

"""simple docstring"""





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

import datasets


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

__UpperCamelCase  :      Any							      =		'''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
'''

__UpperCamelCase  :      Union[str, Any]							      =		'''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''







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






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						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        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any] = False   ,lowercase_			:				str = False   ,lowercase_			:				Optional[int] = False   ,lowercase_			:				str = False   ,):
						lowerCAmelCase__	:						List[str]  				=	len(references[0]		)
						if any(len(lowerCAmelCase__		) != references_per_prediction for refs in references		):
							raise ValueError('''Sacrebleu requires the same number of references for each prediction'''		)
						lowerCAmelCase__	:						str  				=	[[refs[i] for refs in references] for i in range(lowerCAmelCase__		)]

						lowerCAmelCase__	:						Tuple  				=	TER(
						    normalized=lowerCAmelCase__   ,no_punct=lowerCAmelCase__   ,asian_support=lowerCAmelCase__   ,case_sensitive=lowerCAmelCase__   ,)
						lowerCAmelCase__	:						Optional[Any]  				=	sb_ter.corpus_score(lowerCAmelCase__   ,lowerCAmelCase__		)

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








"""simple docstring"""




from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__ = None				)     ->							None:
								if components is None:
														SCREAMING_SNAKE_CASE													=					[]
								SCREAMING_SNAKE_CASE													=					list(lowerCAmelCase__				)







		def __len__(	self				)     ->							int:
								return len(self.__components				)







		def __str__(	self				)     ->							str:
								return "(" + ",".join(map(lowerCAmelCase__						,       self.__components				)				) + ")"







		def __add__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] + other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:
														raise Exception('must have the same size'				)







		def __sub__(	self						,       lowerCAmelCase__				)     ->							Vector:
								SCREAMING_SNAKE_CASE													=					len(self				)
								if size == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					[self.__components[i] - other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return Vector(lowerCAmelCase__				)
								else:  # error case
														raise Exception('must have the same size'				)







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							float:
								...







		def __mul__(	self						,       lowerCAmelCase__				)     ->							float | Vector:
								if isinstance(lowerCAmelCase__						,       (float, int)				):
														SCREAMING_SNAKE_CASE													=					[c * other for c in self.__components]
														return Vector(lowerCAmelCase__				)
								elif isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and len(self				) == len(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					len(self				)
														SCREAMING_SNAKE_CASE													=					[self.__components[i] * other.component(lowerCAmelCase__				) for i in range(lowerCAmelCase__				)]
														return sum(lowerCAmelCase__				)
								else:  # error case
														raise Exception('invalid operand!'				)







		def 							__A						(	self				)     ->							Vector:
								return Vector(self.__components				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							float:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				) and -len(self.__components				) <= i < len(self.__components				):
														return self.__components[i]
								else:
														raise Exception('index out of range'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								assert -len(self.__components				) <= pos < len(self.__components				)
								SCREAMING_SNAKE_CASE													=					value







		def 							__A						(	self				)     ->							float:
								if len(self.__components				) == 0:
														raise Exception('Vector is empty'				)
								SCREAMING_SNAKE_CASE													=					[c**2 for c in self.__components]
								return math.sqrt(sum(lowerCAmelCase__				)				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = False				)     ->							float:
								SCREAMING_SNAKE_CASE													=					self * other
								SCREAMING_SNAKE_CASE													=					self.euclidean_length() * other.euclidean_length()
								if deg:
														return math.degrees(math.acos(num / den				)				)
								else:
														return math.acos(num / den				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return Vector([0] * dimension  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						assert isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) and (isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ))
						SCREAMING_SNAKE_CASE													=					[0] * dimension
						SCREAMING_SNAKE_CASE													=					1
						return Vector(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				float ,			SCREAMING_SNAKE_CASE_		:				Vector ,			SCREAMING_SNAKE_CASE_		:				Vector  )   ->						Vector:
						assert (
						    isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						    and isinstance(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						    and (isinstance(SCREAMING_SNAKE_CASE_ ,			(int, float)  ))
						)
						return x * scalar + y


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Vector:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[random.randint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Vector(SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	:




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								SCREAMING_SNAKE_CASE													=					matrix
								SCREAMING_SNAKE_CASE													=					w
								SCREAMING_SNAKE_CASE													=					h







		def __str__(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					''
								for i in range(self.__height				):
														ans += "|"
														for j in range(self.__width				):
																				if j < self.__width - 1:
																										ans += str(self.__matrix[i][j]				) + ","
																				else:
																										ans += str(self.__matrix[i][j]				) + "|\n"
								return ans







		def __add__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								if self.__width == other.width() and self.__height == other.height():
														SCREAMING_SNAKE_CASE													=					[]
														for i in range(self.__height				):
																				SCREAMING_SNAKE_CASE													=					[
																				    self.__matrix[i][j] + other.component(lowerCAmelCase__						,       lowerCAmelCase__				)
																				    for j in range(self.__width				)
																				]
																				matrix.append(lowerCAmelCase__				)
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								else:
														raise Exception('matrix must have the same dimension!'				)







		def __sub__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								if self.__width == other.width() and self.__height == other.height():
														SCREAMING_SNAKE_CASE													=					[]
														for i in range(self.__height				):
																				SCREAMING_SNAKE_CASE													=					[
																				    self.__matrix[i][j] - other.component(lowerCAmelCase__						,       lowerCAmelCase__				)
																				    for j in range(self.__width				)
																				]
																				matrix.append(lowerCAmelCase__				)
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								else:
														raise Exception('matrices must have the same dimension!'				)







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Matrix:
								...







		@overload
		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector:
								...







		def __mul__(	self						,       lowerCAmelCase__				)     ->							Vector | Matrix:
								if isinstance(lowerCAmelCase__						,       lowerCAmelCase__				):  # matrix-vector
														if len(lowerCAmelCase__				) == self.__width:
																				SCREAMING_SNAKE_CASE													=					zero_vector(self.__height				)
																				for i in range(self.__height				):
																										SCREAMING_SNAKE_CASE													=					[
																										    self.__matrix[i][j] * other.component(lowerCAmelCase__				)
																										    for j in range(self.__width				)
																										]
																										ans.change_component(lowerCAmelCase__						,       sum(lowerCAmelCase__				)				)
																				return ans
														else:
																				raise Exception(
																				    'vector must have the same size as the '
																				    'number of columns of the matrix!'				)
								elif isinstance(lowerCAmelCase__						,       (int, float)				):  # matrix-scalar
														SCREAMING_SNAKE_CASE													=					[
														    [self.__matrix[i][j] * other for j in range(self.__width				)]
														    for i in range(self.__height				)
														]
														return Matrix(lowerCAmelCase__						,       self.__width						,       self.__height				)
								return None







		def 							__A						(	self				)     ->							int:
								return self.__height







		def 							__A						(	self				)     ->							int:
								return self.__width







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if 0 <= x < self.__height and 0 <= y < self.__width:
														return self.__matrix[x][y]
								else:
														raise Exception('change_component: indices out of bounds'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							None:
								if 0 <= x < self.__height and 0 <= y < self.__width:
														SCREAMING_SNAKE_CASE													=					value
								else:
														raise Exception('change_component: indices out of bounds'				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								SCREAMING_SNAKE_CASE													=					self.__matrix[:x] + self.__matrix[x + 1 :]
								for i in range(len(lowerCAmelCase__				)				):
														SCREAMING_SNAKE_CASE													=					minor[i][:y] + minor[i][y + 1 :]
								return Matrix(lowerCAmelCase__						,       self.__width - 1						,       self.__height - 1				).determinant()







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								if 0 <= x < self.__height and 0 <= y < self.__width:
														return (-1) ** (x + y) * self.minor(lowerCAmelCase__						,       lowerCAmelCase__				)
								else:
														raise Exception('Indices out of bounds'				)







		def 							__A						(	self				)     ->							float:
								if self.__height != self.__width:
														raise Exception('Matrix is not square'				)
								if self.__height < 1:
														raise Exception('Matrix has no element'				)
								elif self.__height == 1:
														return self.__matrix[0][0]
								elif self.__height == 2:
														return (
														    self.__matrix[0][0] * self.__matrix[1][1]
														    - self.__matrix[0][1] * self.__matrix[1][0]
														)
								else:
														SCREAMING_SNAKE_CASE													=					[
														    self.__matrix[0][y] * self.cofactor(0						,       lowerCAmelCase__				) for y in range(self.__width				)
														]
														return sum(lowerCAmelCase__				)





def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						SCREAMING_SNAKE_CASE													=					[[0] * n for _ in range(SCREAMING_SNAKE_CASE_  )]
						return Matrix(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						Matrix:
						random.seed(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					[
						    [random.randint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for _ in range(SCREAMING_SNAKE_CASE_  )] for _ in range(SCREAMING_SNAKE_CASE_  )
						]
						return Matrix(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


