
'''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[Any]    							=       "\\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 :  Optional[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 :  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  lowerCAmelCase__   (  datasets.Metric     ):






					def 	_snake_case    (   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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE="auto"				,							__SCREAMING_SNAKE_CASE=-1				,							__SCREAMING_SNAKE_CASE=0.9				,							__SCREAMING_SNAKE_CASE=5				,							__SCREAMING_SNAKE_CASE=5_00				,							__SCREAMING_SNAKE_CASE="gpt2-large"				,							__SCREAMING_SNAKE_CASE=-1				,							__SCREAMING_SNAKE_CASE=10_24				,							__SCREAMING_SNAKE_CASE=25				,							__SCREAMING_SNAKE_CASE=5				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=25				,							):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       compute_mauve(
											    p_text=__SCREAMING_SNAKE_CASE				,							q_text=__SCREAMING_SNAKE_CASE				,							p_features=__SCREAMING_SNAKE_CASE				,							q_features=__SCREAMING_SNAKE_CASE				,							p_tokens=__SCREAMING_SNAKE_CASE				,							q_tokens=__SCREAMING_SNAKE_CASE				,							num_buckets=__SCREAMING_SNAKE_CASE				,							pca_max_data=__SCREAMING_SNAKE_CASE				,							kmeans_explained_var=__SCREAMING_SNAKE_CASE				,							kmeans_num_redo=__SCREAMING_SNAKE_CASE				,							kmeans_max_iter=__SCREAMING_SNAKE_CASE				,							featurize_model_name=__SCREAMING_SNAKE_CASE				,							device_id=__SCREAMING_SNAKE_CASE				,							max_text_length=__SCREAMING_SNAKE_CASE				,							divergence_curve_discretization_size=__SCREAMING_SNAKE_CASE				,							mauve_scaling_factor=__SCREAMING_SNAKE_CASE				,							verbose=__SCREAMING_SNAKE_CASE				,							seed=__SCREAMING_SNAKE_CASE				,							)
											return out




'''simple docstring'''




from __future__ import annotations

import math
import random
from typing import Any





class     __magic_name__						:







			def __init__(	self				:  Any    ):
										lowercase_					:			list[Any]										=       []
										lowercase_					:			int										=       0
										lowercase_					:			int										=       0







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										return self.head == self.tail







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Any    ):
										self.data.append(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.tail + 1







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[Any]										=       self.data[self.head]
										lowercase_					:			Optional[int]										=       self.head + 1
										return ret







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										return self.tail - self.head







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										print(self.data    )
										print("""**************"""    )
										print(self.data[self.head : self.tail]    )





class     __magic_name__						:







			def __init__(	self				:  Any				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[Any]										=       data
										lowercase_					:			MyNode | None										=       None
										lowercase_					:			MyNode | None										=       None
										lowercase_					:			int										=       1







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										return self.data







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return self.left







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										return self.right







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return self.height







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any    ):
										lowercase_					:			Dict										=       data







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  MyNode | None    ):
										lowercase_					:			Optional[Any]										=       node







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  MyNode | None    ):
										lowercase_					:			str										=       node







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  int    ):
										lowercase_					:			Tuple										=       height




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode | None )			->      int:
							if node is None:
														return 0
							return node.get_height()




def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					int )			->      int:
							if a > b:
														return a
							return b




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      MyNode:
							print("""left rotation node:"""      ,      node.get_data() )
							lowercase_					:			Union[str, Any]										=       node.get_left()
							assert ret is not None
							node.set_left(ret.get_right() )
							ret.set_right(UpperCAmelCase__ )
							lowercase_					:			Any										=       my_max(get_height(node.get_right() )      ,      get_height(node.get_left() ) ) + 1
							node.set_height(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       my_max(get_height(ret.get_right() )      ,      get_height(ret.get_left() ) ) + 1
							ret.set_height(UpperCAmelCase__ )
							return ret




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      MyNode:
							print("""right rotation node:"""      ,      node.get_data() )
							lowercase_					:			Dict										=       node.get_right()
							assert ret is not None
							node.set_right(ret.get_left() )
							ret.set_left(UpperCAmelCase__ )
							lowercase_					:			Tuple										=       my_max(get_height(node.get_right() )      ,      get_height(node.get_left() ) ) + 1
							node.set_height(UpperCAmelCase__ )
							lowercase_					:			str										=       my_max(get_height(ret.get_right() )      ,      get_height(ret.get_left() ) ) + 1
							ret.set_height(UpperCAmelCase__ )
							return ret




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      MyNode:
							lowercase_					:			Optional[Any]										=       node.get_left()
							assert left_child is not None
							node.set_left(left_rotation(UpperCAmelCase__ ) )
							return right_rotation(UpperCAmelCase__ )




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      MyNode:
							lowercase_					:			Optional[Any]										=       node.get_right()
							assert right_child is not None
							node.set_right(right_rotation(UpperCAmelCase__ ) )
							return left_rotation(UpperCAmelCase__ )




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode | None      ,      UpperCAmelCase__       :					Any )			->      MyNode | None:
							if node is None:
														return MyNode(UpperCAmelCase__ )
							if data < node.get_data():
														node.set_left(insert_node(node.get_left()      ,      UpperCAmelCase__ ) )
														if (
														    get_height(node.get_left() ) - get_height(node.get_right() ) == 2
														):  # an unbalance detected
																					lowercase_					:			List[Any]										=       node.get_left()
																					assert left_child is not None
																					if (
																					    data < left_child.get_data()
																					):  # new node is the left child of the left child
																												lowercase_					:			List[Any]										=       right_rotation(UpperCAmelCase__ )
																					else:
																												lowercase_					:			Any										=       lr_rotation(UpperCAmelCase__ )
							else:
														node.set_right(insert_node(node.get_right()      ,      UpperCAmelCase__ ) )
														if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
																					lowercase_					:			Any										=       node.get_right()
																					assert right_child is not None
																					if data < right_child.get_data():
																												lowercase_					:			Optional[int]										=       rl_rotation(UpperCAmelCase__ )
																					else:
																												lowercase_					:			Optional[int]										=       left_rotation(UpperCAmelCase__ )
							lowercase_					:			Optional[int]										=       my_max(get_height(node.get_right() )      ,      get_height(node.get_left() ) ) + 1
							node.set_height(UpperCAmelCase__ )
							return node




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      Any:
							while True:
														lowercase_					:			Any										=       root.get_right()
														if right_child is None:
																					break
														lowercase_					:			List[str]										=       right_child
							return root.get_data()




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode )			->      Any:
							while True:
														lowercase_					:			List[Any]										=       root.get_left()
														if left_child is None:
																					break
														lowercase_					:			str										=       left_child
							return root.get_data()




def 							lowerCamelCase	(      UpperCAmelCase__       :					MyNode      ,      UpperCAmelCase__       :					Any )			->      MyNode | None:
							lowercase_					:			Union[str, Any]										=       root.get_left()
							lowercase_					:			int										=       root.get_right()
							if root.get_data() == data:
														if left_child is not None and right_child is not None:
																					lowercase_					:			Any										=       get_left_most(UpperCAmelCase__ )
																					root.set_data(UpperCAmelCase__ )
																					root.set_right(del_node(UpperCAmelCase__      ,      UpperCAmelCase__ ) )
														elif left_child is not None:
																					lowercase_					:			Dict										=       left_child
														elif right_child is not None:
																					lowercase_					:			Dict										=       right_child
														else:
																					return None
							elif root.get_data() > data:
														if left_child is None:
																					print("""No such data""" )
																					return root
														else:
																					root.set_left(del_node(UpperCAmelCase__      ,      UpperCAmelCase__ ) )
							else:  # root.get_data() < data
														if right_child is None:
																					return root
														else:
																					root.set_right(del_node(UpperCAmelCase__      ,      UpperCAmelCase__ ) )

							if get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == 2:
														assert right_child is not None
														if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
																					lowercase_					:			str										=       left_rotation(UpperCAmelCase__ )
														else:
																					lowercase_					:			List[str]										=       rl_rotation(UpperCAmelCase__ )
							elif get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == -2:
														assert left_child is not None
														if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
																					lowercase_					:			Optional[Any]										=       right_rotation(UpperCAmelCase__ )
														else:
																					lowercase_					:			List[str]										=       lr_rotation(UpperCAmelCase__ )
							lowercase_					:			int										=       my_max(get_height(root.get_right() )      ,      get_height(root.get_left() ) ) + 1
							root.set_height(UpperCAmelCase__ )
							return root





class     __magic_name__						:







			def __init__(	self				:  List[str]    ):
										lowercase_					:			MyNode | None										=       None







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										return get_height(self.root    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any    ):
										print("""insert:""" + str(lowercase_    )    )
										lowercase_					:			Dict										=       insert_node(self.root				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any    ):
										print("""delete:""" + str(lowercase_    )    )
										if self.root is None:
																	print("""Tree is empty!"""    )
																	return
										lowercase_					:			int										=       del_node(self.root				,			lowercase_    )







			def __str__(	self				:  Optional[int]				,			):  # a level traversale, gives a more intuitive look on the tree
										lowercase_					:			int										=       """"""
										lowercase_					:			Optional[int]										=       MyQueue()
										q.push(self.root    )
										lowercase_					:			Optional[Any]										=       self.get_height()
										if layer == 0:
																	return output
										lowercase_					:			Optional[int]										=       0
										while not q.is_empty():
																	lowercase_					:			Any										=       q.pop()
																	lowercase_					:			List[Any]										=       """ """ * int(math.pow(2				,			layer - 1    )    )
																	output += space
																	if node is None:
																								output += "*"
																								q.push(lowercase_    )
																								q.push(lowercase_    )
																	else:
																								output += str(node.get_data()    )
																								q.push(node.get_left()    )
																								q.push(node.get_right()    )
																	output += space
																	lowercase_					:			Union[str, Any]										=       cnt + 1
																	for i in range(100    ):
																								if cnt == math.pow(2				,			lowercase_    ) - 1:
																															lowercase_					:			Optional[Any]										=       layer - 1
																															if layer == 0:
																																						output += "\n*************************************"
																																						return output
																															output += "\n"
																															break
										output += "\n*************************************"
										return output




def 							lowerCamelCase	(      )			->      None:
							import doctest

							doctest.testmod()


if __name__ == "__main__":
						_test()
						_lowercase						: Optional[Any]						=						AVLtree()
						_lowercase						: Optional[Any]						=						list(range(10))
						random.shuffle(lst)
						for i in lst:
												t.insert(i)
												print(str(t))
						random.shuffle(lst)
						for i in lst:
												t.del_node(i)
												print(str(t))





