





'''simple docstring'''

from random import shuffle

import tensorflow as tf
from numpy import array







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = int(lowerCAmelCase_							)
				assert noofclusters < len(lowerCAmelCase_							)

				# Find out the dimensionality
				lowercase         = len(vectors[0]							)

				# Will help select random centroids from among the available vectors
				lowercase         = list(range(len(lowerCAmelCase_							)							)							)
				shuffle(lowerCAmelCase_							)

				# GRAPH OF COMPUTATION
				# We initialize a new graph and set it as the default during each run
				# of this algorithm. This ensures that as this function is called
				# multiple times, the default graph doesn't keep getting crowded with
				# unused ops and Variables from previous function calls.

				lowercase         = tf.Graph()

				with graph.as_default():
								# SESSION OF COMPUTATION

								lowercase         = tf.Session()

								##CONSTRUCTING THE ELEMENTS OF COMPUTATION

								##First lets ensure we have a Variable vector for each centroid,
								##initialized to one of the vectors from the available data points
								lowercase         = [
								    tf.Variable(vectors[vector_indices[i]]							) for i in range(lowerCAmelCase_							)
								]
								##These nodes will assign the centroid Variables the appropriate
								##values
								lowercase         = tf.placeholder("float64"		,							[dim]							)
								lowercase         = []
								for centroid in centroids:
												cent_assigns.append(tf.assign(lowerCAmelCase_		,							lowerCAmelCase_							)							)

								##Variables for cluster assignments of individual vectors(initialized
								##to 0 at first)
								lowercase         = [tf.Variable(0							) for i in range(len(lowerCAmelCase_							)							)]
								##These nodes will assign an assignment Variable the appropriate
								##value
								lowercase         = tf.placeholder("int32"							)
								lowercase         = []
								for assignment in assignments:
												cluster_assigns.append(tf.assign(lowerCAmelCase_		,							lowerCAmelCase_							)							)

								##Now lets construct the node that will compute the mean
								# The placeholder for the input
								lowercase         = tf.placeholder("float"		,							[None, dim]							)
								# The Node/op takes the input and computes a mean along the 0th
								# dimension, i.e. the list of input vectors
								lowercase         = tf.reduce_mean(lowerCAmelCase_		,							0							)

								##Node for computing Euclidean distances
								# Placeholders for input
								lowercase         = tf.placeholder("float"		,							[dim]							)
								lowercase         = tf.placeholder("float"		,							[dim]							)
								lowercase         = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowerCAmelCase_		,							lowerCAmelCase_							)		,							2							)							)							)

								##This node will figure out which cluster to assign a vector to,
								##based on Euclidean distances of the vector from the centroids.
								# Placeholder for input
								lowercase         = tf.placeholder("float"		,							[noofclusters]							)
								lowercase         = tf.argmin(lowerCAmelCase_		,							0							)

								##INITIALIZING STATE VARIABLES

								##This will help initialization of all Variables defined with respect
								##to the graph. The Variable-initializer should be defined after
								##all the Variables have been constructed, so that each of them
								##will be included in the initialization.
								lowercase         = tf.initialize_all_variables()

								# Initialize all variables
								sess.run(lowerCAmelCase_							)

								##CLUSTERING ITERATIONS

								# Now perform the Expectation-Maximization steps of K-Means clustering
								# iterations. To keep things simple, we will only do a set number of
								# iterations, instead of using a Stopping Criterion.
								lowercase         = 100
								for _ in range(lowerCAmelCase_							):
												##EXPECTATION STEP
												##Based on the centroid locations till last iteration, compute
												##the _expected_ centroid assignments.
												# Iterate over each vector
												for vector_n in range(len(lowerCAmelCase_							)							):
																lowercase         = vectors[vector_n]
																# Compute Euclidean distance between this vector and each
																# centroid. Remember that this list cannot be named
																#'centroid_distances', since that is the input to the
																# cluster assignment node.
																lowercase         = [
																    sess.run(lowerCAmelCase_		,							feed_dict={va: vect, va: sess.run(lowerCAmelCase_							)}							)
																    for centroid in centroids
																]
																# Now use the cluster assignment node, with the distances
																# as the input
																lowercase         = sess.run(
																    lowerCAmelCase_		,							feed_dict={centroid_distances: distances}							)
																# Now assign the value to the appropriate state variable
																sess.run(
																    cluster_assigns[vector_n]		,							feed_dict={assignment_value: assignment}							)

												##MAXIMIZATION STEP
												# Based on the expected state computed from the Expectation Step,
												# compute the locations of the centroids so as to maximize the
												# overall objective of minimizing within-cluster Sum-of-Squares
												for cluster_n in range(lowerCAmelCase_							):
																# Collect all the vectors assigned to this cluster
																lowercase         = [
																    vectors[i]
																    for i in range(len(lowerCAmelCase_							)							)
																    if sess.run(assignments[i]							) == cluster_n
																]
																# Compute new centroid location
																lowercase         = sess.run(
																    lowerCAmelCase_		,							feed_dict={mean_input: array(lowerCAmelCase_							)}							)
																# Assign value to appropriate variable
																sess.run(
																    cent_assigns[cluster_n]		,							feed_dict={centroid_value: new_location}							)

        # Return centroids and assignments
								lowercase         = sess.run(lowerCAmelCase_							)
								lowercase         = sess.run(lowerCAmelCase_							)
								return centroids, assignments











'''simple docstring'''

import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase : Any											=			"\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\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"

__lowerCamelCase : Dict											=			"\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\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#chrf--chrf for more information.\n"

__lowerCamelCase : List[Any]											=			"\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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




		def 				UpperCAmelCase__   (self      :	List[str]				)					-> 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="https://github.com/mjpost/sacreBLEU#chrf--chrf"							,			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#chrf--chrf"]							,			reference_urls=[
						        "https://github.com/m-popovic/chrF",
						    ]							,			)





		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	Dict							,			A__      :	Tuple							,			A__      :	int = CHRF.CHAR_ORDER							,			A__      :	int = CHRF.WORD_ORDER							,			A__      :	int = CHRF.BETA							,			A__      :	bool = False							,			A__      :	bool = False							,			A__      :	bool = False							,			)					-> List[str]:
						lowercase         = len(references[0]				)
						if any(len(A__				) != references_per_prediction for refs in references				):
										raise ValueError("Sacrebleu requires the same number of references for each prediction"				)
						lowercase         = [[refs[i] for refs in references] for i in range(A__				)]

						lowercase         = CHRF(A__							,			A__							,			A__							,			A__							,			A__							,			A__				)
						lowercase         = sb_chrf.corpus_score(A__							,			A__				)

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}




