
'''simple docstring'''







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_lowercase :  List[Any]    							=       "\\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"

_lowercase :  Tuple    							=       "\\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"

_lowercase :  Tuple    							=       "\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  lowerCAmelCase__   (  datasets.Metric     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=0.9				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=0.5		):


											"""simple docstring"""





											if NLTK_VERSION >= version.Version('''3.6.5'''		):
																	lowercase_	:     Dict													=       [
																	    meteor_score.single_meteor_score(
																	        word_tokenize(__SCREAMING_SNAKE_CASE		)				,							word_tokenize(__SCREAMING_SNAKE_CASE		)				,							alpha=__SCREAMING_SNAKE_CASE				,							beta=__SCREAMING_SNAKE_CASE				,							gamma=__SCREAMING_SNAKE_CASE		)
																	    for ref, pred in zip(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	]
											else:
																	lowercase_	:     Union[str, Any]													=       [
																	    meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							alpha=__SCREAMING_SNAKE_CASE				,							beta=__SCREAMING_SNAKE_CASE				,							gamma=__SCREAMING_SNAKE_CASE		)
																	    for ref, pred in zip(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	]

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





'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



