





import heapq
import sys

import numpy as np

__lowerCamelCase      :    Optional[int]   		=   tuple[int, int]



class 							SCREAMING_SNAKE_CASE__		:





					"""simple docstring"""






					def __init__(      self  :						Optional[Any] ):
						snake_case__				:   Any     =						[]
						snake_case__				:   int     =						set()



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



					def  _lowercase  (      self  :						List[Any] ):
						return len(self.elements ) == 0



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str							,	__A  :						Dict ):
						if item not in self.set:
							heapq.heappush(self.elements							,	(priority, item) )
							self.set.add(snake_case_ )
						else:
							# update
							# print("update", item)
							snake_case__				:   int     =						[]
							(snake_case__)				:   Dict     =						heapq.heappop(self.elements )
							while x != item:
								temp.append((pri, x) )
								(snake_case__)				:   Any     =						heapq.heappop(self.elements )
							temp.append((priority, item) )
							for pro, xxx in temp:
								heapq.heappush(self.elements							,	(pro, xxx) )



					def  _lowercase  (      self  :						int							,	__A  :						Optional[int] ):
						if item in self.set:
							self.set.remove(snake_case_ )
							snake_case__				:   str     =						[]
							(snake_case__)				:   Optional[Any]     =						heapq.heappop(self.elements )
							while x != item:
								temp.append((pro, x) )
								(snake_case__)				:   Optional[Any]     =						heapq.heappop(self.elements )
							for prito, yyy in temp:
								heapq.heappush(self.elements							,	(prito, yyy) )



					def  _lowercase  (      self  :						Union[str, Any] ):
						return self.elements[0][1]






					def  _lowercase  (      self  :						Union[str, Any] ):
						(snake_case__)				:   int     =						heapq.heappop(self.elements )
						self.set.remove(snake_case_ )
						return (priority, item)







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos  ,						snake_case_      :							TPos     ):
	snake_case__				:   str     =						np.array(snake_case_     )
	snake_case__				:   Optional[int]     =						np.array(snake_case_     )
	return np.linalg.norm(a - b     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos  ,						snake_case_      :							TPos     ):
	return consistent_heuristic(snake_case_  ,						snake_case_     ) // t







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos  ,						snake_case_      :							TPos     ):
	return abs(p[0] - goal[0]     ) + abs(p[1] - goal[1]     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos  ,						snake_case_      :							int  ,						snake_case_      :							TPos  ,						snake_case_      :							dict[TPos, float]     ):
	snake_case__				:   List[str]     =						g_function[start] + Wa * heuristics[i](snake_case_  ,						snake_case_     )
	return ans







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Dict  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Tuple     ):
	snake_case__				:   List[str]     =						np.chararray((n, n)     )
	for i in range(snake_case_     ):
		for j in range(snake_case_     ):
			snake_case__				:   List[Any]     =						"*"

	for i in range(snake_case_     ):
		for j in range(snake_case_     ):
			if (j, (n - 1) - i) in blocks:
				snake_case__				:   List[Any]     =						"#"

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

	for i in range(snake_case_     ):
		for j in range(snake_case_     ):
			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__				:   List[str]     =						back_pointer[goal]
	while x != start:
		print(snake_case_  ,						end=" "     )
		snake_case__				:   Tuple     =						back_pointer[x]
	print(snake_case_     )
	sys.exit()







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos     ):







	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       (				snake_case_      :							Optional[int]  ,						snake_case_      :							List[str]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						):







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

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

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







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						[]
	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


__lowerCamelCase      :    str   		=   {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

__lowerCamelCase      :    Union[str, 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),
]
__lowerCamelCase      :    int   		=   make_common_ground()


__lowerCamelCase      :    Tuple   		=   blocks_blk
# hyper parameters
__lowerCamelCase      :    Any   		=   1
__lowerCamelCase      :    List[Any]   		=   1
__lowerCamelCase      :    List[Any]   		=   20
__lowerCamelCase      :    List[str]   		=   3  # one consistent and two other inconsistent

# start and end destination
__lowerCamelCase      :    List[Any]   		=   (0, 0)
__lowerCamelCase      :    int   		=   (n - 1, n - 1)

__lowerCamelCase      :    Any   		=   1







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							TPos  ,						snake_case_      :							TPos  ,						snake_case_      :							int     ):
	snake_case__				:   Tuple     =						{start: 0, goal: float("inf"     )}
	snake_case__				:   List[str]     =						{start: -1, goal: -1}
	snake_case__				:   Any     =						[]
	snake_case__				:   Union[str, Any]     =						set()

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

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











import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


__lowerCamelCase      :    Optional[int]   		=   """\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

__lowerCamelCase      :    str   		=   """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

__lowerCamelCase      :    str   		=   """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,     _KWARGS_DESCRIPTION		)
class 							SCREAMING_SNAKE_CASE__		(       datasets.Metric		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Dict ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,	citation=_CITATION							,	homepage="https://github.com/krishnap25/mauve"							,	inputs_description=_KWARGS_DESCRIPTION							,	features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"							,	id="sequence" ),
						            "references": datasets.Value("string"							,	id="sequence" ),
						        } )							,	codebase_urls=["https://github.com/krishnap25/mauve"]							,	reference_urls=[
						        "https://arxiv.org/abs/2102.01454",
						        "https://github.com/krishnap25/mauve",
						    ]							,	)






					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						List[str]							,	__A  :						int=None							,	__A  :						List[Any]=None							,	__A  :						Optional[int]=None							,	__A  :						List[Any]=None							,	__A  :						Union[str, Any]="auto"							,	__A  :						Optional[Any]=-1							,	__A  :						Optional[Any]=0.9							,	__A  :						Any=5							,	__A  :						List[Any]=5_0_0							,	__A  :						Tuple="gpt2-large"							,	__A  :						Optional[Any]=-1							,	__A  :						str=1_0_2_4							,	__A  :						Tuple=2_5							,	__A  :						str=5							,	__A  :						Optional[int]=True							,	__A  :						Any=2_5							,	):
						snake_case__				:   List[Any]     =						compute_mauve(
						    p_text=__A							,	q_text=__A							,	p_features=__A							,	q_features=__A							,	p_tokens=__A							,	q_tokens=__A							,	num_buckets=__A							,	pca_max_data=__A							,	kmeans_explained_var=__A							,	kmeans_num_redo=__A							,	kmeans_max_iter=__A							,	featurize_model_name=__A							,	device_id=__A							,	max_text_length=__A							,	divergence_curve_discretization_size=__A							,	mauve_scaling_factor=__A							,	verbose=__A							,	seed=__A							,	)
						return out



