
"""simple docstring"""

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						=			{
    'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/config.json',
    'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/config.json',
    'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/config.json',
    'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/config.json',
    'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json',
    'roberta-large-openai-detector': 'https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json',
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'roberta'




	def __init__(			self		,			UpperCAmelCase_=50265		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_="gelu"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=512		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=1		,			UpperCAmelCase_=0		,			UpperCAmelCase_=2		,			UpperCAmelCase_="absolute"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Tuple:
			super().__init__(pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			**UpperCAmelCase_ )

			lowerCamelCase					:      List[Any]          =   vocab_size
			lowerCamelCase					:      List[str]          =   hidden_size
			lowerCamelCase					:      Dict          =   num_hidden_layers
			lowerCamelCase					:      str          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   hidden_act
			lowerCamelCase					:      Any          =   intermediate_size
			lowerCamelCase					:      List[Any]          =   hidden_dropout_prob
			lowerCamelCase					:      List[str]          =   attention_probs_dropout_prob
			lowerCamelCase					:      Dict          =   max_position_embeddings
			lowerCamelCase					:      Any          =   type_vocab_size
			lowerCamelCase					:      str          =   initializer_range
			lowerCamelCase					:      Tuple          =   layer_norm_eps
			lowerCamelCase					:      Any          =   position_embedding_type
			lowerCamelCase					:      Dict          =   use_cache
			lowerCamelCase					:      List[str]          =   classifier_dropout






class 	_lowercase       (    __UpperCAmelCase		):




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








"""simple docstring"""

from __future__ import annotations

_A						=			[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the reference grid
		lowerCamelCase					:      Union[str, Any]          =   1
		lowerCamelCase					:      Any          =   [
		    [0 for col in range(len(grid[0]      )      )] for row in range(len(a_      )      )
		]  # the action grid

		lowerCamelCase					:      List[str]          =   init[0]
		lowerCamelCase					:      Optional[Any]          =   init[1]
		lowerCamelCase					:      List[Any]          =   0
		lowerCamelCase					:      List[str]          =   g + heuristic[x][y]  # cost from starting cell to destination cell
		lowerCamelCase					:      Union[str, Any]          =   [[f, g, x, y]]

		lowerCamelCase					:      Union[str, Any]          =   False  # flag that is set when search is complete
		lowerCamelCase					:      str          =   False  # flag set if we can't find expand

		while not found and not resign:
				if len(a_      ) == 0:
						raise ValueError('Algorithm is unable to find solution'      )
				else:  # to choose the least costliest action so as to move closer to the goal
						cell.sort()
						cell.reverse()
						lowerCamelCase					:      int          =   cell.pop()
						lowerCamelCase					:      str          =   next_cell[2]
						lowerCamelCase					:      Union[str, Any]          =   next_cell[3]
						lowerCamelCase					:      List[str]          =   next_cell[1]

						if x == goal[0] and y == goal[1]:
								lowerCamelCase					:      Any          =   True
						else:
								for i in range(len(a_      )      ):  # to try out different valid actions
										lowerCamelCase					:      Tuple          =   x + DIRECTIONS[i][0]
										lowerCamelCase					:      Union[str, Any]          =   y + DIRECTIONS[i][1]
										if xa >= 0 and xa < len(a_      ) and ya >= 0 and ya < len(grid[0]      ):
												if closed[xa][ya] == 0 and grid[xa][ya] == 0:
														lowerCamelCase					:      str          =   g + cost
														lowerCamelCase					:      Tuple          =   ga + heuristic[xa][ya]
														cell.append([fa, ga, xa, ya]      )
														lowerCamelCase					:      Union[str, Any]          =   1
														lowerCamelCase					:      Any          =   i
		lowerCamelCase					:      Any          =   []
		lowerCamelCase					:      Optional[int]          =   goal[0]
		lowerCamelCase					:      Dict          =   goal[1]
		invpath.append([x, y]      )  # we get the reverse path from here
		while x != init[0] or y != init[1]:
				lowerCamelCase					:      Dict          =   x - DIRECTIONS[action[x][y]][0]
				lowerCamelCase					:      Dict          =   y - DIRECTIONS[action[x][y]][1]
				lowerCamelCase					:      Optional[Any]          =   xa
				lowerCamelCase					:      Union[str, Any]          =   ya
				invpath.append([x, y]      )

		lowerCamelCase					:      Optional[int]          =   []
		for i in range(len(a_      )      ):
				path.append(invpath[len(a_      ) - 1 - i]      )
		return path, action


if __name__ == "__main__":
					_A						=			[
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
					    [0, 1, 0, 0, 0, 0],
					    [0, 1, 0, 0, 1, 0],
					    [0, 0, 0, 0, 1, 0],
					]

					_A						=			[0, 0]
					# all coordinates are given in format [y,x]
					_A						=			[len(grid) - 1, len(grid[0]) - 1]
					_A						=			1

					# the cost map which pushes the path closer to the goal
					_A						=			[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
					for i in range(len(grid)):
										for j in range(len(grid[0])):
															_A						=			abs(i - goal[0]) + abs(j - goal[1])
															if grid[i][j] == 1:
																				# added extra penalty in the heuristic map
																				_A						=			9_9

					_A					,  _A						=			search(grid, init, goal, cost, heuristic)

					print('ACTION MAP')
					for i in range(len(action)):
										print(action[i])

					for i in range(len(path)):
										print(path[i])






