"""simple docstring"""




import comet  # From: unbabel-comet
import torch

import datasets


_a				:		Optional[int]	   =       datasets.logging.get_logger(__name__)

_a				:		str	   =       '\\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'

_a				:		List[Any]	   =       '\\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'

_a				:		Union[str, Any]	   =       '\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 						__A							(			datasets.Metric		):






     def    __A  (		self  ):
       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    __A  (		self    ,					a__  ):
       if self.config_name == "default":
         _lowerCAmelCase						:	Optional[Any]        =					comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""  )  )
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					comet.load_from_checkpoint(comet.download_model(self.config_name  )  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__=None    ,					a__=False  ):
       if gpus is None:
         _lowerCAmelCase						:	Optional[int]        =					1 if torch.cuda.is_available() else 0
       _lowerCAmelCase						:	Any        =					{"""src""": sources, """mt""": predictions, """ref""": references}
       _lowerCAmelCase						:	List[str]        =					[dict(zip(a__    ,					a__  )  ) for t in zip(*data.values()  )]
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					self.scorer.predict(a__    ,					gpus=a__    ,					progress_bar=a__  )
       return {"mean_score": mean_score, "scores": scores}





"""simple docstring"""




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_a				:		Optional[int]	   =       get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

_a				:		Union[str, Any]	   =       250_004
_a				:		Optional[int]	   =       250_020
@require_sentencepiece
@require_tokenizers
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Optional[Any]		 =		MBartaaTokenizer
     _UpperCamelCase							:      Any		 =		MBartaaTokenizerFast
     _UpperCamelCase							:      List[str]		 =		True
     _UpperCamelCase							:      Optional[int]		 =		True






     def    __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:	Tuple        =					MBartaaTokenizer(a__    ,					src_lang="""en_XX"""    ,					tgt_lang="""ro_RO"""    ,					keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					"""<s>"""
       _lowerCAmelCase						:	Optional[int]        =					0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__  )    ,					a__  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__  )    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]    ,					"""<s>"""  )
       self.assertEqual(vocab_keys[1]    ,					"""<pad>"""  )
       self.assertEqual(vocab_keys[-1]    ,					"""<mask>"""  )
       self.assertEqual(len(a__  )    ,					1054  )






     def    __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size    ,					1054  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					MBartaaTokenizer(a__    ,					src_lang="""en_XX"""    ,					tgt_lang="""ro_RO"""    ,					keep_accents=a__  )

       _lowerCAmelCase						:	Optional[int]        =					tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__    ,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]  )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(a__  )    ,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,					)

       _lowerCAmelCase						:	int        =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
       self.assertListEqual(
           a__    ,					[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """9""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """é""", """."""]    ,					)
       _lowerCAmelCase						:	Union[str, Any]        =					tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__    ,					[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]    ,					)

       _lowerCAmelCase						:	int        =					tokenizer.convert_ids_to_tokens(a__  )
       self.assertListEqual(
           a__    ,					[SPIECE_UNDERLINE + """I""", SPIECE_UNDERLINE + """was""", SPIECE_UNDERLINE + """b""", """or""", """n""", SPIECE_UNDERLINE + """in""", SPIECE_UNDERLINE + """""", """<unk>""", """2""", """0""", """0""", """0""", """,""", SPIECE_UNDERLINE + """and""", SPIECE_UNDERLINE + """this""", SPIECE_UNDERLINE + """is""", SPIECE_UNDERLINE + """f""", """al""", """s""", """<unk>""", """."""]    ,					)






     @slow
     def    __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:	Any        =					{"""input_ids""": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 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]], """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, 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], [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, 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="""facebook/mbart-large-50"""    ,					revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""    ,					)






     def    __A  (		self  ):
       if not self.test_slow_tokenizer:
         # as we don't have a slow version, we can't compare the outputs between slow and fast versions
         return

       _lowerCAmelCase						:	List[str]        =					(self.rust_tokenizer_class, """hf-internal-testing/tiny-random-mbart50""", {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
           _lowerCAmelCase						:	List[Any]        =					self.rust_tokenizer_class.from_pretrained(a__    ,					**a__  )
           _lowerCAmelCase						:	List[str]        =					self.tokenizer_class.from_pretrained(a__    ,					**a__  )

           _lowerCAmelCase						:	Optional[int]        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	Optional[Any]        =					tokenizer_r.save_pretrained(a__  )
           _lowerCAmelCase						:	List[str]        =					tokenizer_p.save_pretrained(a__  )

           # Checks it save with the same files + the tokenizer.json file for the fast one
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )
           _lowerCAmelCase						:	Any        =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f  )
           self.assertSequenceEqual(a__    ,					a__  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	List[Any]        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	int        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )
             # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
             # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

           shutil.rmtree(a__  )

           # Save tokenizer rust, legacy_format=True
           _lowerCAmelCase						:	List[Any]        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	Dict        =					tokenizer_r.save_pretrained(a__    ,					legacy_format=a__  )
           _lowerCAmelCase						:	str        =					tokenizer_p.save_pretrained(a__  )

           # Checks it save with the same files
           self.assertSequenceEqual(a__    ,					a__  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	List[str]        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	Any        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )

           shutil.rmtree(a__  )

           # Save tokenizer rust, legacy_format=False
           _lowerCAmelCase						:	Optional[Any]        =					tempfile.mkdtemp()

           _lowerCAmelCase						:	Tuple        =					tokenizer_r.save_pretrained(a__    ,					legacy_format=a__  )
           _lowerCAmelCase						:	str        =					tokenizer_p.save_pretrained(a__  )

           # Checks it saved the tokenizer.json file
           self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files  )  )

           # Checks everything loads correctly in the same way
           _lowerCAmelCase						:	Any        =					tokenizer_r.from_pretrained(a__  )
           _lowerCAmelCase						:	int        =					tokenizer_p.from_pretrained(a__  )

           # Check special tokens are set accordingly on Rust and Python
           for key in tokenizer_pp.special_tokens_map:
             self.assertTrue(hasattr(a__    ,					a__  )  )

           shutil.rmtree(a__  )







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__A							(			unittest.TestCase		):
     _UpperCamelCase							:      Union[str, Any]		 =		"facebook/mbart-large-50-one-to-many-mmt"
     _UpperCamelCase							:      int		 =		[
         " UN Chief Says There Is No Military Solution in Syria",
         " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
     ]
     _UpperCamelCase							:      Any		 =		[
         "Şeful ONU declară că nu există o soluţie militară în Siria",
         "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
         " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
         " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
     ]
     _UpperCamelCase							:      List[str]		 =		[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]






     @classmethod
     def    __A  (		cls  ):
       _lowerCAmelCase						:	MBartaaTokenizer        =					MBartaaTokenizer.from_pretrained(
           cls.checkpoint_name    ,					src_lang="""en_XX"""    ,					tgt_lang="""ro_RO"""  )
       _lowerCAmelCase						:	str        =					1
       return cls






     def    __A  (		self  ):
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]    ,					250001  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]    ,					250004  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]    ,					250020  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]    ,					250038  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens    ,					a__  )






     def    __A  (		self  ):
       self.assertIn(a__    ,					self.tokenizer.all_special_ids  )
       _lowerCAmelCase						:	Union[str, Any]        =					[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
       _lowerCAmelCase						:	int        =					self.tokenizer.decode(a__    ,					skip_special_tokens=a__  )
       _lowerCAmelCase						:	str        =					self.tokenizer.decode(generated_ids[1:]    ,					skip_special_tokens=a__  )
       self.assertEqual(a__    ,					a__  )
       self.assertNotIn(self.tokenizer.eos_token    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					["""this is gunna be a long sentence """ * 20]
       assert isinstance(src_text[0]    ,					a__  )
       _lowerCAmelCase						:	Optional[Any]        =					10
       _lowerCAmelCase						:	Optional[int]        =					self.tokenizer(a__    ,					max_length=a__    ,					truncation=a__  ).input_ids[0]
       self.assertEqual(ids[0]    ,					a__  )
       self.assertEqual(ids[-1]    ,					2  )
       self.assertEqual(len(a__  )    ,					a__  )






     def    __A  (		self  ):
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]  )    ,					[250053, 250001]  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					tempfile.mkdtemp()
       _lowerCAmelCase						:	Tuple        =					self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(a__  )
       _lowerCAmelCase						:	List[Any]        =					MBartaaTokenizer.from_pretrained(a__  )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,					a__  )






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.tokenizer(self.src_text    ,					text_target=self.tgt_text    ,					padding=a__    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	Any        =					shift_tokens_right(batch["""labels"""]    ,					self.tokenizer.pad_token_id  )

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == RO_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.tokenizer(
           self.src_text    ,					text_target=self.tgt_text    ,					padding=a__    ,					truncation=a__    ,					max_length=len(self.expected_src_tokens  )    ,					return_tensors="""pt"""    ,					)
       _lowerCAmelCase						:	Tuple        =					shift_tokens_right(batch["""labels"""]    ,					self.tokenizer.pad_token_id  )

       self.assertIsInstance(a__    ,					a__  )

       self.assertEqual((2, 14)    ,					batch.input_ids.shape  )
       self.assertEqual((2, 14)    ,					batch.attention_mask.shape  )
       _lowerCAmelCase						:	str        =					batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens    ,					a__  )
       self.assertEqual(2    ,					batch.decoder_input_ids[0, 0]  )  # decoder_start_token_id
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens    ,					[EN_CODE]  )
       self.assertEqual(self.tokenizer.suffix_tokens    ,					[self.tokenizer.eos_token_id]  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.tokenizer(self.src_text    ,					padding=a__    ,					truncation=a__    ,					max_length=3    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	List[Any]        =					self.tokenizer(
           text_target=self.tgt_text    ,					padding=a__    ,					truncation=a__    ,					max_length=10    ,					return_tensors="""pt"""  )
       _lowerCAmelCase						:	List[str]        =					targets["""input_ids"""]
       _lowerCAmelCase						:	Any        =					shift_tokens_right(a__    ,					self.tokenizer.pad_token_id  )

       self.assertEqual(batch.input_ids.shape[1]    ,					3  )
       self.assertEqual(batch.decoder_input_ids.shape[1]    ,					10  )






     @require_torch
     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					self.tokenizer._build_translation_inputs(
           """A test"""    ,					return_tensors="""pt"""    ,					src_lang="""en_XX"""    ,					tgt_lang="""ar_AR"""  )

       self.assertEqual(
           nested_simplify(a__  )    ,					{
               # en_XX, A, test, EOS
               """input_ids""": [[250004, 62, 3034, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 250001,
           }    ,					)




