
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_  :	Any 				=					"""\
@inproceedings{pillutla-etal:mauve:neurips2021,
  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
  booktitle = {NeurIPS},
  year      = {2021}
}

"""

lowerCamelCase_  :	Optional[int] 				=					"""\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.

MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.

For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).

This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""

lowerCamelCase_  :	Optional[int] 				=					"""
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
    predictions: list of generated text to score. Each predictions
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
Optional Args:
    num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
    kmeans_max_iter: maximum number of k-means iterations. Default 500
    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'].
    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
    max_text_length: maximum number of tokens to consider. Default 1024
    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
    mauve_scaling_factor: \"c\" from the paper. Default 5.
    verbose: If True (default), print running time updates
    seed: random seed to initialize k-means cluster assignments.
Returns:
    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
    q_hist: same as above, but with q_text.
Examples:

    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
    >>> import datasets
    >>> mauve = datasets.load_metric('mauve')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
    >>> print(out.mauve) # doctest: +SKIP
    1.0
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,    _KWARGS_DESCRIPTION					)
class     _UpperCamelCase			(   datasets.Metric					):






			'''simple docstring'''

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








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def     A__   (  )					->		Union[str, Any]:
				import os as original_os
				from os import path as original_path
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join

				UpperCamelCase_:							Optional[int]		=  """__test_patch_submodule_mock__"""
				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								# Every way to access os.path.join must be patched, and the rest must stay untouched

								# check os.path.join
								assert isinstance(_test_patching.os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj  )
								assert _test_patching.os.path.join is mock

								# check path.join
								assert isinstance(_test_patching.path       ,      _PatchedModuleObj  )
								assert _test_patching.path.join is mock

								# check join
								assert _test_patching.join is mock

								# check that the other attributes are untouched
								assert _test_patching.os.rename is original_rename
								assert _test_patching.path.dirname is original_dirname
								assert _test_patching.os.path.dirname is original_dirname

								# Even renamed modules or objects must be patched

								# check renamed_os.path.join
								assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_os.path.join is mock

								# check renamed_path.join
								assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_path.join is mock

								# check renamed_join
								assert _test_patching.renamed_join is mock

								# check that the other attributes are untouched
								assert _test_patching.renamed_os.rename is original_rename
								assert _test_patching.renamed_path.dirname is original_dirname
								assert _test_patching.renamed_os.path.dirname is original_dirname

				# check that everthing is back to normal when the patch is over

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join







def     A__   (  )					->		Union[str, Any]:
				assert _test_patching.open is open

				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_builtin_mock__"""
				# _test_patching has "open" in its globals
				assert _test_patching.open is open
				with patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  ):
								assert _test_patching.open is mock

				# check that everthing is back to normal when the patch is over

				assert _test_patching.open is open







def     A__   (  )					->		Optional[Any]:
				# pandas.read_csv is not present in _test_patching
				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_missing_mock__"""
				with patch_submodule(_test_patching       ,      """pandas.read_csv"""       ,      lowerCamelCase  ):
								pass







def     A__   (  )					->		Any:
				# builtin should always be mocked even if they're not in the globals
				# in case they're loaded at one point
				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_missing_builtin_mock__"""
				# _test_patching doesn't have "len" in its globals
				assert getattr(_test_patching       ,      """len"""       ,      lowerCamelCase  ) is None
				with patch_submodule(_test_patching       ,      """len"""       ,      lowerCamelCase  ):
								assert _test_patching.len is mock
				assert _test_patching.len is len







def     A__   (  )					->		Any:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_start_and_stop_mock__"""
				UpperCamelCase_:							List[str]		=  patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  )
				assert _test_patching.open is open
				patch.start()
				assert _test_patching.open is mock
				patch.stop()
				assert _test_patching.open is open







def     A__   (  )					->		List[str]:
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_successive_join__"""
				UpperCamelCase_:							Any		=  """__test_patch_submodule_successive_dirname__"""
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_successive_rename__"""
				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename

				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

    # try another order
				with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename







def     A__   (  )					->		Union[str, Any]:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_doesnt_exist_mock__"""
				with patch_submodule(_test_patching       ,      """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass
				with patch_submodule(_test_patching       ,      """os.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass






