


"""simple docstring"""
from __future__ import annotations

__SCREAMING_SNAKE_CASE							:    Dict		=    [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, )    ->				tuple[list[list[int]], list[list[int]]]:
							snake_case_           =		[
							    [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
							]  # the reference grid
							snake_case_           =		1
							snake_case_           =		[
							    [0 for col in range(len(grid[0]				)				)] for row in range(len(_SCREAMING_SNAKE_CASE				)				)
							]  # the action grid

							snake_case_           =		init[0]
							snake_case_           =		init[1]
							snake_case_           =		0
							snake_case_           =		g + heuristic[x][y]  # cost from starting cell to destination cell
							snake_case_           =		[[f, g, x, y]]

							snake_case_           =		False  # flag that is set when search is complete
							snake_case_           =		False  # flag set if we can't find expand

							while not found and not resign:
														if len(_SCREAMING_SNAKE_CASE				) == 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_           =		cell.pop()
																					snake_case_           =		next_cell[2]
																					snake_case_           =		next_cell[3]
																					snake_case_           =		next_cell[1]

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

							snake_case_           =		[]
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														path.append(invpath[len(_SCREAMING_SNAKE_CASE				) - 1 - i]				)
							return path, action


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    List[str]		=    [
				    [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],
				]

				__SCREAMING_SNAKE_CASE							:    List[str]		=    [0, 0]
				# all coordinates are given in format [y,x]
				__SCREAMING_SNAKE_CASE							:    Any		=    [len(grid) - 1, len(grid[0]) - 1]
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    1

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

				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    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])






"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    str		=    {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Any							      =    """mctct"""



							def __init__(				self							:      Dict		,       UpperCAmelCase_							:      List[Any]=8_065		,       UpperCAmelCase_							:      Tuple=1_536		,       UpperCAmelCase_							:      Optional[Any]=36		,       UpperCAmelCase_							:      int=6_144		,       UpperCAmelCase_							:      Dict=4		,       UpperCAmelCase_							:      Any=384		,       UpperCAmelCase_							:      List[str]=920		,       UpperCAmelCase_							:      Any=1E-5		,       UpperCAmelCase_							:      Any=0.3		,       UpperCAmelCase_							:      Tuple="relu"		,       UpperCAmelCase_							:      Union[str, Any]=0.02		,       UpperCAmelCase_							:      Dict=0.3		,       UpperCAmelCase_							:      str=0.3		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=0		,       UpperCAmelCase_							:      int=2		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      Tuple=0.3		,       UpperCAmelCase_							:      Optional[Any]=1		,       UpperCAmelCase_							:      Optional[Any]=(7,)		,       UpperCAmelCase_							:      Optional[Any]=(3,)		,       UpperCAmelCase_							:      List[str]=80		,       UpperCAmelCase_							:      Tuple=1		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[str]="sum"		,       UpperCAmelCase_							:      Union[str, Any]=False		,       **UpperCAmelCase_							:      Any		,       )					->Dict:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_		,       pad_token_id=UpperCAmelCase_		,       bos_token_id=UpperCAmelCase_		,       eos_token_id=UpperCAmelCase_       )
														snake_case_           =		vocab_size
														snake_case_           =		hidden_size
														snake_case_           =		num_hidden_layers
														snake_case_           =		intermediate_size
														snake_case_           =		num_attention_heads
														snake_case_           =		attention_head_dim
														snake_case_           =		max_position_embeddings
														snake_case_           =		layer_norm_eps
														snake_case_           =		layerdrop
														snake_case_           =		hidden_act
														snake_case_           =		initializer_range
														snake_case_           =		hidden_dropout_prob
														snake_case_           =		attention_probs_dropout_prob
														snake_case_           =		pad_token_id
														snake_case_           =		bos_token_id
														snake_case_           =		eos_token_id
														snake_case_           =		conv_glu_dim
														snake_case_           =		conv_dropout
														snake_case_           =		num_conv_layers
														snake_case_           =		input_feat_per_channel
														snake_case_           =		input_channels
														snake_case_           =		conv_channels
														snake_case_           =		ctc_loss_reduction
														snake_case_           =		ctc_zero_infinity

														# prevents config testing fail with exporting to json
														snake_case_           =		list(UpperCAmelCase_       )
														snake_case_           =		list(UpperCAmelCase_       )

														if len(self.conv_kernel       ) != self.num_conv_layers:
																					raise ValueError(
																					    """Configuration for convolutional module is incorrect. """
																					    """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
																					    F"""but is `len(config.conv_kernel) = {len(self.conv_kernel       )}`, """
																					    F"""`config.num_conv_layers = {self.num_conv_layers}`."""       )



