAmelCase         =							[slice(UpperCamelCase  )] * rank
	__lowerCAmelCase         =							indices
	__lowerCAmelCase         =							input[s]

	__lowerCAmelCase         =							list(range(0			,							rank + 1  )  )
	perm.append(perm.pop(dimension + 1  )  )

	return sliced.permute(UpperCamelCase  )






def 		_UpperCAmelCase   (	UpperCamelCase:     Dict			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


	import torch

	__lowerCAmelCase         =							torch.arange(1			,							UpperCamelCase  )
	__lowerCAmelCase         =							torch.remainder(UpperCamelCase			,							UpperCamelCase  )
	__lowerCAmelCase         =							remainders == 0
	__lowerCAmelCase         =							candidates[divisor_indices]
	__lowerCAmelCase         =							torch.max(UpperCamelCase  )
	return largest_divisor, torch.div(UpperCamelCase			,							UpperCamelCase			,							rounding_mode="floor"  )







class       a		(   __UpperCAmelCase					):







							@property
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
								if self.use_past:
									self.fill_with_past_key_values_(snake_case__      ,				direction="inputs" )
									__lowerCAmelCase         =							{0: "batch", 1: "past_sequence + sequence"}
								else:
									__lowerCAmelCase         =							{0: "batch", 1: "sequence"}

								return common_inputs







							@property
							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								return self._config.num_heads







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  PreTrainedTokenizer      ,				snake_case__  :  int = -1      ,				snake_case__  :  int = -1      ,				snake_case__  :  bool = False      ,				snake_case__  :  Optional[TensorType] = None      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							super(snake_case__      ,				self ).generate_dummy_inputs(
								    snake_case__      ,				batch_size=snake_case__      ,				seq_length=snake_case__      ,				is_pair=snake_case__      ,				framework=snake_case__ )

								# We need to order the input in the way they appears in the forward()
								__lowerCAmelCase         =							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

										__lowerCAmelCase   ,						__lowerCAmelCase         =							common_inputs["input_ids"].shape
										# Not using the same length for past_key_values
										__lowerCAmelCase         =							seqlen + 2
										__lowerCAmelCase         =							(
										    batch,
										    self.num_attention_heads,
										    past_key_values_length,
										    self._config.hidden_size // self.num_attention_heads,
										)
										__lowerCAmelCase         =							[
										    (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(self.num_layers )
										]

								__lowerCAmelCase         =							common_inputs["attention_mask"]
								if self.use_past:
									__lowerCAmelCase         =							ordered_inputs["attention_mask"].dtype
									__lowerCAmelCase         =							torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(snake_case__      ,				snake_case__      ,				dtype=snake_case__ )]      ,				dim=1 )

								return ordered_inputs







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 13








from __future__ import annotations

UpperCamelCase_   	=	list[tuple[int, int]]

UpperCamelCase_   	=	[
    [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],
]

UpperCamelCase_   	=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right

class       a		:







							def __init__(						self  :  Optional[Any]      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  int      ,				snake_case__  :  float      ,				snake_case__  :  Node | None      ,				):
								"""simple docstring"""







								__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       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							abs(self.pos_x - self.goal_x )
								__lowerCAmelCase         =							abs(self.pos_y - self.goal_y )
								return dx + dy







							def __lt__(						self  :  Optional[int]      ,				snake_case__  :  int ):
								"""simple docstring"""







								return self.f_cost < other.f_cost

class       a		:







							def __init__(						self  :  Dict      ,				snake_case__  :  tuple[int, int]      ,				snake_case__  :  tuple[int, int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							Node(start[1]      ,				start[0]      ,				goal[1]      ,				goal[0]      ,				0      ,				snake_case__ )
								__lowerCAmelCase         =							Node(goal[1]      ,				goal[0]      ,				goal[1]      ,				goal[0]      ,				99_999      ,				snake_case__ )

								__lowerCAmelCase         =							[self.start]
								__lowerCAmelCase         =							[]

								__lowerCAmelCase         =							False







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								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(snake_case__ )

									self.closed_nodes.append(snake_case__ )
									__lowerCAmelCase         =							self.get_successors(snake_case__ )

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

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

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







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Node ):
								"""simple docstring"""







								__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(snake_case__ ) - 1):
										continue

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

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







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Node | None ):
								"""simple docstring"""







								__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__":
					UpperCamelCase_   	=	(0, 0)
					UpperCamelCase_   	=	(len(grid) - 1, len(grid[0]) - 1)
					for elem in grid:
										print(elem)

					print("------")

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

										for elem in grid:
															print(elem)






