






import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


__lowerCamelCase :      List[Any]										=				version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
	from nltk import word_tokenize


__lowerCamelCase :      Dict										=				"""\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n"""

__lowerCamelCase :      str										=				"""\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"""

__lowerCamelCase :      List[str]										=				"""\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n    >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results[\"meteor\"], 4))\n    0.6944\n"""



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





		"""simple docstring"""






		def 	_lowercase					( self					:  Optional[Any]	):
			return datasets.MetricInfo(
			    description=_DESCRIPTION							,		citation=_CITATION							,		inputs_description=_KWARGS_DESCRIPTION							,		features=datasets.Features(
			        {
			            "predictions": datasets.Value("string"							,		id="sequence"	),
			            "references": datasets.Value("string"							,		id="sequence"	),
			        }	)							,		codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]							,		reference_urls=[
			        "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
			        "https://en.wikipedia.org/wiki/METEOR",
			    ]							,		)



		def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  List[str]	):
			import nltk

			nltk.download("wordnet"	)
			if NLTK_VERSION >= version.Version("3.6.5"	):
				nltk.download("punkt"	)
			if NLTK_VERSION >= version.Version("3.6.6"	):
				nltk.download("omw-1.4"	)






		def 	_lowercase					( self					:  Any							,		__A					:  Union[str, Any]							,		__A					:  Tuple							,		__A					:  Dict=0.9							,		__A					:  Union[str, Any]=3							,		__A					:  Optional[int]=0.5	):







			if NLTK_VERSION >= version.Version("3.6.5"	):
				snake_case__			:				Dict       =  [
				    meteor_score.single_meteor_score(
				        word_tokenize(UpperCamelCase_	)							,		word_tokenize(UpperCamelCase_	)							,		alpha=UpperCamelCase_							,		beta=UpperCamelCase_							,		gamma=UpperCamelCase_	)
				    for ref, pred in zip(UpperCamelCase_							,		UpperCamelCase_	)
				]
			else:
				snake_case__			:				Tuple       =  [
				    meteor_score.single_meteor_score(UpperCamelCase_							,		UpperCamelCase_							,		alpha=UpperCamelCase_							,		beta=UpperCamelCase_							,		gamma=UpperCamelCase_	)
				    for ref, pred in zip(UpperCamelCase_							,		UpperCamelCase_	)
				]

			return {"meteor": np.mean(UpperCamelCase_	)}











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



