





"""simple docstring"""




from collections.abc import Iterable
from typing import Any


class        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__ = None      )	-> List[str]:
								a      :		Optional[Any]	=				value
								a      :		Node | None	=				None  # Added in order to delete a node easier
								a      :		Node | None	=				None
								a      :		Node | None	=				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        __UpperCamelCase				:






							def __init__(   self      ,					lowerCAmelCase__ = None      )	-> Tuple:
								a      :		Dict	=				root






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






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






							def     __a   (   self      ,					lowerCAmelCase__      )	-> 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      ,					lowerCAmelCase__      )	-> None:
								a      :		int	=				Node(lowerCAmelCase__      )  # create a new Node
								if self.empty():  # if Tree is empty
									a      :		Union[str, Any]	=				new_node  # set its root
								else:  # Tree is not empty
									a      :		Any	=				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:
												a      :		Dict	=				new_node  # We insert the new node in a leaf
												break
											else:
												a      :		Optional[int]	=				parent_node.left
										else:
											if parent_node.right is None:
												a      :		List[str]	=				new_node
												break
											else:
												a      :		Tuple	=				parent_node.right
									a      :		Union[str, Any]	=				parent_node






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






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






							def     __a   (   self      ,					lowerCAmelCase__ = None      )	-> Node | None:
								if node is None:
									if self.root is None:
										return None
									a      :		List[str]	=				self.root

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






							def     __a   (   self      ,					lowerCAmelCase__ = None      )	-> Node | None:
								if node is None:
									a      :		str	=				self.root
								if self.root is None:
									return None
								if not self.empty():
									a      :		Any	=				self.root
									while node.left is not None:
										a      :		Dict	=				node.left
								return node






							def     __a   (   self      ,					lowerCAmelCase__      )	-> None:
								a      :		List[str]	=				self.search(lowerCAmelCase__      )  # 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(lowerCAmelCase__      ,					lowerCAmelCase__      )
									elif node.left is None:  # Has only right children
										self.__reassign_nodes(lowerCAmelCase__      ,					node.right      )
									elif node.right is None:  # Has only left children
										self.__reassign_nodes(lowerCAmelCase__      ,					node.left      )
									else:
										a      :		Union[str, Any]	=				self.get_max(
										    node.left      )  # Gets the max value of the left branch
										self.remove(tmp_node.value      )  # type: ignore
										a      :		List[str]	=				(
										    tmp_node.value  # type: ignore
										)  # Assigns the value to the node to delete and keep tree structure






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






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






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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Node | None					) ->list[Node]:






	'''simple docstring'''






	a      :		List[str]	=				[]
	if curr_node is not None:
		a      :		Dict	=				postorder(curr_node.left					) + postorder(curr_node.right					) + [curr_node]
	return node_list






def       _SCREAMING_SNAKE_CASE		(	) ->None:






	'''simple docstring'''






	a      :		Dict	=				(8, 3, 6, 1, 10, 14, 13, 4, 7)
	a      :		Union[str, Any]	=				BinarySearchTree()
	for i in testlist:
		t.insert(_lowercase					)

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

	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(_lowercase					)
		print(_lowercase					)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)



'''simple docstring'''




import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


_lowercase						: List[str]						=						"\\n@inproceedings{snover-etal-2006-study,\n    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n    author = \"Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John\",\n    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",\n    month = aug # \" 8-12\",\n    year = \"2006\",\n    address = \"Cambridge, Massachusetts, USA\",\n    publisher = \"Association for Machine Translation in the Americas\",\n    url = \"https://aclanthology.org/2006.amta-papers.25\",\n    pages = \"223--231\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

_lowercase						: Tuple						=						"\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n"

_lowercase						: Optional[int]						=						"\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    'score' (float): TER score (num_edits / sum_ref_lengths * 100)\n    'num_edits' (int): The cumulative number of edits\n    'ref_length' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\",\n        ...                     \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}\n\n    Example 2:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}\n\n    Example 3:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}\n\n    Example 4:\n        >>> predictions = [\"does this sentence match??\",\n        ...                     \"what about this sentence?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}\n\n    Example 5:\n        >>> predictions = [\"does this sentence match??\",\n        ...                    \"what about this sentence?\",\n        ...                    \"What did the TER metric user say to the developer?\"]\n        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],\n        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],\n        ...             [\"Your jokes are...\", \"...TERrible\"]]\n        >>> ter = datasets.load_metric(\"ter\")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}\n"





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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										if version.parse(scb.__version__    ) < version.parse("""1.4.12"""    ):
																	raise ImportWarning(
																	    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																	    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""    )
										return datasets.MetricInfo(
										    description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""http://www.cs.umd.edu/~snover/tercom/"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
										        {
										            """predictions""": datasets.Value("""string"""				,			id="""sequence"""    ),
										            """references""": datasets.Sequence(datasets.Value("""string"""				,			id="""sequence"""    )				,			id="""references"""    ),
										        }    )				,			codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""]				,			reference_urls=[
										        """https://github.com/jhclark/tercom""",
										    ]				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Any				,			lowercase_				:  Tuple				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			lowercase_				:  bool = False				,			):
										lowercase_					:			int										=       len(references[0]    )
										if any(len(lowercase_    ) != references_per_prediction for refs in references    ):
																	raise ValueError("""Sacrebleu requires the same number of references for each prediction"""    )
										lowercase_					:			Optional[int]										=       [[refs[i] for refs in references] for i in range(lowercase_    )]

										lowercase_					:			List[str]										=       TER(
										    normalized=lowercase_				,			no_punct=lowercase_				,			asian_support=lowercase_				,			case_sensitive=lowercase_				,			)
										lowercase_					:			List[str]										=       sb_ter.corpus_score(lowercase_				,			lowercase_    )

										return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}





