
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


_A     =  "\\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"

_A     =  "\\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"

_A     =  "\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  )       ->   Union[str, 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       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase=None       ,       _UpperCamelCase="auto"       ,       _UpperCamelCase=-1       ,       _UpperCamelCase=0.9       ,       _UpperCamelCase=5       ,       _UpperCamelCase=500       ,       _UpperCamelCase="gpt2-large"       ,       _UpperCamelCase=-1       ,       _UpperCamelCase=1_024       ,       _UpperCamelCase=25       ,       _UpperCamelCase=5       ,       _UpperCamelCase=True       ,       _UpperCamelCase=25       ,       )       ->   str:
							lowerCAmelCase_      			=			compute_mauve(
							    p_text=_UpperCamelCase       ,       q_text=_UpperCamelCase       ,       p_features=_UpperCamelCase       ,       q_features=_UpperCamelCase       ,       p_tokens=_UpperCamelCase       ,       q_tokens=_UpperCamelCase       ,       num_buckets=_UpperCamelCase       ,       pca_max_data=_UpperCamelCase       ,       kmeans_explained_var=_UpperCamelCase       ,       kmeans_num_redo=_UpperCamelCase       ,       kmeans_max_iter=_UpperCamelCase       ,       featurize_model_name=_UpperCamelCase       ,       device_id=_UpperCamelCase       ,       max_text_length=_UpperCamelCase       ,       divergence_curve_discretization_size=_UpperCamelCase       ,       mauve_scaling_factor=_UpperCamelCase       ,       verbose=_UpperCamelCase       ,       seed=_UpperCamelCase       ,       )
							return out





import itertools
import string
from collections.abc import Generator, Iterable






def      lowerCamelCase__       (       __lowerCAmelCase						:						Iterable[str]					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			iter(__lowerCAmelCase       )
			while True:
						lowerCAmelCase_      			=			tuple(itertools.islice(__lowerCAmelCase					,							__lowerCAmelCase       )       )
						if not chunk:
									return
						yield chunk






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"".join([c.upper() for c in dirty if c in string.ascii_letters]       )
			lowerCAmelCase_      			=			""

			if len(__lowerCAmelCase       ) < 2:
						return dirty

			for i in range(len(__lowerCAmelCase       ) - 1       ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(__lowerCAmelCase       ) & 1:
						clean += "X"

			return clean






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			"ABCDEFGHIKLMNOPQRSTUVWXYZ"
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCAmelCase_      			=			[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(__lowerCAmelCase       )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(__lowerCAmelCase       )

			return table






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			prepare_input(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			generate_table(__lowerCAmelCase       )
			lowerCAmelCase_      			=			""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(__lowerCAmelCase					,							2       ):
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )
						lowerCAmelCase_			,						lowerCAmelCase_      			=			divmod(table.index(__lowerCAmelCase       )					,							5       )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



