





"""simple docstring"""






import os

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

import datasets


lowerCAmelCase_			=							datasets.logging.get_logger(__name__)


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

lowerCAmelCase_			=							'''\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

lowerCAmelCase_			=							'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

lowerCAmelCase_			=							{
    '''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 	lowerCAmelCase					(      datasets.Metric							):






       def 							__A (   self     ):
              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    ,  a__     ):
              # check that config name specifies a valid BLEURT model
              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(a__    ,  a__     )     )






       def 							__A (   self    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     self.scorer.score(references=a__    ,  candidates=a__     )
              return {"scores": scores}













"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
    import optax
    from flax.training.common_utils import onehot

    from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
    from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 	lowerCAmelCase					(      unittest.TestCase							):






       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'     )
              _UpperCAmelCase     =     AutoTokenizer.from_pretrained('google/mt5-small'     )

              _UpperCAmelCase     =     tokenizer('Hello there'    ,  return_tensors='np'     ).input_ids
              _UpperCAmelCase     =     tokenizer('Hi I am'    ,  return_tensors='np'     ).input_ids

              _UpperCAmelCase     =     shift_tokens_right(a__    ,  model.config.pad_token_id    ,  model.config.decoder_start_token_id     )

              _UpperCAmelCase     =     model(a__    ,  decoder_input_ids=a__     ).logits
              _UpperCAmelCase     =     optax.softmax_cross_entropy(a__    ,  onehot(a__    ,  logits.shape[-1]     )     ).mean()

              _UpperCAmelCase     =     -(labels.shape[-1] * loss.item())

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






