
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase__		:Dict			  =   version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
       from nltk import word_tokenize


lowercase__		: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"

lowercase__		:Optional[Any]			  =   "\\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__		:List[Any]			  =   "\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     lowercase		( datasets.Metric		):




       def 			A__				(    self):
            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 			A__				(    self       ,A__):
            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 			A__				(    self       ,A__       ,A__       ,A__=0.9       ,A__=3       ,A__=0.5):







            if NLTK_VERSION >= version.Version('''3.6.5'''):
                 lowercase						     =						[
                     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:
                 lowercase						     =						[
                     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_)}






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




