


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 	__lowercase    (datasets.Metric  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""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 					UpperCamelCase__     (      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			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     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












'''simple docstring'''






import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


__UpperCamelCase					    =	"http://www.mocksite.com/file1.txt"
__UpperCamelCase					    =	"\"text\": [\"foo\", \"foo\"]"
__UpperCamelCase					    =	"6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8"




class  _A :
		lowercase__:   str				     =   200
		lowercase__:   List[str]				     =   {'''Content-Length''': '''100'''}
		lowercase__:   Union[str, Any]				     =   {}

		def 	lowercase__     (   self					:   Any  ,      **__magic_name__					:   List[Any]			)					->    Dict:


					"""simple docstring"""

					return [bytes(__magic_name__  ,      """utf-8"""			)]







def 					_a						(       *_lowerCamelCase    ,	**_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			return MockResponse()







@pytest.mark.parametrize("""urls_type"""    ,	[str, list, dict] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			import requests

			monkeypatch.setattr(_lowerCamelCase    ,	"""request"""    ,	_lowerCamelCase )

			__snake_case					:      Union[str, Any]							      =     URL
			if issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      str							      =     url
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Dict							      =     [url]
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Union[str, Any]							      =     {"""train""": url}
			__snake_case					:      Dict							      =     """dummy"""
			__snake_case					:      List[str]							      =     """downloads"""
			__snake_case					:      List[Any]							      =     tmp_path
			__snake_case					:      List[Any]							      =     DownloadConfig(
			    cache_dir=os.path.join(_lowerCamelCase    ,	_lowerCamelCase )    ,	use_etag=_lowerCamelCase    ,	)
			__snake_case					:      List[str]							      =     DownloadManager(dataset_name=_lowerCamelCase    ,	download_config=_lowerCamelCase )
			__snake_case					:      int							      =     dl_manager.download(_lowerCamelCase )
			__snake_case					:      Tuple							      =     urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									__snake_case					:      Any							      =     [downloaded_paths]
									__snake_case					:      List[Any]							      =     [urls]
						elif isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									assert "train" in downloaded_paths.keys()
									__snake_case					:      Tuple							      =     downloaded_paths.values()
									__snake_case					:      Optional[int]							      =     urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(_lowerCamelCase    ,	_lowerCamelCase ):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									__snake_case					:      List[str]							      =     Path(_lowerCamelCase )
									__snake_case					:      Any							      =     downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									__snake_case					:      Union[str, Any]							      =     downloaded_path.read_text()
									assert content == CONTENT
									__snake_case					:      List[str]							      =     downloaded_path.with_suffix(""".json""" )
									assert metadata_downloaded_path.exists()
									__snake_case					:      Union[str, Any]							      =     json.loads(metadata_downloaded_path.read_text() )
									assert metadata_content == {"url": URL, "etag": None}







@pytest.mark.parametrize("""paths_type"""    ,	[str, list, dict] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				List[Any]:



			"""simple docstring"""


			__snake_case					:      Any							      =     str(_lowerCamelCase )
			if issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Optional[int]							      =     filename
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Tuple							      =     [filename]
			elif issubclass(_lowerCamelCase    ,	_lowerCamelCase ):
						__snake_case					:      Dict							      =     {"""train""": filename}
			__snake_case					:      Optional[Any]							      =     """dummy"""
			__snake_case					:      List[Any]							      =     xz_file.parent
			__snake_case					:      int							      =     """extracted"""
			__snake_case					:      Dict							      =     DownloadConfig(
			    cache_dir=_lowerCamelCase    ,	use_etag=_lowerCamelCase    ,	)
			__snake_case					:      List[str]							      =     DownloadManager(dataset_name=_lowerCamelCase    ,	download_config=_lowerCamelCase )
			__snake_case					:      Optional[Any]							      =     dl_manager.extract(_lowerCamelCase )
			__snake_case					:      Union[str, Any]							      =     paths
			for extracted_paths in [extracted_paths]:
						if isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									__snake_case					:      Dict							      =     [extracted_paths]
									__snake_case					:      int							      =     [paths]
						elif isinstance(_lowerCamelCase    ,	_lowerCamelCase ):
									assert "train" in extracted_paths.keys()
									__snake_case					:      int							      =     extracted_paths.values()
									__snake_case					:      int							      =     paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(_lowerCamelCase    ,	_lowerCamelCase ):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									__snake_case					:      Any							      =     Path(_lowerCamelCase )
									__snake_case					:      str							      =     extracted_path.parts
									assert parts[-1] == hash_url_to_filename(_lowerCamelCase    ,	etag=_lowerCamelCase )
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									__snake_case					:      Optional[int]							      =     extracted_path.read_text()
									__snake_case					:      str							      =     text_file.read_text()
									assert extracted_file_content == expected_file_content







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			assert path.endswith(""".jsonl""" )
			for num_items, line in enumerate(_lowerCamelCase    ,	start=1 ):
						__snake_case					:      Tuple							      =     json.loads(line.decode("""utf-8""" ) )
						assert item.keys() == {"col_1", "col_2", "col_3"}
			assert num_items == 4







@pytest.mark.parametrize("""archive_jsonl"""    ,	["""tar_jsonl_path""", """zip_jsonl_path"""] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      Any							      =     request.getfixturevalue(_lowerCamelCase )
			__snake_case					:      str							      =     DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
						_test_jsonl(_lowerCamelCase    ,	_lowerCamelCase )
			assert num_jsonl == 2







@pytest.mark.parametrize("""archive_nested_jsonl"""    ,	["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""] )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				List[str]:



			"""simple docstring"""


			__snake_case					:      int							      =     request.getfixturevalue(_lowerCamelCase )
			__snake_case					:      List[str]							      =     DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(_lowerCamelCase )    ,	start=1 ):
									_test_jsonl(_lowerCamelCase    ,	_lowerCamelCase )
			assert num_tar == 1
			assert num_jsonl == 2







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(_lowerCamelCase )    ,	start=1 ):
						assert os.path.basename(_lowerCamelCase ) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2


