




'''simple docstring'''

import functools
import operator

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


a    :							List[Any]										=    logging.get_logger(__name__)

a    :							List[str]										=    {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"wavlm"


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]=32			,							lowercase_       :   Optional[int]=768			,							lowercase_       :   List[str]=12			,							lowercase_       :   Dict=12			,							lowercase_       :   Any=3072			,							lowercase_       :   List[Any]="gelu"			,							lowercase_       :   int=0.1			,							lowercase_       :   Dict=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   int=0.0			,							lowercase_       :   List[Any]=0.1			,							lowercase_       :   int=0.1			,							lowercase_       :   Optional[int]=0.02			,							lowercase_       :   Dict=1e-5			,							lowercase_       :   Tuple="group"			,							lowercase_       :   str="gelu"			,							lowercase_       :   Any=(512, 512, 512, 512, 512, 512, 512)			,							lowercase_       :   str=(5, 2, 2, 2, 2, 2, 2)			,							lowercase_       :   Any=(10, 3, 3, 3, 3, 2, 2)			,							lowercase_       :   Tuple=False			,							lowercase_       :   List[Any]=128			,							lowercase_       :   int=16			,							lowercase_       :   Tuple=320			,							lowercase_       :   Union[str, Any]=800			,							lowercase_       :   List[str]=False			,							lowercase_       :   str=True			,							lowercase_       :   List[Any]=0.05			,							lowercase_       :   Optional[Any]=10			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   Optional[int]=0.0			,							lowercase_       :   List[str]=10			,							lowercase_       :   Union[str, Any]=320			,							lowercase_       :   Optional[Any]=2			,							lowercase_       :   List[str]=0.1			,							lowercase_       :   int=100			,							lowercase_       :   Tuple=256			,							lowercase_       :   Tuple=256			,							lowercase_       :   Dict=0.1			,							lowercase_       :   Any="mean"			,							lowercase_       :   Tuple=False			,							lowercase_       :   Dict=False			,							lowercase_       :   str=256			,							lowercase_       :   Optional[int]=(512, 512, 512, 512, 1500)			,							lowercase_       :   List[str]=(5, 3, 3, 1, 1)			,							lowercase_       :   Any=(1, 2, 3, 1, 1)			,							lowercase_       :   int=512			,							lowercase_       :   List[Any]=80			,							lowercase_       :   Optional[Any]=0			,							lowercase_       :   int=1			,							lowercase_       :   List[str]=2			,							lowercase_       :   List[str]=False			,							lowercase_       :   Dict=3			,							lowercase_       :   List[Any]=2			,							lowercase_       :   Optional[Any]=3			,							lowercase_       :   int=None			,							**lowercase_       :   List[str]			,							):
									super().__init__(**lowercase_			,							pad_token_id=lowercase_			,							bos_token_id=lowercase_			,							eos_token_id=lowercase_   )
									snake_case_		     =							hidden_size
									snake_case_		     =							feat_extract_norm
									snake_case_		     =							feat_extract_activation
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							conv_bias
									snake_case_		     =							num_buckets
									snake_case_		     =							max_bucket_distance
									snake_case_		     =							num_conv_pos_embeddings
									snake_case_		     =							num_conv_pos_embedding_groups
									snake_case_		     =							len(self.conv_dim   )
									snake_case_		     =							num_hidden_layers
									snake_case_		     =							intermediate_size
									snake_case_		     =							hidden_act
									snake_case_		     =							num_attention_heads
									snake_case_		     =							hidden_dropout
									snake_case_		     =							attention_dropout
									snake_case_		     =							activation_dropout
									snake_case_		     =							feat_proj_dropout
									snake_case_		     =							final_dropout
									snake_case_		     =							layerdrop
									snake_case_		     =							layer_norm_eps
									snake_case_		     =							initializer_range
									snake_case_		     =							num_ctc_classes
									snake_case_		     =							vocab_size
									snake_case_		     =							do_stable_layer_norm
									snake_case_		     =							use_weighted_layer_sum
									snake_case_		     =							classifier_proj_size

									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
									snake_case_		     =							apply_spec_augment
									snake_case_		     =							mask_time_prob
									snake_case_		     =							mask_time_length
									snake_case_		     =							mask_time_min_masks
									snake_case_		     =							mask_feature_prob
									snake_case_		     =							mask_feature_length

									# parameters for pretraining with codevector quantized representations
									snake_case_		     =							num_codevectors_per_group
									snake_case_		     =							num_codevector_groups
									snake_case_		     =							contrastive_logits_temperature
									snake_case_		     =							num_negatives
									snake_case_		     =							codevector_dim
									snake_case_		     =							proj_codevector_dim
									snake_case_		     =							diversity_loss_weight

									# ctc loss
									snake_case_		     =							ctc_loss_reduction
									snake_case_		     =							ctc_zero_infinity

									# adapter
									snake_case_		     =							add_adapter
									snake_case_		     =							adapter_kernel_size
									snake_case_		     =							adapter_stride
									snake_case_		     =							num_adapter_layers
									snake_case_		     =							output_hidden_size or hidden_size

									# SequenceClassification-specific parameter. Feel free to ignore for other classes.
									snake_case_		     =							classifier_proj_size

									# XVector-specific parameters. Feel free to ignore for other classes.
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							list(lowercase_   )
									snake_case_		     =							xvector_output_dim


				@property
				def     A_					(						self       :   Tuple   ):
									return functools.reduce(operator.mul			,							self.conv_stride			,							1   )







'''simple docstring'''

import heapq
import sys

import numpy as np

a    :							Dict										=    tuple[int, int]






class 		a							:


				def __init__(						self       :   Dict   ):
									snake_case_		     =							[]
									snake_case_		     =							set()


				def     A_					(						self       :   int   ):
									if not self.empty():
														return self.elements[0][0]
									else:
														return float('''inf'''   )


				def     A_					(						self       :   List[str]   ):
									return len(self.elements   ) == 0


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									if item not in self.set:
														heapq.heappush(self.elements			,							(priority, item)   )
														self.set.add(lowercase_   )
									else:
														# update
														# print("update", item)
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pri, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														temp.append((priority, item)   )
														for pro, xxx in temp:
																			heapq.heappush(self.elements			,							(pro, xxx)   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									if item in self.set:
														self.set.remove(lowercase_   )
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pro, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														for prito, yyy in temp:
																			heapq.heappush(self.elements			,							(prito, yyy)   )


				def     A_					(						self       :   Optional[int]   ):
									return self.elements[0][1]


				def     A_					(						self       :   Tuple   ):
									((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
									self.set.remove(lowercase_   )
									return (priority, item)







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							np.array(__UpperCAmelCase      )
					snake_case_		     =							np.array(__UpperCAmelCase      )
					return np.linalg.norm(a - b      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return consistent_heuristic(__UpperCAmelCase, __UpperCAmelCase      ) // t


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							g_function[start] + Wa * heuristics[i](__UpperCAmelCase, __UpperCAmelCase      )
					return ans


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							np.chararray((n, n)      )
					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															snake_case_		     =							'''*'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (j, (n - 1) - i) in blocks:
																				snake_case_		     =							'''#'''

					snake_case_		     =							'''-'''
					snake_case_		     =							back_pointer[goal]
					while x != start:
										((snake_case_)     ,(snake_case_))		     =							x
										# print(x)
										snake_case_		     =							'''-'''
										snake_case_		     =							back_pointer[x]
					snake_case_		     =							'''-'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j], end=''' '''      )
																				print('''<-- End position''', end=''' '''      )
															else:
																				print(grid[i][j], end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					snake_case_		     =							back_pointer[goal]
					while x != start:
										print(__UpperCAmelCase, end=''' '''      )
										snake_case_		     =							back_pointer[x]
					print(__UpperCAmelCase      )
					sys.exit()


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   str:

					'''simple docstring'''

					for itera in range(__UpperCAmelCase      ):
										open_list[itera].remove_element(__UpperCAmelCase      )
					# print("s", s)
					# print("j", j)
					((snake_case_)     ,(snake_case_))		     =							s
					snake_case_		     =							(x - 1, y)
					snake_case_		     =							(x + 1, y)
					snake_case_		     =							(x, y + 1)
					snake_case_		     =							(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__UpperCAmelCase      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__UpperCAmelCase      )
																				snake_case_		     =							-1
																				snake_case_		     =							float('''inf'''      )

															if valid(__UpperCAmelCase      ) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_		     =							g_function[s] + 1
																				snake_case_		     =							s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__UpperCAmelCase, key(__UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1, __UpperCAmelCase      ):
																																			if key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ) <= Wa * key(
																																			    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      ):
																																								open_list[j].put(
																																								    __UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							[]
					for x in range(1, 5      ):
										for y in range(1, 6      ):
															some_list.append((x, y)      )

					for x in range(15, 20      ):
										some_list.append((x, 17)      )

					for x in range(10, 19      ):
										for y in range(1, 15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1, 4      ):
										for y in range(12, 19      ):
															some_list.append((x, y)      )
					for x in range(3, 13      ):
										for y in range(16, 19      ):
															some_list.append((x, y)      )
					return some_list


a    :							Tuple										=    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a    :							Dict										=    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a    :							List[str]										=    make_common_ground()


a    :							Optional[int]										=    blocks_blk
# hyper parameters
a    :							Union[str, Any]										=    1
a    :							str										=    1
a    :							int										=    20
a    :							Optional[int]										=    3  # one consistent and two other inconsistent

# start and end destination
a    :							List[str]										=    (0, 0)
a    :							str										=    (n - 1, n - 1)

a    :							str										=    1


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							{start: 0, goal: float('''inf'''      )}
					snake_case_		     =							{start: -1, goal: -1}
					snake_case_		     =							[]
					snake_case_		     =							set()

					for i in range(__UpperCAmelCase      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )

					snake_case_		     =							[]
					snake_case_		     =							[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1, __UpperCAmelCase      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_     ,snake_case_		     =							open_list[i].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_inad.append(__UpperCAmelCase      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_		     =							open_list[0].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_anchor.append(__UpperCAmelCase      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1, -1, -1      ):
										for j in range(__UpperCAmelCase      ):
															if (j, i) in blocks:
																				print('''#''', end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*''', end=''' '''      )
																				else:
																									print('''-''', end=''' '''      )
															else:
																				print('''*''', end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position''', end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)

