






'''simple docstring'''







import heapq
import sys

import numpy as np

_A	:			Dict         =tuple[int, int]

class _lowercase	:




							def __init__(							self:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       []
														lowerCamelCase__					:	Dict         =       set()




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




							def 		lowerCamelCase_				(							self:				int     ):
														return len(self.elements     ) == 0




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




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				int     ):
														if item in self.set:
																					self.set.remove(UpperCamelCase__     )
																					lowerCamelCase__					:	Optional[int]         =       []
																					((lowerCamelCase__)  ,		(lowerCamelCase__))					:	Optional[Any]         =       heapq.heappop(self.elements     )
																					while x != item:
																												temp.append((pro, x)     )
																												((lowerCamelCase__)  ,		(lowerCamelCase__))					:	Tuple         =       heapq.heappop(self.elements     )
																					for prito, yyy in temp:
																												heapq.heappush(self.elements			,							(prito, yyy)     )




							def 		lowerCamelCase_				(							self:				str     ):
														return self.elements[0][1]




							def 		lowerCamelCase_				(							self:				int     ):
														((lowerCamelCase__)  ,		(lowerCamelCase__))					:	str         =       heapq.heappop(self.elements     )
														self.set.remove(UpperCamelCase__     )
														return (priority, item)





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Tuple:
							# euclidean distance
							lowerCamelCase__					:	Any         =       np.array(UpperCamelCase      )
							lowerCamelCase__					:	List[Any]         =       np.array(UpperCamelCase      )
							return np.linalg.norm(a - b      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       List[str]:
							# integer division by time variable
							return consistent_heuristic(UpperCamelCase				,			UpperCamelCase      ) // t





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       Any:
							# manhattan distance
							return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Optional[Any]:
							lowerCamelCase__					:	List[Any]         =       g_function[start] + Wa * heuristics[i](UpperCamelCase				,			UpperCamelCase      )
							return ans





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       Dict:
							lowerCamelCase__					:	Union[str, Any]         =       np.chararray((n, n)      )
							for i in range(UpperCamelCase      ):
														for j in range(UpperCamelCase      ):
																					lowerCamelCase__					:	List[str]         =       """*"""

							for i in range(UpperCamelCase      ):
														for j in range(UpperCamelCase      ):
																					if (j, (n - 1) - i) in blocks:
																												lowerCamelCase__					:	Tuple         =       """#"""

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

							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:-"""      )
							lowerCamelCase__					:	List[str]         =       back_pointer[goal]
							while x != start:
														print(UpperCamelCase				,			end=""" """      )
														lowerCamelCase__					:	Union[str, Any]         =       back_pointer[x]
							print(UpperCamelCase      )
							sys.exit()





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase      )      ->       Dict:
							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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase				,			)      ->       Optional[Any]:
							for itera in range(UpperCamelCase      ):
														open_list[itera].remove_element(UpperCamelCase      )
							# print("s", s)
							# print("j", j)
							((lowerCamelCase__)  ,		(lowerCamelCase__))					:	Tuple         =       s
							lowerCamelCase__					:	Optional[Any]         =       (x - 1, y)
							lowerCamelCase__					:	List[str]         =       (x + 1, y)
							lowerCamelCase__					:	Union[str, Any]         =       (x, y + 1)
							lowerCamelCase__					:	Optional[int]         =       (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      )
																												lowerCamelCase__					:	str         =       -1
																												lowerCamelCase__					:	List[Any]         =       float("""inf"""      )

																					if valid(UpperCamelCase      ) and g_function[neighbours] > g_function[s] + 1:
																												lowerCamelCase__					:	List[str]         =       g_function[s] + 1
																												lowerCamelCase__					:	Dict         =       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  SCREAMING_SNAKE_CASE_       ()      ->       Tuple:
							lowerCamelCase__					:	Tuple         =       []
							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	:			List[str]         ={0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

_A	:			int         =[
    (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	:			Dict         =make_common_ground()


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

# start and end destination
_A	:			Union[str, Any]         =(0, 0)
_A	:			Optional[int]         =(n - 1, n - 1)

_A	:			Optional[Any]         =1





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase				,			UpperCamelCase      )      ->       int:
							lowerCamelCase__					:	Dict         =       {start: 0, goal: float("""inf"""      )}
							lowerCamelCase__					:	Optional[int]         =       {start: -1, goal: -1}
							lowerCamelCase__					:	int         =       []
							lowerCamelCase__					:	Tuple         =       set()

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

							lowerCamelCase__					:	list[int]         =       []
							lowerCamelCase__					:	list[int]         =       []
							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:
																																			lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       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:
																																			lowerCamelCase__					:	Tuple         =       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)









'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

