





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       def filter_log(UpperCamelCase_	):
              return False if "This IS expected if you are initializing" in record.msg else True

       UpperCamelCase_ 							=	datasets.utils.logging.get_logger("transformers.modeling_utils"	)
       logger.addFilter(UpperCamelCase_	)
       try:
              yield
       finally:
              logger.removeFilter(UpperCamelCase_	)


_UpperCAmelCase										=					'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

_UpperCAmelCase										=					'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

_UpperCAmelCase										=					'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,    _KWARGS_DESCRIPTION  )
class 				_UpperCamelCase       (						datasets.Metric  ):

       def lowercase			(		self:			Union[str, Any]		)							->			Dict:




              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION       , citation=_CITATION       , homepage="https://github.com/Tiiiger/bert_score"       , 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/Tiiiger/bert_score"]       , reference_urls=[
                      "https://github.com/Tiiiger/bert_score",
                      "https://arxiv.org/abs/1904.09675",
                  ]       , )

       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			List[Any]=None       , _SCREAMING_SNAKE_CASE:			str=False       , _SCREAMING_SNAKE_CASE:			int=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Tuple=4       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , _SCREAMING_SNAKE_CASE:			List[str]=False       , )							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	bert_score.utils.get_hash
              UpperCamelCase_ 							=	bert_score.BERTScorer

              if version.parse(bert_score.__version__		) >= version.parse("0.3.10"		):
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
              elif use_fast_tokenizer:
                     raise ImportWarning(
                         "To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"
                         "You can install it with `pip install \"bert-score>=0.3.10\"`."		)

              if model_type is None:
                     assert lang is not None, "either lang or model_type should be specified"
                     UpperCamelCase_ 							=	bert_score.utils.langamodel[lang.lower()]

              if num_layers is None:
                     UpperCamelCase_ 							=	bert_score.utils.modelalayers[model_type]

              UpperCamelCase_ 							=	get_hash(
                  model=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , use_custom_baseline=baseline_path is not None       , )

              with filter_logging_context():
                     if not hasattr(self       , "cached_bertscorer"		) or self.cached_bertscorer.hash != hashcode:
                            UpperCamelCase_ 							=	scorer(
                                model_type=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , nthreads=_SCREAMING_SNAKE_CASE       , all_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE       , lang=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , baseline_path=_SCREAMING_SNAKE_CASE       , )

              ((UpperCamelCase_)							,			(UpperCamelCase_)							,			(UpperCamelCase_)) 							=	self.cached_bertscorer.score(
                  cands=_SCREAMING_SNAKE_CASE       , refs=_SCREAMING_SNAKE_CASE       , verbose=_SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , )
              UpperCamelCase_ 							=	{
                  "precision": P.tolist(),
                  "recall": R.tolist(),
                  "f1": F.tolist(),
                  "hashcode": hashcode,
              }
              return output_dict

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			int=None       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Any:




              """simple docstring"""


              if references is not None:
                     UpperCamelCase_ 							=	[[ref] if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		) else ref for ref in references]
              super().add_batch(predictions=_SCREAMING_SNAKE_CASE       , references=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , **_SCREAMING_SNAKE_CASE:			int		)							->			Any:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	[reference]
              super().add(prediction=_SCREAMING_SNAKE_CASE       , reference=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   UpperCamelCase_					:    int			      = EsmTokenizer
   def _lowercase(     self     )						->Dict:
        super().setUp()
        a	:     Tuple						      =    tempfile.mkdtemp()
        # fmt: off
        a	:     List[str]						      =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
        # fmt: on
        a	:     Tuple						      =    os.path.join(self.tmpdirname				,  VOCAB_FILES_NAMES['''vocab_file''']     )
        with open(self.vocab_file				,  '''w'''				,  encoding='''utf-8'''     ) as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )
   def _lowercase(     self				,  **lowerCAmelCase_     )						->List[PreTrainedTokenizerBase]:
        return [self.get_tokenizer(**lowerCAmelCase_     )]
   def _lowercase(     self				,  **lowerCAmelCase_     )						->PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname				,  **lowerCAmelCase_     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     List[Any]						      =    tokenizer.tokenize('''LAGVS'''     )
        self.assertListEqual(lowerCAmelCase_				,  ['''L''', '''A''', '''G''', '''V''', '''S''']     )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )				,  [4, 5, 6, 7, 8]     )
   def _lowercase(     self     )						->Optional[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[Any]						      =    '''LAGVS'''
        self.assertListEqual(tokenizer.encode(lowerCAmelCase_     )				,  [0, 4, 5, 6, 7, 8, 2]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Any						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[int]						      =    ['''LAGVS''', '''WCB''']
        a	:     Optional[Any]						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[Any]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     str						      =    ['''LAGVS''', '''WCB''']
        a	:     Dict						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]     )


   def _lowercase(     self     )						->List[str]:
        a	:     int						      =    self.get_tokenizers(fast=lowerCAmelCase_     )
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
                  a	:     Union[str, Any]						      =    '''<unk>'''
                  a	:     List[Any]						      =    '''<mask>'''

                  a	:     List[Any]						      =    tokenizer.tokenize(lowerCAmelCase_     )
                  a	:     Tuple						      =    tokenizer.tokenize(lowerCAmelCase_     )

                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )