






import os

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

import datasets


_a    :   Tuple        =			datasets.logging.get_logger(__name__)


_a    :   Union[str, Any]        =			"""\
@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}
}
"""

_a    :   int        =			"""\
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.
"""

_a    :   Any        =			"""
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]
"""

_a    :   List[str]        =			{
    """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 	_UpperCAmelCase  (  datasets.Metric		):




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''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 			snake_case_      (   self    ,	_lowerCAmelCase		):



          '''simple docstring'''
          # 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')."		)
              lowerCAmelCase__   :Optional[int]      							=				"bleurt-base-128"

          if self.config_name.lower() in CHECKPOINT_URLS:
              lowerCAmelCase__   :int      							=				self.config_name.lower()

          elif self.config_name.upper() in CHECKPOINT_URLS:
              lowerCAmelCase__   :Optional[int]      							=				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
          lowerCAmelCase__   :Tuple      							=				dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]		)
          lowerCAmelCase__   :Dict      							=				score.BleurtScorer(os.path.join(_lowerCAmelCase    ,	_lowerCAmelCase		)		)



      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				self.scorer.score(references=_lowerCAmelCase    ,	candidates=_lowerCAmelCase		)
          return {"scores": scores}











import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_a    :   List[Any]        =			get_tests_dir("""fixtures/test_sentencepiece.model""")
_a    :   Dict        =			get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_a    :   Optional[Any]        =			"""pt""" if is_torch_available() else """tf"""







@require_sentencepiece
@require_tokenizers
class 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    CamembertTokenizer
      A          =    CamembertTokenizerFast
      A          =    True
      A          =    True
      def 			snake_case_      (   self		):



          '''simple docstring'''
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowerCAmelCase__   :List[str]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				"<pad>"
          lowerCAmelCase__   :Union[str, Any]      							=				1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase		)    ,	_lowerCAmelCase		)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase		)    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				list(self.get_tokenizer().get_vocab().keys()		)

          self.assertEqual(vocab_keys[0]    ,	"<s>NOTUSED"		)
          self.assertEqual(vocab_keys[1]    ,	"<pad>"		)
          self.assertEqual(vocab_keys[-1]    ,	"<mask>"		)
          self.assertEqual(len(_lowerCAmelCase		)    ,	1_004		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.assertEqual(self.get_tokenizer().vocab_size    ,	1_005		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				CamembertTokenizer(_lowerCAmelCase		)
          tokenizer.save_pretrained(self.tmpdirname		)
          lowerCAmelCase__   :Tuple      							=				CamembertTokenizerFast.from_pretrained(self.tmpdirname		)

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :Any      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				rust_tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.convert_ids_to_tokens(_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          if not self.test_rust_tokenizer:
              return

          lowerCAmelCase__   :Optional[Any]      							=				self.get_tokenizer()
          lowerCAmelCase__   :int      							=				self.get_rust_tokenizer()

          lowerCAmelCase__   :List[str]      							=				"I was born in 92000, and this is falsé."

          lowerCAmelCase__   :int      							=				tokenizer.tokenize(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				rust_tokenizer.tokenize(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase    ,	add_special_tokens=_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :Dict      							=				self.get_rust_tokenizer()
          lowerCAmelCase__   :Tuple      							=				tokenizer.encode(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				rust_tokenizer.encode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase    ,	_lowerCAmelCase		)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # fmt: off
          lowerCAmelCase__   :str      							=				{"input_ids": [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          lowerCAmelCase__   :Tuple      							=				[
              "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
              "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
              "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
              "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
              "telles que la traduction et la synthèse de texte.",
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=_lowerCAmelCase    ,	model_name="camembert-base"    ,	revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"    ,	sequences=_lowerCAmelCase    ,	)



