
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_A     :   int     =  """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

_A     :   Tuple     =  """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

_A     :   Any     =  """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""






def      lowerCamelCase__       (       __lowerCAmelCase						:						List[Any]					,							__lowerCAmelCase						:						Optional[Any]       ):




			"""simple docstring"""


			return float((preds == labels).mean()       )






def      lowerCamelCase__       (       __lowerCAmelCase						:						Optional[int]					,							__lowerCAmelCase						:						List[Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			simple_accuracy(_lowerCamelCase					,							_lowerCamelCase       )
			lowerCAmelCase_      			=			float(fa_score(y_true=_lowerCamelCase					,							y_pred=_lowerCamelCase       )       )
			return {
			    "accuracy": acc,
			    "f1": fa,
			}






def      lowerCamelCase__       (       __lowerCAmelCase						:						Tuple					,							__lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			float(pearsonr(_lowerCamelCase					,							_lowerCamelCase       )[0]       )
			lowerCAmelCase_      			=			float(spearmanr(_lowerCamelCase					,							_lowerCamelCase       )[0]       )
			return {
			    "pearson": pearson_corr,
			    "spearmanr": spearman_corr,
			}





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




				def   __a		(      self  )       ->   List[str]:





							if self.config_name not in [
							    "sst2",
							    "mnli",
							    "mnli_mismatched",
							    "mnli_matched",
							    "cola",
							    "stsb",
							    "mrpc",
							    "qqp",
							    "qnli",
							    "rte",
							    "wnli",
							    "hans",
							]:
										raise KeyError(
										    "You should supply a configuration name selected in "
										    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
										    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"  )
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,       citation=_CITATION       ,       inputs_description=_KWARGS_DESCRIPTION       ,       features=datasets.Features(
							        {
							            "predictions": datasets.Value("int64" if self.config_name != "stsb" else "float32"  ),
							            "references": datasets.Value("int64" if self.config_name != "stsb" else "float32"  ),
							        }  )       ,       codebase_urls=[]       ,       reference_urls=[]       ,       format="numpy"       ,       )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   Tuple:





							if self.config_name == "cola":
										return {"matthews_correlation": matthews_corrcoef(UpperCamelCase__       ,       UpperCamelCase__  )}
							elif self.config_name == "stsb":
										return pearson_and_spearman(UpperCamelCase__       ,       UpperCamelCase__  )
							elif self.config_name in ["mrpc", "qqp"]:
										return acc_and_fa(UpperCamelCase__       ,       UpperCamelCase__  )
							elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
										return {"accuracy": simple_accuracy(UpperCamelCase__       ,       UpperCamelCase__  )}
							else:
										raise KeyError(
										    "You should supply a configuration name selected in "
										    "[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", "
										    "\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]"  )





from collections.abc import Iterable
from typing import Any





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   str:
							lowerCAmelCase_      			=			value
							lowerCAmelCase_      			=			None  # Added in order to delete a node easier
							lowerCAmelCase_      			=			None
							lowerCAmelCase_      			=			None




				def __repr__(      self  )       ->   str:
							from pprint import pformat

							if self.left is None and self.right is None:
										return str(self.value  )
							return pformat({f"""{self.value}""": (self.left, self.right)}       ,       indent=1  )





class 			_lowerCAmelCase      :




				def __init__(      self       ,       _UpperCamelCase = None  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			root




				def __str__(      self  )       ->   str:
							return str(self.root  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if new_children is not None:  # reset its kids
										lowerCAmelCase_      			=			node.parent
							if node.parent is not None:  # reset its parent
										if self.is_right(_UpperCamelCase  ):  # If it is the right children
													lowerCAmelCase_      			=			new_children
										else:
													lowerCAmelCase_      			=			new_children
							else:
										lowerCAmelCase_      			=			new_children




				def   __a		(      self       ,       _UpperCamelCase  )       ->   bool:
							if node.parent and node.parent.right:
										return node == node.parent.right
							return False




				def   __a		(      self  )       ->   bool:
							return self.root is None




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			Node(_UpperCamelCase  )  # create a new Node
							if self.empty():  # if Tree is empty
										lowerCAmelCase_      			=			new_node  # set its root
							else:  # Tree is not empty
										lowerCAmelCase_      			=			self.root  # from root
										if parent_node is None:
													return
										while True:  # While we don't get to a leaf
													if value < parent_node.value:  # We go left
																if parent_node.left is None:
																			lowerCAmelCase_      			=			new_node  # We insert the new node in a leaf
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.left
													else:
																if parent_node.right is None:
																			lowerCAmelCase_      			=			new_node
																			break
																else:
																			lowerCAmelCase_      			=			parent_node.right
										lowerCAmelCase_      			=			parent_node




				def   __a		(      self       ,       *_UpperCamelCase  )       ->   None:
							for value in values:
										self.__insert(_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Node | None:
							if self.empty():
										raise IndexError("Warning: Tree is empty! please use another."  )
							else:
										lowerCAmelCase_      			=			self.root
										# use lazy evaluation here to avoid NoneType Attribute error
										while node is not None and node.value is not value:
													lowerCAmelCase_      			=			node.left if value < node.value else node.right
										return node




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										if self.root is None:
													return None
										lowerCAmelCase_      			=			self.root

							if not self.empty():
										while node.right is not None:
													lowerCAmelCase_      			=			node.right
							return node




				def   __a		(      self       ,       _UpperCamelCase = None  )       ->   Node | None:
							if node is None:
										lowerCAmelCase_      			=			self.root
							if self.root is None:
										return None
							if not self.empty():
										lowerCAmelCase_      			=			self.root
										while node.left is not None:
													lowerCAmelCase_      			=			node.left
							return node




				def   __a		(      self       ,       _UpperCamelCase  )       ->   None:
							lowerCAmelCase_      			=			self.search(_UpperCamelCase  )  # Look for the node with that label
							if node is not None:
										if node.left is None and node.right is None:  # If it has no children
													self.__reassign_nodes(_UpperCamelCase       ,       _UpperCamelCase  )
										elif node.left is None:  # Has only right children
													self.__reassign_nodes(_UpperCamelCase       ,       node.right  )
										elif node.right is None:  # Has only left children
													self.__reassign_nodes(_UpperCamelCase       ,       node.left  )
										else:
													lowerCAmelCase_      			=			self.get_max(
													    node.left  )  # Gets the max value of the left branch
													self.remove(tmp_node.value  )  # type: ignore
													lowerCAmelCase_      			=			(
													    tmp_node.value  # type: ignore
													)  # Assigns the value to the node to delete and keep tree structure




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Iterable:
							if node is not None:
										yield node  # Preorder Traversal
										yield from self.preorder_traverse(node.left  )
										yield from self.preorder_traverse(node.right  )




				def   __a		(      self       ,       _UpperCamelCase=None  )       ->   Any:
							if traversal_function is None:
										return self.preorder_traverse(self.root  )
							else:
										return traversal_function(self.root  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   None:
							if node:
										self.inorder(_UpperCamelCase       ,       node.left  )
										arr.append(node.value  )
										self.inorder(_UpperCamelCase       ,       node.right  )




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			[]
							self.inorder(_UpperCamelCase       ,       _UpperCamelCase  )  # append all values to list using inorder traversal
							return arr[k - 1]






def      lowerCamelCase__       (       __lowerCAmelCase						:						Node | None       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			[]
			if curr_node is not None:
						lowerCAmelCase_      			=			postorder(curr_node.left       ) + postorder(curr_node.right       ) + [curr_node]
			return node_list






def      lowerCamelCase__       (       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			(8, 3, 6, 1, 10, 14, 13, 4, 7)
			lowerCAmelCase_      			=			BinarySearchTree()
			for i in testlist:
						t.insert(__lowerCAmelCase       )

			# Prints all the elements of the list in order traversal
			print(__lowerCAmelCase       )

			if t.search(6       ) is not None:
						print("The value 6 exists"       )
			else:
						print("The value 6 doesn't exist"       )

			if t.search(-1       ) is not None:
						print("The value -1 exists"       )
			else:
						print("The value -1 doesn't exist"       )

			if not t.empty():
						print("Max Value: "					,							t.get_max().value       )  # type: ignore
						print("Min Value: "					,							t.get_min().value       )  # type: ignore

			for i in testlist:
						t.remove(__lowerCAmelCase       )
						print(__lowerCAmelCase       )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)



