






"""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__    						=			'\\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__    						=			'\\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__    						=			'\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 _lowerCAmelCase						(    datasets.Metric       ):







						def 			A      (    self							)		->							int:
										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   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_="auto"   ,				lowerCAmelCase_=-1   ,				lowerCAmelCase_=0.9   ,				lowerCAmelCase_=5   ,				lowerCAmelCase_=5_0_0   ,				lowerCAmelCase_="gpt2-large"   ,				lowerCAmelCase_=-1   ,				lowerCAmelCase_=1_0_2_4   ,				lowerCAmelCase_=2_5   ,				lowerCAmelCase_=5   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=2_5   ,				)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    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









"""simple docstring"""
import operator as op





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    []
				_SCREAMING_SNAKE_CASE   :		Dict       		=    lambda lowerCamelCase,						lowerCamelCase       : int(x / y       )  # noqa: E731 integer division operation
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8       ),						'Action'.center(12       ),						'Stack',						sep=' | '       )
				print('-' * (30 + len(lowerCamelCase       ))       )

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowerCamelCase       )  # append x to stack
												# output in tabular format
												print(x.rjust(8       ),						('push(' + x + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )
								else:
												_SCREAMING_SNAKE_CASE   :		int       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												_SCREAMING_SNAKE_CASE   :		Tuple       		=    stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8       ),						('pop(' + a + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | '       )

												stack.append(
												    str(opr[x](int(lowerCamelCase       ),						int(lowerCamelCase       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8       ),						('push(' + a + x + b + ')').ljust(12       ),						','.join(lowerCamelCase       ),						sep=' | ',						)

				return int(stack[0]       )


if __name__ == "__main__":
			lowerCAmelCase__    						=			input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
			print('\n\tResult = ', solve(Postfix))

