


"""simple docstring"""
from __future__ import annotations



class 			_UpperCAmelCase :






		'''simple docstring'''

		def __init__(self				,       a_					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				TypeError(
			    '''Matrices must be formed from a list of zero or more lists containing at '''
			    '''least one and the same number of values, each of which must be of type '''
			    '''int or float.'''					)
			if len(a_					) != 0:
				__snake_case	:						List[Any]						=				len(rows[0]					)
				if cols == 0:
					raise error
				for row in rows:
					if len(a_					) != cols:
						raise error
					for value in row:
						if not isinstance(a_				,       (int, float)					):
							raise error
				__snake_case	:						List[Any]						=				rows
			else:
				__snake_case	:						int						=				[]



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return [[row[i] for row in self.rows] for i in range(len(self.rows[0]					)					)]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.rows					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.rows[0]					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return (self.num_rows, self.num_columns)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.order[0] == self.order[1]



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				[
			    [0 if column_num != row_num else 1 for column_num in range(self.num_rows					)]
			    for row_num in range(self.num_rows					)
			]
			return Matrix(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.is_square:
				return 0
			if self.order == (0, 0):
				return 1
			if self.order == (1, 1):
				return int(self.rows[0][0]					)
			if self.order == (2, 2):
				return int(
				    (self.rows[0][0] * self.rows[1][1])
				    - (self.rows[0][1] * self.rows[1][0])					)
			else:
				return sum(
				    self.rows[0][column] * self.cofactors().rows[0][column]
				    for column in range(self.num_columns					)					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return bool(self.determinant()					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				[
			    [
			        self.rows[other_row][other_column]
			        for other_column in range(self.num_columns					)
			        if other_column != column
			    ]
			    for other_row in range(self.num_rows					)
			    if other_row != row
			]
			return Matrix(a_					).determinant()



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			if (row + column) % 2 == 0:
				return self.get_minor(a_				,       a_					)
			return -1 * self.get_minor(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return Matrix(
			    [
			        [self.get_minor(a_				,       a_					) for column in range(self.num_columns					)]
			        for row in range(self.num_rows					)
			    ]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return Matrix(
			    [
			        [
			            self.minors().rows[row][column]
			            if (row + column) % 2 == 0
			            else self.minors().rows[row][column] * -1
			            for column in range(self.minors().num_columns					)
			        ]
			        for row in range(self.minors().num_rows					)
			    ]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				[
			    [self.cofactors().rows[column][row] for column in range(self.num_columns					)]
			    for row in range(self.num_rows					)
			]
			return Matrix(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				self.determinant()
			if not determinant:
				raise TypeError('''Only matrices with a non-zero determinant have an inverse'''					)
			return self.adjugate() * (1 / determinant)



		def __repr__(self					):





			'''simple docstring'''

			return str(self.rows					)



		def __str__(self					):





			'''simple docstring'''

			if self.num_rows == 0:
				return "[]"
			if self.num_rows == 1:
				return "[[" + ". ".join(str(self.rows[0]					)					) + "]]"
			return (
			    "["
			    + "\n ".join(
			        [
			            '''[''' + '''. '''.join([str(a_					) for value in row]					) + '''.]'''
			            for row in self.rows
			        ]					)
			    + "]"
			)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						List[str]						=				TypeError('''Row must be a list containing all ints and/or floats'''					)
			if not isinstance(a_				,       a_					):
				raise type_error
			for value in row:
				if not isinstance(a_				,       (int, float)					):
					raise type_error
			if len(a_					) != self.num_columns:
				raise ValueError(
				    '''Row must be equal in length to the other rows in the matrix'''					)
			if position is None:
				self.rows.append(a_					)
			else:
				__snake_case	:						List[Any]						=				self.rows[0:position] + [row] + self.rows[position:]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				TypeError(
			    '''Column must be a list containing all ints and/or floats'''					)
			if not isinstance(a_				,       a_					):
				raise type_error
			for value in column:
				if not isinstance(a_				,       (int, float)					):
					raise type_error
			if len(a_					) != self.num_rows:
				raise ValueError(
				    '''Column must be equal in length to the other columns in the matrix'''					)
			if position is None:
				__snake_case	:						str						=				[self.rows[i] + [column[i]] for i in range(self.num_rows					)]
			else:
				__snake_case	:						Union[str, Any]						=				[
				    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
				    for i in range(self.num_rows					)
				]



		def __eq__(self				,       a_					):





			'''simple docstring'''

			if not isinstance(a_				,       a_					):
				return NotImplemented
			return self.rows == other.rows



		def __ne__(self				,       a_					):





			'''simple docstring'''

			return not self == other



		def __neg__(self					):





			'''simple docstring'''

			return self * -1



		def __add__(self				,       a_					):





			'''simple docstring'''

			if self.order != other.order:
				raise ValueError('''Addition requires matrices of the same order'''					)
			return Matrix(
			    [
			        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns					)]
			        for i in range(self.num_rows					)
			    ]					)



		def __sub__(self				,       a_					):





			'''simple docstring'''

			if self.order != other.order:
				raise ValueError('''Subtraction requires matrices of the same order'''					)
			return Matrix(
			    [
			        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns					)]
			        for i in range(self.num_rows					)
			    ]					)



		def __mul__(self				,       a_					):





			'''simple docstring'''

			if isinstance(a_				,       (int, float)					):
				return Matrix(
				    [[int(element * other					) for element in row] for row in self.rows]					)
			elif isinstance(a_				,       a_					):
				if self.num_columns != other.num_rows:
					raise ValueError(
					    '''The number of columns in the first matrix must '''
					    '''be equal to the number of rows in the second'''					)
				return Matrix(
				    [
				        [Matrix.dot_product(a_				,       a_					) for column in other.columns()]
				        for row in self.rows
				    ]					)
			else:
				raise TypeError(
				    '''A Matrix can only be multiplied by an int, float, or another matrix'''					)



		def __pow__(self				,       a_					):





			'''simple docstring'''

			if not isinstance(a_				,       a_					):
				raise TypeError('''A Matrix can only be raised to the power of an int'''					)
			if not self.is_square:
				raise ValueError('''Only square matrices can be raised to a power'''					)
			if other == 0:
				return self.identity()
			if other < 0:
				if self.is_invertable():
					return self.inverse() ** (-other)
				raise ValueError(
				    '''Only invertable matrices can be raised to a negative power'''					)
			__snake_case	:						Dict						=				self
			for _ in range(other - 1					):
				result *= self
			return result



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls				,       a_				,       a_					):





			'''simple docstring'''

			return sum(row[i] * column[i] for i in range(len(a_					)					)					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()













"""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_				     =     '\\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_				     =     '\\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_				     =     '\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 					snake_case		(							datasets.Metric						):


   '''simple docstring'''

   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     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 :     int,       _lowerCamelCase :     str,       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Tuple=None,       _lowerCamelCase :     Optional[Any]=None,       _lowerCamelCase :     Union[str, Any]=None,       _lowerCamelCase :     str="auto",       _lowerCamelCase :     Union[str, Any]=-1,       _lowerCamelCase :     List[str]=0.9,       _lowerCamelCase :     int=5,       _lowerCamelCase :     Tuple=5_00,       _lowerCamelCase :     Union[str, Any]="gpt2-large",       _lowerCamelCase :     int=-1,       _lowerCamelCase :     Union[str, Any]=10_24,       _lowerCamelCase :     Union[str, Any]=25,       _lowerCamelCase :     str=5,       _lowerCamelCase :     Any=True,       _lowerCamelCase :     Union[str, Any]=25,       ):

     '''simple docstring'''
     __A									= 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




