



from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
_snake_case		=  0

_snake_case		=  [
    [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],
]

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

_snake_case		=  tuple[int, int]



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a,			__a,			__a,			__a,			__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    pos_x
														_lowerCAmelCase     :							Optional[Any]    =    pos_y
														_lowerCAmelCase     :							Union[str, Any]    =    (pos_y, pos_x)
														_lowerCAmelCase     :							Tuple    =    goal_x
														_lowerCAmelCase     :							Any    =    goal_y
														_lowerCAmelCase     :							Tuple    =    g_cost
														_lowerCAmelCase     :							Dict    =    parent
														_lowerCAmelCase     :							str    =    self.calculate_heuristic()
														_lowerCAmelCase     :							str    =    self.g_cost + self.h_cost







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.pos_x - self.goal_x
														_lowerCAmelCase     :							Dict    =    self.pos_y - self.goal_y
														if HEURISTIC == 1:
																					return abs(__a) + abs(__a)
														else:
																					return sqrt(dy**2 + dx**2)







							def __lt__(   self,			__a):

														'''simple docstring'''

														return self.f_cost < other.f_cost



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    Node(start[1],			start[0],			goal[1],			goal[0],			0,			__a)
														_lowerCAmelCase     :							List[str]    =    Node(goal[1],			goal[0],			goal[1],			goal[0],			9_9999,			__a)

														_lowerCAmelCase     :							Any    =    [self.start]
														_lowerCAmelCase     :							list[Node]    =    []

														_lowerCAmelCase     :							Dict    =    False







							def    snake_case__      (   self):

														'''simple docstring'''

														while self.open_nodes:
																					# Open Nodes are sorted using __lt__
																					self.open_nodes.sort()
																					_lowerCAmelCase     :							Dict    =    self.open_nodes.pop(0)

																					if current_node.pos == self.target.pos:
																												return self.retrace_path(__a)

																					self.closed_nodes.append(__a)
																					_lowerCAmelCase     :							List[Any]    =    self.get_successors(__a)

																					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(__a)
																												else:
																																			# retrieve the best current path
																																			_lowerCAmelCase     :							Dict    =    self.open_nodes.pop(self.open_nodes.index(__a))

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

														return [self.start.pos]







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    []
														for action in delta:
																					_lowerCAmelCase     :							Tuple    =    parent.pos_x + action[1]
																					_lowerCAmelCase     :							Tuple    =    parent.pos_y + action[0]
																					if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(__a) - 1):
																												continue

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

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    node
														_lowerCAmelCase     :							str    =    []
														while current_node is not None:
																					path.append((current_node.pos_y, current_node.pos_x))
																					_lowerCAmelCase     :							Tuple    =    current_node.parent
														path.reverse()
														return path



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    AStar(__a,			__a)
														_lowerCAmelCase     :							List[str]    =    AStar(__a,			__a)
														_lowerCAmelCase     :							Tuple    =    False







							def    snake_case__      (   self):

														'''simple docstring'''

														while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
																					self.fwd_astar.open_nodes.sort()
																					self.bwd_astar.open_nodes.sort()
																					_lowerCAmelCase     :							List[str]    =    self.fwd_astar.open_nodes.pop(0)
																					_lowerCAmelCase     :							str    =    self.bwd_astar.open_nodes.pop(0)

																					if current_bwd_node.pos == current_fwd_node.pos:
																												return self.retrace_bidirectional_path(
																												    __a,			__a)

																					self.fwd_astar.closed_nodes.append(__a)
																					self.bwd_astar.closed_nodes.append(__a)

																					_lowerCAmelCase     :							Tuple    =    current_bwd_node
																					_lowerCAmelCase     :							Any    =    current_fwd_node

																					_lowerCAmelCase     :							Dict    =    {
																					    self.fwd_astar: self.fwd_astar.get_successors(__a),
																					    self.bwd_astar: self.bwd_astar.get_successors(__a),
																					}

																					for astar in [self.fwd_astar, self.bwd_astar]:
																												for child_node in successors[astar]:
																																			if child_node in astar.closed_nodes:
																																										continue

																																			if child_node not in astar.open_nodes:
																																										astar.open_nodes.append(__a)
																																			else:
																																										# retrieve the best current path
																																										_lowerCAmelCase     :							Optional[int]    =    astar.open_nodes.pop(
																																										    astar.open_nodes.index(__a))

																																										if child_node.g_cost < better_node.g_cost:
																																																	astar.open_nodes.append(__a)
																																										else:
																																																	astar.open_nodes.append(__a)

														return [self.fwd_astar.start.pos]







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.fwd_astar.retrace_path(__a)
														_lowerCAmelCase     :							Optional[int]    =    self.bwd_astar.retrace_path(__a)
														bwd_path.pop()
														bwd_path.reverse()
														_lowerCAmelCase     :							str    =    fwd_path + bwd_path
														return path


if __name__ == "__main__":
						# all coordinates are given in format [y,x]
						_snake_case		=  (0, 0)
						_snake_case		=  (len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						_snake_case		=  time.time()
						_snake_case		=  AStar(init, goal)
						_snake_case		=  a_star.search()
						_snake_case		=  time.time() - start_time
						print(f'''AStar execution time = {end_time:f} seconds''')

						_snake_case		=  time.time()
						_snake_case		=  BidirectionalAStar(init, goal)
						_snake_case		=  time.time() - bd_start_time
						print(f'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'wav2vec2'







							def __init__(   self,			__a=32,			__a=768,			__a=12,			__a=12,			__a=3072,			__a="gelu",			__a=0.1,			__a=0.1,			__a=0.1,			__a=0.0,			__a=0.0,			__a=0.1,			__a=0.1,			__a=0.02,			__a=1E-5,			__a="group",			__a="gelu",			__a=(512, 512, 512, 512, 512, 512, 512),			__a=(5, 2, 2, 2, 2, 2, 2),			__a=(10, 3, 3, 3, 3, 2, 2),			__a=False,			__a=128,			__a=16,			__a=False,			__a=True,			__a=0.05,			__a=10,			__a=2,			__a=0.0,			__a=10,			__a=0,			__a=320,			__a=2,			__a=0.1,			__a=100,			__a=256,			__a=256,			__a=0.1,			__a="sum",			__a=False,			__a=False,			__a=256,			__a=(512, 512, 512, 512, 1500),			__a=(5, 3, 3, 1, 1),			__a=(1, 2, 3, 1, 1),			__a=512,			__a=0,			__a=1,			__a=2,			__a=False,			__a=3,			__a=2,			__a=3,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a,			pad_token_id=__a,			bos_token_id=__a,			eos_token_id=__a)
														_lowerCAmelCase     :							str    =    hidden_size
														_lowerCAmelCase     :							Optional[int]    =    feat_extract_norm
														_lowerCAmelCase     :							Union[str, Any]    =    feat_extract_activation
														_lowerCAmelCase     :							Optional[Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    conv_bias
														_lowerCAmelCase     :							str    =    num_conv_pos_embeddings
														_lowerCAmelCase     :							List[Any]    =    num_conv_pos_embedding_groups
														_lowerCAmelCase     :							str    =    len(self.conv_dim)
														_lowerCAmelCase     :							List[str]    =    num_hidden_layers
														_lowerCAmelCase     :							str    =    intermediate_size
														_lowerCAmelCase     :							Any    =    hidden_act
														_lowerCAmelCase     :							int    =    num_attention_heads
														_lowerCAmelCase     :							Optional[Any]    =    hidden_dropout
														_lowerCAmelCase     :							List[str]    =    attention_dropout
														_lowerCAmelCase     :							Tuple    =    activation_dropout
														_lowerCAmelCase     :							int    =    feat_proj_dropout
														_lowerCAmelCase     :							List[str]    =    final_dropout
														_lowerCAmelCase     :							int    =    layerdrop
														_lowerCAmelCase     :							int    =    layer_norm_eps
														_lowerCAmelCase     :							Union[str, Any]    =    initializer_range
														_lowerCAmelCase     :							str    =    vocab_size
														_lowerCAmelCase     :							Optional[Any]    =    do_stable_layer_norm
														_lowerCAmelCase     :							Any    =    use_weighted_layer_sum

														if (
														    (len(self.conv_stride) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel) != self.num_feat_extract_layers)
														    or (len(self.conv_dim) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																					    f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
																					    f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														_lowerCAmelCase     :							str    =    apply_spec_augment
														_lowerCAmelCase     :							Optional[Any]    =    mask_time_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_time_length
														_lowerCAmelCase     :							List[str]    =    mask_time_min_masks
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_prob
														_lowerCAmelCase     :							Optional[int]    =    mask_feature_length
														_lowerCAmelCase     :							List[str]    =    mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														_lowerCAmelCase     :							Union[str, Any]    =    num_codevectors_per_group
														_lowerCAmelCase     :							str    =    num_codevector_groups
														_lowerCAmelCase     :							Optional[int]    =    contrastive_logits_temperature
														_lowerCAmelCase     :							Optional[int]    =    feat_quantizer_dropout
														_lowerCAmelCase     :							Optional[int]    =    num_negatives
														_lowerCAmelCase     :							Union[str, Any]    =    codevector_dim
														_lowerCAmelCase     :							Any    =    proj_codevector_dim
														_lowerCAmelCase     :							Optional[int]    =    diversity_loss_weight

														# ctc loss
														_lowerCAmelCase     :							Tuple    =    ctc_loss_reduction
														_lowerCAmelCase     :							Tuple    =    ctc_zero_infinity

														# adapter
														_lowerCAmelCase     :							List[Any]    =    add_adapter
														_lowerCAmelCase     :							List[str]    =    adapter_kernel_size
														_lowerCAmelCase     :							str    =    adapter_stride
														_lowerCAmelCase     :							List[str]    =    num_adapter_layers
														_lowerCAmelCase     :							str    =    output_hidden_size or hidden_size
														_lowerCAmelCase     :							Tuple    =    adapter_attn_dim

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														_lowerCAmelCase     :							str    =    list(__a)
														_lowerCAmelCase     :							Union[str, Any]    =    list(__a)
														_lowerCAmelCase     :							List[str]    =    list(__a)
														_lowerCAmelCase     :							Tuple    =    xvector_output_dim







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return functools.reduce(operator.mul,			self.conv_stride,			1)
