


"""simple docstring"""

import comet  # From: unbabel-comet
import torch

import datasets


UpperCAmelCase__					=datasets.logging.get_logger(__name__)

UpperCAmelCase__					="\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",\n   author = \"Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon\",\n   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n   month = nov,\n   year = \"2020\",\n   address = \"Online\",\n   publisher = \"Association for Computational Linguistics\",\n   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",\n   pages = \"2685--2702\",\n}\n"

UpperCAmelCase__					="\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n"

UpperCAmelCase__					="\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric('comet')\n    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use\n    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]\n    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]\n    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [0.19, 0.92]\n"





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      , _KWARGS_DESCRIPTION     )
class  lowerCamelCase__    (		datasets.Metric     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       return datasets.MetricInfo(
           description=_DESCRIPTION   ,							citation=_CITATION   ,							homepage="""https://unbabel.github.io/COMET/html/index.html"""   ,							inputs_description=_KWARGS_DESCRIPTION   ,							features=datasets.Features(
               {
                   """sources""": datasets.Value("""string"""   ,							id="""sequence"""				),
                   """predictions""": datasets.Value("""string"""   ,							id="""sequence"""				),
                   """references""": datasets.Value("""string"""   ,							id="""sequence"""				),
               }				)   ,							codebase_urls=["""https://github.com/Unbabel/COMET"""]   ,							reference_urls=[
               """https://github.com/Unbabel/COMET""",
               """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
               """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
           ]   ,							)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Tuple				):






       '''simple docstring'''


       if self.config_name == "default":
        __lowercase						=						comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""				)				)
       else:
        __lowercase						=						comet.load_from_checkpoint(comet.download_model(self.config_name				)				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[str]   ,							A_						:						List[Any]   ,							A_						:						int   ,							A_						:						Optional[Any]=None   ,							A_						:						Dict=False				):






       '''simple docstring'''


       if gpus is None:
        __lowercase						=						1 if torch.cuda.is_available() else 0
       __lowercase						=						{"""src""": sources, """mt""": predictions, """ref""": references}
       __lowercase						=						[dict(zip(A_   ,							A_				)				) for t in zip(*data.values()				)]
       __lowercase				,		__lowercase						=						self.scorer.predict(A_   ,							gpus=A_   ,							progress_bar=A_				)
       return {"mean_score": mean_score, "scores": scores}




"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__					=get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__					={"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__					=">>zh<<"
UpperCAmelCase__					="Helsinki-NLP/"

if is_torch_available():
       UpperCAmelCase__					="pt"
elif is_tf_available():
       UpperCAmelCase__					="tf"
else:
       UpperCAmelCase__					="jax"





@require_sentencepiece
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Dict           =		MarianTokenizer
      a			:  Optional[int]           =		False
      a			:  Any           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       super().setUp()
       __lowercase						=						["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase						=						dict(zip(A_   ,							range(len(A_				)				)				)				)
       __lowercase						=						Path(self.tmpdirname				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

       __lowercase						=						MarianTokenizer.from_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							**A_						:						Any				):






       '''simple docstring'''


       return MarianTokenizer.from_pretrained(self.tmpdirname   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						"""</s>"""
       __lowercase						=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""</s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<unk>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<pad>"""				)
       self.assertEqual(len(A_				)   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''				)
       __lowercase						=						en_de_tokenizer(["""I am a small frog"""]   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       __lowercase						=						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(A_   ,							batch.input_ids[0]				)

       __lowercase						=						tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_				)
       __lowercase						=						[x.name for x in Path(A_				).glob("""*"""				)]
       self.assertIn("""source.spm"""   ,							A_				)
       MarianTokenizer.from_pretrained(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()

       __lowercase						=						tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]   ,							padding=A_   ,							truncation=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch.input_ids.shape   ,							(2, 5_1_2)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()
       __lowercase						=						tok(["""I am a tiny frog""", """I am a small frog"""]   ,							padding=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch_smaller.input_ids.shape   ,							(2, 1_0)				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""Helsinki-NLP/opus-mt-en-de"""   ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,							decode_kwargs={"""use_source_tokenizer""": True}   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

       __lowercase						=						"""Tämä on testi"""
       __lowercase						=						"""This is a test"""

       __lowercase						=						[7_6, 7, 2_0_4_7, 2]
       __lowercase						=						[6_9, 1_2, 1_1, 9_4_0, 2]

       __lowercase						=						tokenizer(A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer(text_target=A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.decode(A_   ,							skip_special_tokens=A_				)
       self.assertEqual(A_   ,							A_				)
