'''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


_lowercase						: Optional[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"

_lowercase						: 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"

_lowercase						: List[Any]						=						"\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     __magic_name__						(    datasets.Metric):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  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 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Any				,			lowercase_				:  Any=None				,			lowercase_				:  str=None				,			lowercase_				:  Dict=None				,			lowercase_				:  Any=None				,			lowercase_				:  int="auto"				,			lowercase_				:  Tuple=-1				,			lowercase_				:  str=0.9				,			lowercase_				:  Union[str, Any]=5				,			lowercase_				:  List[str]=500				,			lowercase_				:  Union[str, Any]="gpt2-large"				,			lowercase_				:  List[Any]=-1				,			lowercase_				:  str=1024				,			lowercase_				:  List[str]=25				,			lowercase_				:  str=5				,			lowercase_				:  List[Any]=True				,			lowercase_				:  Tuple=25				,			):
										lowercase_					:			List[str]										=       compute_mauve(
										    p_text=lowercase_				,			q_text=lowercase_				,			p_features=lowercase_				,			q_features=lowercase_				,			p_tokens=lowercase_				,			q_tokens=lowercase_				,			num_buckets=lowercase_				,			pca_max_data=lowercase_				,			kmeans_explained_var=lowercase_				,			kmeans_num_redo=lowercase_				,			kmeans_max_iter=lowercase_				,			featurize_model_name=lowercase_				,			device_id=lowercase_				,			max_text_length=lowercase_				,			divergence_curve_discretization_size=lowercase_				,			mauve_scaling_factor=lowercase_				,			verbose=lowercase_				,			seed=lowercase_				,			)
										return out






'''simple docstring'''




import operator as op




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[Any] )			->      int:
							lowercase_					:			Optional[Any]										=       []
							lowercase_					:			str										=       lambda UpperCAmelCase__      ,      UpperCAmelCase__ : int(x / y )  # noqa: E731 integer division operation
							lowercase_					:			Optional[Any]										=       {
							    """^""": 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(UpperCAmelCase__ )) )

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

																					lowercase_					:			Optional[int]										=       stack.pop()  # pop stack
																					# output in tabular format
																					print("""""".rjust(8 )      ,      ("""pop(""" + a + """)""").ljust(12 )      ,      """,""".join(UpperCAmelCase__ )      ,      sep=""" | """ )

																					stack.append(
																					    str(opr[x](int(UpperCAmelCase__ )      ,      int(UpperCAmelCase__ ) ) ) )  # 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(UpperCAmelCase__ )      ,      sep=""" | """      ,      )

							return int(stack[0] )


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





