




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  ):






    """simple docstring"""





    def 				a__ ( self    )					->						Any:
         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  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         # 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_     :				Tuple  =     '''bleurt-base-128'''

         if self.config_name.lower() in CHECKPOINT_URLS:
              UpperCAmelCase_     :				Optional[int]  =     self.config_name.lower()

         elif self.config_name.upper() in CHECKPOINT_URLS:
              UpperCAmelCase_     :				Tuple  =     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_     :				List[Any]  =     dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]    )
         UpperCAmelCase_     :				Any  =     score.BleurtScorer(os.path.join(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Any:
         UpperCAmelCase_     :				Optional[Any]  =     self.scorer.score(references=_SCREAMING_SNAKE_CASE  ,candidates=_SCREAMING_SNAKE_CASE    )
         return {"scores": scores}


"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

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

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






