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	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""TODO: Add a description here."""

	import evaluate
	import datasets


	# TODO: Add BibTeX citation
	_CITATION = """\
	@InProceedings{huggingface:metric,
	title = {A great new metric},
	authors={huggingface, Inc.},
	year={2020}
	}
	"""

	# TODO: Add description of the metric here
	_DESCRIPTION = """\
	This new metric is designed to solve this great NLP task and is crafted with a lot of care.
	"""


	# TODO: Add description of the arguments of the metric here
	_KWARGS_DESCRIPTION = """
	Calculates how good are predictions given some references, using certain scores
	Args:
	predictions: list of predictions to score. Each predictions
	should be a string with tokens separated by spaces.
	references: list of reference for each prediction. Each
	reference should be a string with tokens separated by spaces.
	Returns:
	accuracy: description of the first score,
	another_score: description of the second score,
	Examples:
	Examples should be written in doctest format, and should illustrate how
	to use the function.

	>>> my_new_metric = evaluate.load_metric("my_new_metric")
	>>> results = my_new_metric.compute(references=[0, 1], predictions=[0, 1])
	>>> print(results)
	{'accuracy': 1.0}
	"""

	# TODO: Define external resources urls if needed
	BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"


	@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
	class test(evaluate.EvaluationModule):
	"""TODO: Short description of my metric."""

	def _info(self):
	# TODO: Specifies the evaluate.MetricInfo object
	return evaluate.EvaluationModuleInfo(
	# This is the description that will appear on the metrics page.
	module_type="metric",
	description=_DESCRIPTION,
	citation=_CITATION,
	inputs_description=_KWARGS_DESCRIPTION,
	# This defines the format of each prediction and reference
	features=datasets.Features({
	'predictions': datasets.Value('int64'),
	'references': datasets.Value('int64'),
	}),
	# Homepage of the metric for documentation
	homepage="http://metric.homepage",
	# Additional links to the codebase or references
	codebase_urls=["http://github.com/path/to/codebase/of/new_metric"],
	reference_urls=["http://path.to.reference.url/new_metric"]
	)

	def _download_and_prepare(self, dl_manager):
	"""Optional: download external resources useful to compute the scores"""
	# TODO: Download external resources if needed
	pass

	def _compute(self, predictions, references):
	"""Returns the scores"""
	# TODO: Compute the different scores of the metric
	accuracy = sum(i == j for i, j in zip(predictions, references)) / len(predictions)
	return {
	"accuracy": accuracy,
	}