
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowercase        =  '''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowercase        =  '''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

lowercase        =  '''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''





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

					def    lowercase__      (	self					:     int   ):
												if version.parse(scb.__version__   ) < version.parse("""1.4.12"""   ):
																			raise ImportWarning(
																			    """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
																			    """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""   )
												return datasets.MetricInfo(
												    description=_DESCRIPTION   ,					citation=_CITATION   ,					homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""   ,					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/mjpost/sacreBLEU#chrf--chrf"""]   ,					reference_urls=[
												        """https://github.com/m-popovic/chrF""",
												    ]   ,					)



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     int = CHRF.CHAR_ORDER   ,					__UpperCamelCase					:     int = CHRF.WORD_ORDER   ,					__UpperCamelCase					:     int = CHRF.BETA   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					):
												lowerCamelCase_							  =					len(references[0]   )
												if any(len(__UpperCamelCase   ) != references_per_prediction for refs in references   ):
																			raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
												lowerCamelCase_							  =					[[refs[i] for refs in references] for i in range(__UpperCamelCase   )]

												lowerCamelCase_							  =					CHRF(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					sb_chrf.corpus_score(__UpperCamelCase   ,					__UpperCamelCase   )

												return {
												    "score": output.score,
												    "char_order": output.char_order,
												    "word_order": output.word_order,
												    "beta": output.beta,
												}







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


lowercase        =  get_tests_dir('''fixtures/test_sentencepiece.model''')

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

lowercase        =  2_5_0_0_0_4
lowercase        =  2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    MBartaaTokenizer
					SCREAMING_SNAKE_CASE			    =    MBartaaTokenizerFast
					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    True

					def    lowercase__      (	self					:     Tuple   ):
												super().setUp()

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

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					"""<s>"""
												lowerCamelCase_							  =					0

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

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					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(__UpperCamelCase   )   ,					1_0_5_4   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												self.assertEqual(self.get_tokenizer().vocab_size   ,					1_0_5_4   )

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					MBartaaTokenizer(__UpperCamelCase   ,					src_lang="""en_XX"""   ,					tgt_lang="""ro_RO"""   ,					keep_accents=__UpperCamelCase   )

												lowerCamelCase_							  =					tokenizer.tokenize("""This is a test"""   )
												self.assertListEqual(__UpperCamelCase   ,					["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(__UpperCamelCase   )   ,					[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]   ,					)

												lowerCamelCase_							  =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[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_							  =					tokenizer.convert_tokens_to_ids(__UpperCamelCase   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[
												        value + tokenizer.fairseq_offset
												        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
												    ]   ,					)

												lowerCamelCase_							  =					tokenizer.convert_ids_to_tokens(__UpperCamelCase   )
												self.assertListEqual(
												    __UpperCamelCase   ,					[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    lowercase__      (	self					:     Optional[int]   ):
												# fmt: off
												lowerCamelCase_							  =					{"""input_ids""": [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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=__UpperCamelCase   ,					model_name="""facebook/mbart-large-50"""   ,					revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""   ,					)



					def    lowercase__      (	self					:     Dict   ):
												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_							  =					(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_							  =					self.rust_tokenizer_class.from_pretrained(__UpperCamelCase   ,					**__UpperCamelCase   )
																										lowerCamelCase_							  =					self.tokenizer_class.from_pretrained(__UpperCamelCase   ,					**__UpperCamelCase   )

																										lowerCamelCase_							  =					tempfile.mkdtemp()

																										lowerCamelCase_							  =					tokenizer_r.save_pretrained(__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.save_pretrained(__UpperCamelCase   )

																										# 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_							  =					tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f   )
																										self.assertSequenceEqual(__UpperCamelCase   ,					__UpperCamelCase   )

																										# Checks everything loads correctly in the same way
																										lowerCamelCase_							  =					tokenizer_r.from_pretrained(__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.from_pretrained(__UpperCamelCase   )

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

																										shutil.rmtree(__UpperCamelCase   )

																										# Save tokenizer rust, legacy_format=True
																										lowerCamelCase_							  =					tempfile.mkdtemp()

																										lowerCamelCase_							  =					tokenizer_r.save_pretrained(__UpperCamelCase   ,					legacy_format=__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.save_pretrained(__UpperCamelCase   )

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

																										# Checks everything loads correctly in the same way
																										lowerCamelCase_							  =					tokenizer_r.from_pretrained(__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.from_pretrained(__UpperCamelCase   )

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

																										shutil.rmtree(__UpperCamelCase   )

																										# Save tokenizer rust, legacy_format=False
																										lowerCamelCase_							  =					tempfile.mkdtemp()

																										lowerCamelCase_							  =					tokenizer_r.save_pretrained(__UpperCamelCase   ,					legacy_format=__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.save_pretrained(__UpperCamelCase   )

																										# 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_							  =					tokenizer_r.from_pretrained(__UpperCamelCase   )
																										lowerCamelCase_							  =					tokenizer_p.from_pretrained(__UpperCamelCase   )

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

																										shutil.rmtree(__UpperCamelCase   )





@require_torch
@require_sentencepiece
@require_tokenizers
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    '''facebook/mbart-large-50-one-to-many-mmt'''
					SCREAMING_SNAKE_CASE			    =    [
					    ''' 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.''',
					]
					SCREAMING_SNAKE_CASE			    =    [
					    '''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.''',
					]
					SCREAMING_SNAKE_CASE			    =    [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]

					@classmethod
					def    lowercase__      (	cls					:     Optional[Any]   ):
												lowerCamelCase_							  =					MBartaaTokenizer.from_pretrained(
												    cls.checkpoint_name   ,					src_lang="""en_XX"""   ,					tgt_lang="""ro_RO"""   )
												lowerCamelCase_							  =					1
												return cls

					def    lowercase__      (	self					:     Union[str, Any]   ):
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]   ,					2_5_0_0_0_1   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]   ,					2_5_0_0_0_4   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]   ,					2_5_0_0_2_0   )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]   ,					2_5_0_0_3_8   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
												self.assertListEqual(self.expected_src_tokens   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Tuple   ):
												self.assertIn(__UpperCamelCase   ,					self.tokenizer.all_special_ids   )
												lowerCamelCase_							  =					[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
												lowerCamelCase_							  =					self.tokenizer.decode(__UpperCamelCase   ,					skip_special_tokens=__UpperCamelCase   )
												lowerCamelCase_							  =					self.tokenizer.decode(generated_ids[1:]   ,					skip_special_tokens=__UpperCamelCase   )
												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )
												self.assertNotIn(self.tokenizer.eos_token   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					["""this is gunna be a long sentence """ * 2_0]
												assert isinstance(src_text[0]   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					1_0
												lowerCamelCase_							  =					self.tokenizer(__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					truncation=__UpperCamelCase   ).input_ids[0]
												self.assertEqual(ids[0]   ,					__UpperCamelCase   )
												self.assertEqual(ids[-1]   ,					2   )
												self.assertEqual(len(__UpperCamelCase   )   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Any   ):
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]   )   ,					[2_5_0_0_5_3, 2_5_0_0_0_1]   )

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					tempfile.mkdtemp()
												lowerCamelCase_							  =					self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(__UpperCamelCase   )
												lowerCamelCase_							  =					MBartaaTokenizer.from_pretrained(__UpperCamelCase   )
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,					__UpperCamelCase   )

					@require_torch
					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.tokenizer(self.src_text   ,					text_target=self.tgt_text   ,					padding=__UpperCamelCase   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					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    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer(
												    self.src_text   ,					text_target=self.tgt_text   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=len(self.expected_src_tokens   )   ,					return_tensors="""pt"""   ,					)
												lowerCamelCase_							  =					shift_tokens_right(batch["""labels"""]   ,					self.tokenizer.pad_token_id   )

												self.assertIsInstance(__UpperCamelCase   ,					__UpperCamelCase   )

												self.assertEqual((2, 1_4)   ,					batch.input_ids.shape   )
												self.assertEqual((2, 1_4)   ,					batch.attention_mask.shape   )
												lowerCamelCase_							  =					batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens   ,					__UpperCamelCase   )
												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    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.tokenizer(self.src_text   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=3   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					self.tokenizer(
												    text_target=self.tgt_text   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=1_0   ,					return_tensors="""pt"""   )
												lowerCamelCase_							  =					targets["""input_ids"""]
												lowerCamelCase_							  =					shift_tokens_right(__UpperCamelCase   ,					self.tokenizer.pad_token_id   )

												self.assertEqual(batch.input_ids.shape[1]   ,					3   )
												self.assertEqual(batch.decoder_input_ids.shape[1]   ,					1_0   )



					@require_torch
					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.tokenizer._build_translation_inputs(
												    """A test"""   ,					return_tensors="""pt"""   ,					src_lang="""en_XX"""   ,					tgt_lang="""ar_AR"""   )

												self.assertEqual(
												    nested_simplify(__UpperCamelCase   )   ,					{
												        # en_XX, A, test, EOS
												        """input_ids""": [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
												        """attention_mask""": [[1, 1, 1, 1]],
												        # ar_AR
												        """forced_bos_token_id""": 2_5_0_0_0_1,
												    }   ,					)





