





'''simple docstring'''




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowercase__   :				Optional[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__   :				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__   :				Dict      =   "\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							):


					def 							SCREAMING_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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

										'''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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase=0.9						,     _UpperCAmelCase=3						,     _UpperCAmelCase=0.5):

										'''simple docstring'''






										if NLTK_VERSION >= version.Version('3.6.5'):
															__A						:			List[Any]								=	[
															    meteor_score.single_meteor_score(
															        word_tokenize(_snake_case)						,     word_tokenize(_snake_case)						,     alpha=_snake_case						,     beta=_snake_case						,     gamma=_snake_case)
															    for ref, pred in zip(_snake_case						,     _snake_case)
															]
										else:
															__A						:			Optional[Any]								=	[
															    meteor_score.single_meteor_score(_snake_case						,     _snake_case						,     alpha=_snake_case						,     beta=_snake_case						,     gamma=_snake_case)
															    for ref, pred in zip(_snake_case						,     _snake_case)
															]

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






'''simple docstring'''




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class 			SCREAMING_SNAKE_CASE   (a__							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	SMALL_MODEL_IDENTIFIER
          __A						:			Any								=	'pt'
          __A						:			str								=	'tf'


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	AutoModel.from_pretrained(self.test_model)
          model_pt.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	TFAutoModel.from_pretrained(self.test_model						,     from_pt=_UpperCAmelCase)
          model_tf.save_pretrained(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	'mock_framework'

          # Framework provided - return whatever the user provides
          __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model						,     _UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # Local checkpoint and framework provided - return provided framework
          # PyTorch checkpoint
          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_UpperCAmelCase)
               __A						:			Optional[int]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # TensorFlow checkpoint
          with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_UpperCAmelCase)
               __A						:			List[str]								=	FeaturesManager.determine_framework(_UpperCAmelCase)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Invalid local checkpoint
          with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			Tuple								=	FeaturesManager.determine_framework(_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase):
               __A						:			Union[str, Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # PyTorch not in environment -> use TensorFlow
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			List[Any]								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_tf)

          # Both in environment -> use PyTorch
          __A						:			Any								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Dict								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               __A						:			int								=	FeaturesManager.determine_framework(self.test_model)
               self.assertEqual(_UpperCAmelCase						,     self.framework_pt)

          # Both not in environment -> raise error
          __A						:			List[str]								=	MagicMock(return_value=_UpperCAmelCase)
          __A						:			Tuple								=	MagicMock(return_value=_UpperCAmelCase)
          with patch('transformers.onnx.features.is_tf_available'						,     _UpperCAmelCase), patch(
              'transformers.onnx.features.is_torch_available'						,     _UpperCAmelCase):
               with self.assertRaises(_UpperCAmelCase):
                    __A						:			int								=	FeaturesManager.determine_framework(self.test_model)