






'''simple docstring'''





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      Tuple     						=						logging.get_logger()





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = True					):




	'''simple docstring'''







	print(F'Converting {name}...'					)

	with torch.no_grad():
		if hidden_sizes == 128:
			if name[-1] == "S":
				__lowercase             =  timm.create_model("""levit_128s"""				,  pretrained=lowerCamelCase					)
			else:
				__lowercase             =  timm.create_model("""levit_128"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 192:
			__lowercase             =  timm.create_model("""levit_192"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 256:
			__lowercase             =  timm.create_model("""levit_256"""				,  pretrained=lowerCamelCase					)
		if hidden_sizes == 384:
			__lowercase             =  timm.create_model("""levit_384"""				,  pretrained=lowerCamelCase					)

		from_model.eval()
		__lowercase             =  LevitForImageClassificationWithTeacher(lowerCamelCase					).eval()
		__lowercase             =  OrderedDict()

		__lowercase             =  from_model.state_dict()
		__lowercase             =  list(from_model.state_dict().keys()					)
		__lowercase             =  list(our_model.state_dict().keys()					)
		print(len(lowerCamelCase					)				,  len(lowerCamelCase					)					)
		for i in range(len(lowerCamelCase					)					):
			__lowercase             =  weights[og_keys[i]]
		our_model.load_state_dict(lowerCamelCase					)

		__lowercase             =  torch.randn((2, 3, 224, 224)					)
		__lowercase             =  from_model(lowerCamelCase					)
		__lowercase             =  our_model(lowerCamelCase					).logits

	assert torch.allclose(lowerCamelCase				,  lowerCamelCase					), "The model logits don't match the original one."

	__lowercase             =  name
	print(lowerCamelCase					)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name					)
		__lowercase             =  LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name					)

		print(F'Pushed {checkpoint_name}'					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase = None				,  lowerCamelCase = True					):




	'''simple docstring'''







	__lowercase             =  """imagenet-1k-id2label.json"""
	__lowercase             =  1_000
	__lowercase             =  (1, num_labels)

	__lowercase             =  """huggingface/label-files"""
	__lowercase             =  num_labels
	__lowercase             =  json.load(open(hf_hub_download(lowerCamelCase				,  lowerCamelCase				,  repo_type="""dataset"""					)				,  """r"""					)					)
	__lowercase             =  {int(lowerCamelCase					): v for k, v in idalabel.items()}

	__lowercase             =  idalabel
	__lowercase             =  {v: k for k, v in idalabel.items()}

	__lowercase             =  partial(lowerCamelCase				,  num_labels=lowerCamelCase				,  idalabel=lowerCamelCase				,  labelaid=lowerCamelCase					)

	__lowercase             =  {
	    """levit-128S""": 128,
	    """levit-128""": 128,
	    """levit-192""": 192,
	    """levit-256""": 256,
	    """levit-384""": 384,
	}

	__lowercase             =  {
	    """levit-128S""": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,  num_attention_heads=[4, 6, 8]				,  depths=[2, 3, 4]				,  key_dim=[16, 16, 16]				,  drop_path_rate=0				,  ),
	    """levit-128""": ImageNetPreTrainedConfig(
	        hidden_sizes=[128, 256, 384]				,  num_attention_heads=[4, 8, 12]				,  depths=[4, 4, 4]				,  key_dim=[16, 16, 16]				,  drop_path_rate=0				,  ),
	    """levit-192""": ImageNetPreTrainedConfig(
	        hidden_sizes=[192, 288, 384]				,  num_attention_heads=[3, 5, 6]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0				,  ),
	    """levit-256""": ImageNetPreTrainedConfig(
	        hidden_sizes=[256, 384, 512]				,  num_attention_heads=[4, 6, 8]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0				,  ),
	    """levit-384""": ImageNetPreTrainedConfig(
	        hidden_sizes=[384, 512, 768]				,  num_attention_heads=[6, 9, 12]				,  depths=[4, 4, 4]				,  key_dim=[32, 32, 32]				,  drop_path_rate=0.1				,  ),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]				,  lowerCamelCase				,  names_to_config[model_name]				,  lowerCamelCase				,  lowerCamelCase					)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					)
	return config, expected_shape


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default=None,
						    type=str,
						    help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default="""levit-dump-folder/""",
						    type=Path,
						    required=False,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
						parser.add_argument(
						    """--no-push_to_hub""",
						    dest="""push_to_hub""",
						    action="""store_false""",
						    help="""Do not push model and image processor to the hub""",
						)

						__UpperCamelCase      :      List[str]     						=						parser.parse_args()
						__UpperCamelCase      :      Path     						=						args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









import heapq
import sys

import numpy as np

__UpperCamelCase      :      List[str]     						=						tuple[int, int]


class        __UpperCamelCase    :







	def __init__(  self      :							Optional[int]			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  []
		__lowercase             =  set()







	def  _a   (  self      :							int			)		-> List[Any]:
		"""simple docstring"""







		if not self.empty():
			return self.elements[0][0]
		else:
			return float("""inf"""			)







	def  _a   (  self      :							Dict			)		-> Optional[Any]:
		"""simple docstring"""







		return len(self.elements			) == 0







	def  _a   (  self      :							str       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item not in self.set:
			heapq.heappush(self.elements       ,				(priority, item)			)
			self.set.add(_lowerCAmelCase			)
		else:
			# update
			# print("update", item)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pri, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			temp.append((priority, item)			)
			for pro, xxx in temp:
				heapq.heappush(self.elements       ,				(pro, xxx)			)







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Dict:
		"""simple docstring"""







		if item in self.set:
			self.set.remove(_lowerCAmelCase			)
			__lowercase             =  []
			((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			while x != item:
				temp.append((pro, x)			)
				((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
			for prito, yyy in temp:
				heapq.heappush(self.elements       ,				(prito, yyy)			)







	def  _a   (  self      :							Any			)		-> List[Any]:
		"""simple docstring"""







		return self.elements[0][1]







	def  _a   (  self      :							Optional[int]			)		-> List[Any]:
		"""simple docstring"""







		((__lowercase)       ,						(__lowercase))             =  heapq.heappop(self.elements			)
		self.set.remove(_lowerCAmelCase			)
		return (priority, item)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.array(lowerCamelCase					)
	__lowercase             =  np.array(lowerCamelCase					)
	return np.linalg.norm(a - b					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return consistent_heuristic(lowerCamelCase				,  lowerCamelCase					) // t





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  g_function[start] + Wa * heuristics[i](lowerCamelCase				,  lowerCamelCase					)
	return ans





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  np.chararray((n, n)					)
	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			__lowercase             =  """*"""

	for i in range(lowerCamelCase					):
		for j in range(lowerCamelCase					):
			if (j, (n - 1) - i) in blocks:
				__lowercase             =  """#"""

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

	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:-"""					)
	__lowercase             =  back_pointer[goal]
	while x != start:
		print(lowerCamelCase				,  end=""" """					)
		__lowercase             =  back_pointer[x]
	print(lowerCamelCase					)
	sys.exit()





def 				snake_case  (     lowerCamelCase					):




	'''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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  ):




	'''simple docstring'''







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

			if valid(lowerCamelCase					) and g_function[neighbours] > g_function[s] + 1:
				__lowercase             =  g_function[s] + 1
				__lowercase             =  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 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  []
	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


__UpperCamelCase      :      Optional[int]     						=						{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__UpperCamelCase      :      Optional[Any]     						=						[
    (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      :      Optional[Any]     						=						make_common_ground()


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

# start and end destination
__UpperCamelCase      :      str     						=						(0, 0)
__UpperCamelCase      :      str     						=						(n - 1, n - 1)

__UpperCamelCase      :      Optional[Any]     						=						1





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

	__lowercase             =  []
	__lowercase             =  []
	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:
					__lowercase       ,						__lowercase             =  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:
					__lowercase             =  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)

