


"""simple docstring"""


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

__lowerCamelCase   :List[Any]						     =		'\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

__lowerCamelCase   :Optional[int]						     =		'\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    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\'].\n    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\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: "c" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'


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


     """simple docstring"""

     def 		a__    (						self:    str						)->     List[Any]:
           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 		a__    (						self:    List[Any]  ,		__a:    Dict  ,		__a:    Optional[int]  ,		__a:    int=None  ,		__a:    str=None  ,		__a:    Optional[Any]=None  ,		__a:    int=None  ,		__a:    Tuple="auto"  ,		__a:    int=-1  ,		__a:    List[Any]=0.9  ,		__a:    int=5  ,		__a:    List[Any]=500  ,		__a:    Optional[Any]="gpt2-large"  ,		__a:    int=-1  ,		__a:    Tuple=1_024  ,		__a:    str=25  ,		__a:    List[Any]=5  ,		__a:    Optional[Any]=True  ,		__a:    List[Any]=25  ,		)->     Dict:
           lowerCamelCase       :       Dict   =						compute_mauve(
               p_text=lowerCAmelCase__  ,		q_text=lowerCAmelCase__  ,		p_features=lowerCAmelCase__  ,		q_features=lowerCAmelCase__  ,		p_tokens=lowerCAmelCase__  ,		q_tokens=lowerCAmelCase__  ,		num_buckets=lowerCAmelCase__  ,		pca_max_data=lowerCAmelCase__  ,		kmeans_explained_var=lowerCAmelCase__  ,		kmeans_num_redo=lowerCAmelCase__  ,		kmeans_max_iter=lowerCAmelCase__  ,		featurize_model_name=lowerCAmelCase__  ,		device_id=lowerCAmelCase__  ,		max_text_length=lowerCAmelCase__  ,		divergence_curve_discretization_size=lowerCAmelCase__  ,		mauve_scaling_factor=lowerCAmelCase__  ,		verbose=lowerCAmelCase__  ,		seed=lowerCAmelCase__  ,		)
           return out








from __future__ import annotations

lowerCAmelCase__			:      Union[str, Any]								=[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]






def 						a__			(				A__,      A__,      A__,      A__,      A__,      ):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the reference grid
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					1
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[
			    [0 for col in range(len(grid[0]				)				)] for row in range(len(A__				)				)
			]  # the action grid

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					init[0]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					init[1]
			SCREAMING_SNAKE_CASE_		:     Dict					=					0
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + heuristic[x][y]  # cost from starting cell to destination cell
			SCREAMING_SNAKE_CASE_		:     List[str]					=					[[f, g, x, y]]

			SCREAMING_SNAKE_CASE_		:     Dict					=					False  # flag that is set when search is complete
			SCREAMING_SNAKE_CASE_		:     Tuple					=					False  # flag set if we can't find expand

			while not found and not resign:
						if len(A__				) == 0:
									raise ValueError('Algorithm is unable to find solution'				)
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									SCREAMING_SNAKE_CASE_		:     Any					=					cell.pop()
									SCREAMING_SNAKE_CASE_		:     Any					=					next_cell[2]
									SCREAMING_SNAKE_CASE_		:     str					=					next_cell[3]
									SCREAMING_SNAKE_CASE_		:     Tuple					=					next_cell[1]

									if x == goal[0] and y == goal[1]:
												SCREAMING_SNAKE_CASE_		:     Dict					=					True
									else:
												for i in range(len(A__				)				):  # to try out different valid actions
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					x + DIRECTIONS[i][0]
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(A__				) and ya >= 0 and ya < len(grid[0]				):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					g + cost
																					SCREAMING_SNAKE_CASE_		:     List[Any]					=					ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya]				)
																					SCREAMING_SNAKE_CASE_		:     int					=					1
																					SCREAMING_SNAKE_CASE_		:     int					=					i
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[]
			SCREAMING_SNAKE_CASE_		:     List[str]					=					goal[0]
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					goal[1]
			invpath.append([x, y]				)  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						SCREAMING_SNAKE_CASE_		:     Any					=					x - DIRECTIONS[action[x][y]][0]
						SCREAMING_SNAKE_CASE_		:     int					=					y - DIRECTIONS[action[x][y]][1]
						SCREAMING_SNAKE_CASE_		:     int					=					xa
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					ya
						invpath.append([x, y]				)

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			for i in range(len(A__				)				):
						path.append(invpath[len(A__				) - 1 - i]				)
			return path, action


if __name__ == "__main__":
				lowerCAmelCase__			:      Union[str, Any]								=[
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				lowerCAmelCase__			:      Any								=[0, 0]
				# all coordinates are given in format [y,x]
				lowerCAmelCase__			:      List[str]								=[len(grid) - 1, len(grid[0]) - 1]
				lowerCAmelCase__			:      str								=1

				# the cost map which pushes the path closer to the goal
				lowerCAmelCase__			:      Union[str, Any]								=[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												lowerCAmelCase__			:      List[str]								=abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																lowerCAmelCase__			:      List[str]								=99

				lowerCAmelCase__							,lowerCAmelCase__			:      Any								=search(grid, init, goal, cost, heuristic)

				print('ACTION MAP')
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])





