




'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


A									=datasets.logging.get_logger(__name__)


A									='\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

A									='\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A									='\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A									={
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION				)
class     _a						(			datasets.Metric				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION     ,       citation=_CITATION     ,       homepage='''https://github.com/google-research/bleurt'''     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                       '''references''': datasets.Value('''string'''     ,       id='''sequence'''       ),
                   }       )     ,       codebase_urls=['''https://github.com/google-research/bleurt''']     ,       reference_urls=['''https://github.com/google-research/bleurt''', '''https://arxiv.org/abs/2004.04696''']     ,       )
       def 					A   (			self		:			List[Any]     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           if self.config_name == "default":
               logger.warning(
                   '''Using default BLEURT-Base checkpoint for sequence maximum length 128. '''
                   '''You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'''       )
               UpperCAmelCase     =	'''bleurt-base-128'''

           if self.config_name.lower() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.lower()

           elif self.config_name.upper() in CHECKPOINT_URLS:
               UpperCAmelCase     =	self.config_name.upper()

           else:
               raise KeyError(
                   f"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"       )

           # download the model checkpoint specified by self.config_name and set up the scorer
           UpperCAmelCase     =	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
           UpperCAmelCase     =	score.BleurtScorer(os.path.join(lowercase     ,       lowercase       )       )







       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.scorer.score(references=lowercase     ,       candidates=lowercase       )
           return {"scores": scores}










'''simple docstring'''







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )


class     _a						(			unittest.TestCase				):
       def __init__(			self		:			List[Any]     ,       lowercase		:			Dict     ,       lowercase		:			List[str]=13     ,       lowercase		:			str=7     ,       lowercase		:			List[str]=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Any=99     ,       lowercase		:			Any=32     ,       lowercase		:			Any=5     ,       lowercase		:			Tuple=4     ,       lowercase		:			List[Any]=37     ,       lowercase		:			List[Any]="gelu"     ,       lowercase		:			int=0.1     ,       lowercase		:			Any=0.1     ,       lowercase		:			Optional[int]=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			int=4     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_attention_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_choices
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

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

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

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

           UpperCAmelCase     =	RobertaPreLayerNormConfig(
               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 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

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







       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs

           UpperCAmelCase     =	True
           UpperCAmelCase     =	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       vocab_size=2       )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Any            =      True

       __a       :  str            =      (
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModelTester(self       )







       @slow
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase     =	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )
               UpperCAmelCase     =	model(np.ones((1, 1)       )       )
               self.assertIsNotNone(lowercase       )






@require_flax
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           UpperCAmelCase     =	[1, 11, 50_265]
           self.assertEqual(list(output.shape       )     ,       lowercase       )
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )







       @slow
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''     ,       from_pt=lowercase       )

           UpperCAmelCase     =	np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]     ,       dtype=jnp.intaa       )
           UpperCAmelCase     =	model(lowercase       )[0]
           # compare the actual values for a slice.
           UpperCAmelCase     =	np.array(
               [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,       dtype=np.floataa       )
           self.assertTrue(np.allclose(output[:, :3, :3]     ,       lowercase     ,       atol=1E-4       )       )




