





"""simple docstring"""






import heapq
import sys

import numpy as np

__UpperCAmelCase						=			tuple[int, int]



class 					__UpperCAmelCase							:
				def __init__(		self    :			Optional[Any]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			[]
							a__  :	Optional[int]		 =			set()
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Any:



							'''simple docstring'''



							if not self.empty():
										return self.elements[0][0]
							else:
										return float("inf"    )
				def       UpperCAmelCase      (		self    :			Any    )	->	Any:



							'''simple docstring'''



							return len(self.elements    ) == 0
				def       UpperCAmelCase      (		self    :			List[Any]	, a_    :			Dict	, a_    :			List[str]    )	->	int:



							'''simple docstring'''



							if item not in self.set:
										heapq.heappush(self.elements	, (priority, item)    )
										self.set.add(a_    )
							else:
										# update
										# print("update", item)
										a__  :	Union[str, Any]		 =			[]
										((a__) ,	(a__))  :	str		 =			heapq.heappop(self.elements    )
										while x != item:
													temp.append((pri, x)    )
													((a__) ,	(a__))  :	Dict		 =			heapq.heappop(self.elements    )
										temp.append((priority, item)    )
										for pro, xxx in temp:
													heapq.heappush(self.elements	, (pro, xxx)    )
				def       UpperCAmelCase      (		self    :			List[str]	, a_    :			Dict    )	->	Tuple:



							'''simple docstring'''



							if item in self.set:
										self.set.remove(a_    )
										a__  :	Tuple		 =			[]
										((a__) ,	(a__))  :	Optional[Any]		 =			heapq.heappop(self.elements    )
										while x != item:
													temp.append((pro, x)    )
													((a__) ,	(a__))  :	List[Any]		 =			heapq.heappop(self.elements    )
										for prito, yyy in temp:
													heapq.heappush(self.elements	, (prito, yyy)    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



							return self.elements[0][1]






				def       UpperCAmelCase      (		self    :			str    )	->	Tuple:



							'''simple docstring'''



							((a__) ,	(a__))  :	Optional[Any]		 =			heapq.heappop(self.elements    )
							self.set.remove(a_    )
							return (priority, item)






def      lowercase__ (					lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			TPos			)    ->			List[Any]:



			'''simple docstring'''
			# euclidean distance
			a__  :	Dict		 =			np.array(lowerCAmelCase__			)
			a__  :	str		 =			np.array(lowerCAmelCase__			)
			return np.linalg.norm(a - b			)






def      lowercase__ (					lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			TPos			)    ->			Any:



			'''simple docstring'''
			# integer division by time variable
			return consistent_heuristic(lowerCAmelCase__							,			lowerCAmelCase__			) // t






def      lowercase__ (					lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			TPos			)    ->			List[Any]:



			'''simple docstring'''
			# manhattan distance
			return abs(p[0] - goal[0]			) + abs(p[1] - goal[1]			)






def      lowercase__ (					lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			dict[TPos, float]			)    ->			List[str]:



			'''simple docstring'''
			a__  :	str		 =			g_function[start] + Wa * heuristics[i](lowerCAmelCase__							,			lowerCAmelCase__			)
			return ans






def      lowercase__ (					lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Tuple			)    ->			Optional[Any]:



			'''simple docstring'''
			a__  :	Union[str, Any]		 =			np.chararray((n, n)			)
			for i in range(lowerCAmelCase__			):
						for j in range(lowerCAmelCase__			):
									a__  :	Any		 =			"*"

			for i in range(lowerCAmelCase__			):
						for j in range(lowerCAmelCase__			):
									if (j, (n - 1) - i) in blocks:
												a__  :	Optional[int]		 =			"#"

			a__  :	int		 =			"-"
			a__  :	List[str]		 =			back_pointer[goal]
			while x != start:
						((a__) ,	(a__))  :	Any		 =			x
						# print(x)
						a__  :	Optional[int]		 =			"-"
						a__  :	int		 =			back_pointer[x]
			a__  :	List[Any]		 =			"-"

			for i in range(lowerCAmelCase__			):
						for j in range(lowerCAmelCase__			):
									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:-"			)
			a__  :	List[str]		 =			back_pointer[goal]
			while x != start:
						print(lowerCAmelCase__							,			end=" "			)
						a__  :	Optional[int]		 =			back_pointer[x]
			print(lowerCAmelCase__			)
			sys.exit()






def      lowercase__ (					lowerCAmelCase__   :			TPos			)    ->			Dict:



			'''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      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			Tuple							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			List[str]							,			)    ->			List[str]:



			'''simple docstring'''
			for itera in range(lowerCAmelCase__			):
						open_list[itera].remove_element(lowerCAmelCase__			)
			# print("s", s)
			# print("j", j)
			((a__) ,	(a__))  :	List[str]		 =			s
			a__  :	int		 =			(x - 1, y)
			a__  :	Any		 =			(x + 1, y)
			a__  :	Optional[int]		 =			(x, y + 1)
			a__  :	Union[str, Any]		 =			(x, y - 1)

			for neighbours in [left, right, up, down]:
						if neighbours not in blocks:
									if valid(lowerCAmelCase__			) and neighbours not in visited:
												# print("neighbour", neighbours)
												visited.add(lowerCAmelCase__			)
												a__  :	List[Any]		 =			-1
												a__  :	int		 =			float("inf"			)

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






def      lowercase__ (					)    ->			List[str]:



			'''simple docstring'''
			a__  :	Union[str, Any]		 =			[]
			for x in range(1							,			5			):
						for y in range(1							,			6			):
									some_list.append((x, y)			)

			for x in range(1_5							,			2_0			):
						some_list.append((x, 1_7)			)

			for x in range(1_0							,			1_9			):
						for y in range(1							,			1_5			):
									some_list.append((x, y)			)

    # L block
			for x in range(1							,			4			):
						for y in range(1_2							,			1_9			):
									some_list.append((x, y)			)
			for x in range(3							,			1_3			):
						for y in range(1_6							,			1_9			):
									some_list.append((x, y)			)
			return some_list


__UpperCAmelCase						=			{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__UpperCAmelCase						=			[
    (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),
]
__UpperCAmelCase						=			make_common_ground()


__UpperCAmelCase						=			blocks_blk
# hyper parameters
__UpperCAmelCase						=			1
__UpperCAmelCase						=			1
__UpperCAmelCase						=			20
__UpperCAmelCase						=			3  # one consistent and two other inconsistent

# start and end destination
__UpperCAmelCase						=			(0, 0)
__UpperCAmelCase						=			(n - 1, n - 1)

__UpperCAmelCase						=			1






def      lowercase__ (					lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			TPos							,			lowerCAmelCase__   :			int			)    ->			Dict:



			'''simple docstring'''
			a__  :	Union[str, Any]		 =			{start: 0, goal: float("inf"			)}
			a__  :	Any		 =			{start: -1, goal: -1}
			a__  :	Any		 =			[]
			a__  :	Optional[int]		 =			set()

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

			a__  :	list[int]		 =			[]
			a__  :	list[int]		 =			[]
			while open_list[0].minkey() < float("inf"			):
						for i in range(1							,			lowerCAmelCase__			):
									# 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(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)
												else:
															a__ ,	a__  :	List[Any]		 =			open_list[i].top_show()
															visited.add(lowerCAmelCase__			)
															expand_state(
															    lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			)
															close_list_inad.append(lowerCAmelCase__			)
									else:
												if g_function[goal] <= open_list[0].minkey():
															if g_function[goal] < float("inf"			):
																		do_something(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)
												else:
															a__  :	Dict		 =			open_list[0].top_show()
															visited.add(lowerCAmelCase__			)
															expand_state(
															    lowerCAmelCase__							,			0							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			)
															close_list_anchor.append(lowerCAmelCase__			)
			print("No path found to goal"			)
			print()
			for i in range(n - 1							,			-1							,			-1			):
						for j in range(lowerCAmelCase__			):
									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)






"""simple docstring"""






import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


__UpperCAmelCase						=			{
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

__UpperCAmelCase						=			AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]=False			)    ->			List[str]:



			'''simple docstring'''
			a__ ,	a__  :	Optional[int]		 =			create_model(
			    "HTSAT-tiny"							,			"roberta"							,			lowerCAmelCase__							,			precision="fp32"							,			device="cuda:0" if torch.cuda.is_available() else "cpu"							,			enable_fusion=lowerCAmelCase__							,			fusion_type="aff_2d" if enable_fusion else None							,			)
			return model, model_cfg






def      lowercase__ (					lowerCAmelCase__   :			Optional[int]			)    ->			Dict:



			'''simple docstring'''
			a__  :	Any		 =			{}

			a__  :	Tuple		 =			R".*sequential.(\d+).*"
			a__  :	Tuple		 =			R".*_projection.(\d+).*"

			for key, value in state_dict.items():
						# check if any key needs to be modified
						for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
									if key_to_modify in key:
												a__  :	str		 =			key.replace(lowerCAmelCase__							,			lowerCAmelCase__			)

						if re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									# replace sequential layers with list
									a__  :	Optional[Any]		 =			re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)

									a__  :	int		 =			key.replace(F"sequential.{sequential_layer}."							,			F"layers.{int(lowerCAmelCase__			)//3}.linear."			)
						elif re.match(lowerCAmelCase__							,			lowerCAmelCase__			):
									a__  :	Tuple		 =			int(re.match(lowerCAmelCase__							,			lowerCAmelCase__			).group(1			)			)

									# Because in CLAP they use `nn.Sequential`...
									a__  :	List[Any]		 =			1 if projecton_layer == 0 else 2

									a__  :	Dict		 =			key.replace(F"_projection.{projecton_layer}."							,			F"_projection.linear{transformers_projection_layer}."			)

						if "audio" and "qkv" in key:
									# split qkv into query key and value
									a__  :	List[Any]		 =			value
									a__  :	List[Any]		 =			mixed_qkv.size(0			) // 3

									a__  :	Optional[int]		 =			mixed_qkv[:qkv_dim]
									a__  :	List[str]		 =			mixed_qkv[qkv_dim : qkv_dim * 2]
									a__  :	Optional[Any]		 =			mixed_qkv[qkv_dim * 2 :]

									a__  :	Tuple		 =			query_layer
									a__  :	int		 =			key_layer
									a__  :	Optional[int]		 =			value_layer
						else:
									a__  :	List[str]		 =			value

			return model_state_dict






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Tuple=False			)    ->			Tuple:



			'''simple docstring'''
			a__ ,	a__  :	Tuple		 =			init_clap(lowerCAmelCase__							,			enable_fusion=lowerCAmelCase__			)

			clap_model.eval()
			a__  :	Optional[int]		 =			clap_model.state_dict()
			a__  :	Optional[Any]		 =			rename_state_dict(lowerCAmelCase__			)

			a__  :	Union[str, Any]		 =			ClapConfig()
			a__  :	Dict		 =			enable_fusion
			a__  :	Any		 =			ClapModel(lowerCAmelCase__			)

			# ignore the spectrogram embedding layer
			model.load_state_dict(lowerCAmelCase__							,			strict=lowerCAmelCase__			)

			model.save_pretrained(lowerCAmelCase__			)
			transformers_config.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
		__UpperCAmelCase						=			parser.parse_args()

		convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)