





'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase					    =	logging.get_logger(__name__)

__UpperCamelCase					    =	{
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}




class  _A (					__lowercase		):
		lowercase__:   int				     =   '''xlm'''
		lowercase__:   Dict				     =   {
		    '''hidden_size''': '''emb_dim''',
		    '''num_attention_heads''': '''n_heads''',
		    '''num_hidden_layers''': '''n_layers''',
		    '''n_words''': '''vocab_size''',  # For backward compatibility
		}

		def __init__(   self					:   str  ,      __magic_name__					:   str=3_01_45  ,      __magic_name__					:   str=20_48  ,      __magic_name__					:   Dict=12  ,      __magic_name__					:   List[Any]=16  ,      __magic_name__					:   Optional[int]=0.1  ,      __magic_name__					:   List[Any]=0.1  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   Tuple=False  ,      __magic_name__					:   List[Any]=False  ,      __magic_name__					:   List[str]=False  ,      __magic_name__					:   List[Any]=1  ,      __magic_name__					:   int=True  ,      __magic_name__					:   List[Any]=5_12  ,      __magic_name__					:   List[Any]=20_48**-0.5  ,      __magic_name__					:   Tuple=1E-12  ,      __magic_name__					:   List[str]=0.02  ,      __magic_name__					:   List[str]=0  ,      __magic_name__					:   str=1  ,      __magic_name__					:   Union[str, Any]=2  ,      __magic_name__					:   List[Any]=3  ,      __magic_name__					:   Union[str, Any]=5  ,      __magic_name__					:   List[str]=True  ,      __magic_name__					:   Dict="first"  ,      __magic_name__					:   str=True  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Optional[Any]=True  ,      __magic_name__					:   List[str]=0.1  ,      __magic_name__					:   List[str]=5  ,      __magic_name__					:   List[Any]=5  ,      __magic_name__					:   int=0  ,      __magic_name__					:   int=0  ,      __magic_name__					:   List[str]=2  ,      __magic_name__					:   int=0  ,      **__magic_name__					:   Any  ,      )					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     vocab_size
					__snake_case					:      List[Any]							      =     emb_dim
					__snake_case					:      Tuple							      =     n_layers
					__snake_case					:      Union[str, Any]							      =     n_heads
					__snake_case					:      Optional[int]							      =     dropout
					__snake_case					:      Dict							      =     attention_dropout
					__snake_case					:      Tuple							      =     gelu_activation
					__snake_case					:      Any							      =     sinusoidal_embeddings
					__snake_case					:      List[Any]							      =     causal
					__snake_case					:      Dict							      =     asm
					__snake_case					:      Optional[Any]							      =     n_langs
					__snake_case					:      Any							      =     use_lang_emb
					__snake_case					:      int							      =     layer_norm_eps
					__snake_case					:      List[Any]							      =     bos_index
					__snake_case					:      Any							      =     eos_index
					__snake_case					:      Union[str, Any]							      =     pad_index
					__snake_case					:      Optional[Any]							      =     unk_index
					__snake_case					:      List[Any]							      =     mask_index
					__snake_case					:      Union[str, Any]							      =     is_encoder
					__snake_case					:      Optional[int]							      =     max_position_embeddings
					__snake_case					:      int							      =     embed_init_std
					__snake_case					:      Dict							      =     init_std
					__snake_case					:      Any							      =     summary_type
					__snake_case					:      int							      =     summary_use_proj
					__snake_case					:      Any							      =     summary_activation
					__snake_case					:      Any							      =     summary_proj_to_labels
					__snake_case					:      List[str]							      =     summary_first_dropout
					__snake_case					:      List[Any]							      =     start_n_top
					__snake_case					:      List[Any]							      =     end_n_top
					__snake_case					:      str							      =     mask_token_id
					__snake_case					:      List[Any]							      =     lang_id

					if "n_words" in kwargs:
								__snake_case					:      int							      =     kwargs["""n_words"""]

					super().__init__(pad_token_id=__magic_name__  ,      bos_token_id=__magic_name__  ,      **__magic_name__			)





class  _A (					__lowercase		):

		@property
		def 	lowercase__     (   self					:   Optional[int]			)					->    Mapping[str, Mapping[int, str]]:


					"""simple docstring"""

					if self.task == "multiple-choice":
								__snake_case					:      str							      =     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__snake_case					:      Dict							      =     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					    ]			)









'''simple docstring'''






from __future__ import annotations

__UpperCamelCase					    =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				tuple[list[list[int]], list[list[int]]]:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the reference grid
			__snake_case					:      Tuple							      =     1
			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the action grid

			__snake_case					:      List[str]							      =     init[0]
			__snake_case					:      str							      =     init[1]
			__snake_case					:      int							      =     0
			__snake_case					:      int							      =     g + heuristic[x][y]  # cost from starting cell to destination cell
			__snake_case					:      List[str]							      =     [[f, g, x, y]]

			__snake_case					:      Any							      =     False  # flag that is set when search is complete
			__snake_case					:      int							      =     False  # flag set if we can't find expand

			while not found and not resign:
						if len(_lowerCamelCase ) == 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()
									__snake_case					:      Tuple							      =     cell.pop()
									__snake_case					:      Optional[int]							      =     next_cell[2]
									__snake_case					:      List[Any]							      =     next_cell[3]
									__snake_case					:      int							      =     next_cell[1]

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

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


if __name__ == "__main__":
	__UpperCamelCase					    =	[
	    [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],
	]

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

	# the cost map which pushes the path closer to the goal
	__UpperCamelCase					    =	[[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])):
			__UpperCamelCase					    =	abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				__UpperCamelCase					    =	99

	__UpperCamelCase    ,		__UpperCamelCase					    =	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])


