



import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__UpperCAmelCase	       =	'''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

__UpperCAmelCase	       =	'''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

__UpperCAmelCase	       =	'''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,   _KWARGS_DESCRIPTION							)
class     lowerCAmelCase_		(  datasets.Metric							):
       def  snake_case_	(      self	)					->		Dict:
          if version.parse(scb.__version__	) < version.parse('1.4.12'	):
             raise ImportWarning(
                 'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
                 'You can install it with `pip install "sacrebleu>=1.4.12"`.'	)
          return datasets.MetricInfo(
              description=_DESCRIPTION,      citation=_CITATION,      homepage='https://github.com/mjpost/sacreBLEU#chrf--chrf',      inputs_description=_KWARGS_DESCRIPTION,      features=datasets.Features(
                  {
                      'predictions': datasets.Value('string',      id='sequence'	),
                      'references': datasets.Sequence(datasets.Value('string',      id='sequence'	),      id='references'	),
                  }	),      codebase_urls=['https://github.com/mjpost/sacreBLEU#chrf--chrf'],      reference_urls=[
                  'https://github.com/m-popovic/chrF',
              ],      )






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = CHRF.CHAR_ORDER,      SCREAMING_SNAKE_CASE_ = CHRF.WORD_ORDER,      SCREAMING_SNAKE_CASE_ = CHRF.BETA,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = False,      )					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     len(references[0]	)
          if any(len(SCREAMING_SNAKE_CASE_	) != references_per_prediction for refs in references	):
             raise ValueError('Sacrebleu requires the same number of references for each prediction'	)
          UpperCamelCase   :  Any						     =     [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_	)]

          UpperCamelCase   :  Union[str, Any]						     =     CHRF(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     sb_chrf.corpus_score(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          return {
              "score": output.score,
              "char_order": output.char_order,
              "word_order": output.word_order,
              "beta": output.beta,
          }








import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers.models.esm.modeling_esmfold import EsmForProteinFolding



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=19,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=None,      )					->		Optional[int]:
          UpperCamelCase   :  Any						     =     parent
          UpperCamelCase   :  Optional[Any]						     =     batch_size
          UpperCamelCase   :  Optional[Any]						     =     seq_length
          UpperCamelCase   :  Tuple						     =     is_training
          UpperCamelCase   :  List[str]						     =     use_input_mask
          UpperCamelCase   :  Optional[int]						     =     use_token_type_ids
          UpperCamelCase   :  Any						     =     use_labels
          UpperCamelCase   :  Union[str, Any]						     =     vocab_size
          UpperCamelCase   :  int						     =     hidden_size
          UpperCamelCase   :  List[Any]						     =     num_hidden_layers
          UpperCamelCase   :  Dict						     =     num_attention_heads
          UpperCamelCase   :  List[Any]						     =     intermediate_size
          UpperCamelCase   :  str						     =     hidden_act
          UpperCamelCase   :  List[Any]						     =     hidden_dropout_prob
          UpperCamelCase   :  List[str]						     =     attention_probs_dropout_prob
          UpperCamelCase   :  List[str]						     =     max_position_embeddings
          UpperCamelCase   :  Union[str, Any]						     =     type_vocab_size
          UpperCamelCase   :  str						     =     type_sequence_label_size
          UpperCamelCase   :  List[str]						     =     initializer_range
          UpperCamelCase   :  List[Any]						     =     num_labels
          UpperCamelCase   :  Any						     =     num_choices
          UpperCamelCase   :  Tuple						     =     scope
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  str						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

          UpperCamelCase   :  Optional[Any]						     =     None
          if self.use_input_mask:
             UpperCamelCase   :  Dict						     =     random_attention_mask([self.batch_size, self.seq_length]	)

          UpperCamelCase   :  Union[str, Any]						     =     None
          UpperCamelCase   :  Optional[int]						     =     None
          UpperCamelCase   :  Optional[int]						     =     None
          if self.use_labels:
             UpperCamelCase   :  int						     =     ids_tensor([self.batch_size],      self.type_sequence_label_size	)
             UpperCamelCase   :  Optional[Any]						     =     ids_tensor([self.batch_size, self.seq_length],      self.num_labels	)
             UpperCamelCase   :  Tuple						     =     ids_tensor([self.batch_size],      self.num_choices	)

          UpperCamelCase   :  Union[str, Any]						     =     self.get_config()

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  List[str]						     =     EsmConfig(
              vocab_size=33,      hidden_size=self.hidden_size,      pad_token_id=1,      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,      initializer_range=self.initializer_range,      is_folding_model=SCREAMING_SNAKE_CASE_,      esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False},      )
          return config
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Union[str, Any]						     =     EsmForProteinFolding(config=SCREAMING_SNAKE_CASE_	).float()
          model.to(SCREAMING_SNAKE_CASE_	)
          model.eval()
          UpperCamelCase   :  int						     =     model(SCREAMING_SNAKE_CASE_,      attention_mask=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     model(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     model(SCREAMING_SNAKE_CASE_	)

          self.parent.assertEqual(result.positions.shape,      (8, self.batch_size, self.seq_length, 14, 3)	)
          self.parent.assertEqual(result.angles.shape,      (8, self.batch_size, self.seq_length, 7, 2)	)






       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Union[str, Any]						     =     self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase
          )     ,					(
              UpperCamelCase
          )     ,					(
              UpperCamelCase
          )     ,					(
              UpperCamelCase
          )     ,					(
              UpperCamelCase
          )     ,					(
              UpperCamelCase
          )     ,					
          )   :  Tuple						     =     config_and_inputs
          UpperCamelCase   :  Optional[Any]						     =     {'input_ids': input_ids, 'attention_mask': input_mask}
          return config, inputs_dict






@require_torch
class     lowerCAmelCase_		(  a__	,   a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  False

       UpperCAmelCase__    : Optional[int]       					=  (EsmForProteinFolding,) if is_torch_available() else ()
       UpperCAmelCase__    : int       					=  ()
       UpperCAmelCase__    : List[str]       					=  {} if is_torch_available() else {}
       UpperCAmelCase__    : Optional[int]       					=  False
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Tuple						     =     EsmFoldModelTester(self	)
          UpperCamelCase   :  List[str]						     =     ConfigTester(self,      config_class=SCREAMING_SNAKE_CASE_,      hidden_size=37	)
       def  snake_case_	(      self	)					->		int:
          self.config_tester.run_common_tests()
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)
       @unittest.skip('Does not support attention outputs'	)
       def  snake_case_	(      self	)					->		Tuple:
          pass
       @unittest.skip
       def  snake_case_	(      self	)					->		List[Any]:
          pass
       @unittest.skip('Esm does not support embedding resizing'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('Esm does not support embedding resizing'	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          pass
       @unittest.skip('ESMFold does not support passing input embeds!'	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          pass
       @unittest.skip('ESMFold does not support head pruning.'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('ESMFold does not support head pruning.'	)
       def  snake_case_	(      self	)					->		int:
          pass
       @unittest.skip('ESMFold does not support head pruning.'	)
       def  snake_case_	(      self	)					->		Dict:
          pass
       @unittest.skip('ESMFold does not support head pruning.'	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          pass
       @unittest.skip('ESMFold does not support head pruning.'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('ESMFold does not output hidden states in the normal way.'	)
       def  snake_case_	(      self	)					->		str:
          pass
       @unittest.skip('ESMfold does not output hidden states in the normal way.'	)
       def  snake_case_	(      self	)					->		List[str]:
          pass
       @unittest.skip('ESMFold only has one output format.'	)
       def  snake_case_	(      self	)					->		int:
          pass
       @unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('ESMFold does not support input chunking.'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'	)
       def  snake_case_	(      self	)					->		Tuple:
          pass
       @unittest.skip('ESMFold doesn\'t support torchscript compilation.'	)
       def  snake_case_	(      self	)					->		Any:
          pass
       @unittest.skip('ESMFold doesn\'t support torchscript compilation.'	)
       def  snake_case_	(      self	)					->		str:
          pass
       @unittest.skip('ESMFold doesn\'t support torchscript compilation.'	)
       def  snake_case_	(      self	)					->		List[Any]:
          pass
       @unittest.skip('ESMFold doesn\'t support data parallel.'	)
       def  snake_case_	(      self	)					->		List[str]:
          pass






       @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          pass






@require_torch
class     lowerCAmelCase_		(  a__							):
       @slow
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Union[str, Any]						     =     EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'	).float()
          model.eval()
          UpperCamelCase   :  Tuple						     =     torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
          UpperCamelCase   :  List[str]						     =     model(SCREAMING_SNAKE_CASE_	)['positions']
          UpperCamelCase   :  int						     =     torch.tensor([2.58_28, 0.79_93, -10.93_34],      dtype=torch.floataa	)
          self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0],      SCREAMING_SNAKE_CASE_,      atol=1e-4	)	)



