


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)


class 	_a							(      unittest.TestCase      ):

							def __init__(							self:     Optional[int]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Any=13   ,						UpperCamelCase_:     Optional[int]=7   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     str=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     List[Any]=True   ,						UpperCamelCase_:     str=99   ,						UpperCamelCase_:     Any=32   ,						UpperCamelCase_:     Union[str, Any]=5   ,						UpperCamelCase_:     List[Any]=4   ,						UpperCamelCase_:     List[str]=37   ,						UpperCamelCase_:     List[Any]="gelu"   ,						UpperCamelCase_:     Union[str, Any]=0.1   ,						UpperCamelCase_:     Any=0.1   ,						UpperCamelCase_:     Union[str, Any]=512   ,						UpperCamelCase_:     int=16   ,						UpperCamelCase_:     str=2   ,						UpperCamelCase_:     Tuple=0.02   ,						UpperCamelCase_:     Dict=4   ,						)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				seq_length
									lowercase__						=				is_training
									lowercase__						=				use_attention_mask
									lowercase__						=				use_token_type_ids
									lowercase__						=				use_labels
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_act
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				type_sequence_label_size
									lowercase__						=				initializer_range
									lowercase__						=				num_choices

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

									lowercase__						=				None
									if self.use_attention_mask:
											lowercase__						=				random_attention_mask([self.batch_size, self.seq_length]							)

									lowercase__						=				None
									if self.use_token_type_ids:
											lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size							)

									lowercase__						=				RoFormerConfig(
									    vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						is_decoder=lowercase_   ,						initializer_range=self.initializer_range   ,						)

									return config, input_ids, token_type_ids, attention_mask


							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.prepare_config_and_inputs()
									lowercase__					,		lowercase__					,		lowercase__					,		lowercase__						=				config_and_inputs
									lowercase__						=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
									return config, inputs_dict


@require_flax
class 	_a							(      UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Tuple  				=			True

							_lowercase     :      Optional[int]  				=			(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				FlaxRoFormerModelTester(self							)


							@slow
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       str:





									"""simple docstring"""







									for model_class_name in self.all_model_classes:
											lowercase__						=				model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''   ,						from_pt=lowercase_							)
											lowercase__						=				model(np.ones((1, 1)							)							)
											self.assertIsNotNone(lowercase_							)


@require_flax
class 	_a							(      unittest.TestCase      ):

							@slow
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
									lowercase__						=				jnp.array([[0, 1, 2, 3, 4, 5]]							)
									lowercase__						=				model(lowercase_							)[0]

									lowercase__						=				50_000

									lowercase__						=				(1, 6, vocab_size)
									self.assertEqual(output.shape   ,						lowercase_							)

									lowercase__						=				jnp.array(
									    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]							)

									self.assertTrue(jnp.allclose(output[:, :3, :3]   ,						lowercase_   ,						atol=1E-4							)							)





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCAmelCase	=							'\\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	=							'\\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	=							'\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 	_a							(      datasets.Metric      ):

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Union[str, Any]:





									"""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 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     List[str]							)      ->       Union[str, Any]:





									"""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 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict=0.9   ,						UpperCamelCase_:     Union[str, Any]=3   ,						UpperCamelCase_:     Optional[int]=0.5							)      ->       Dict:





									"""simple docstring"""







									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_							)}


