





'''simple docstring'''






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase						: List[Any]		=   get_tests_dir('fixtures/test_sentencepiece.model')







@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				XGLMTokenizer
  lowerCAmelCase_					=				XGLMTokenizerFast
  lowerCAmelCase_					=				True
  lowerCAmelCase_					=				True


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Union[str, Any]:
    super().setUp()

    # We have a SentencePiece fixture for testing
    _UpperCamelCase       			=XGLMTokenizer(A__							,	keep_accents=A__  )
    tokenizer.save_pretrained(self.tmpdirname  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						int:
    _UpperCamelCase       			="""<pad>"""
    _UpperCamelCase       			=1

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


  def   UpperCamelCase__      (				self    :					Any  )     ->						int:
    _UpperCamelCase       			=list(self.get_tokenizer().get_vocab().keys()  )

    self.assertEqual(vocab_keys[0]							,	'''<s>'''  )
    self.assertEqual(vocab_keys[1]							,	'''<pad>'''  )
    self.assertEqual(len(A__  )							,	1008  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						str:
    self.assertEqual(self.get_tokenizer().vocab_size							,	1008  )


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase       			=XGLMTokenizer(A__							,	keep_accents=A__  )

    _UpperCamelCase       			=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]]							,	)

    _UpperCamelCase       			=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''',
            '''.''',
        ]							,	)
    _UpperCamelCase       			=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]
        ]							,	)

    _UpperCamelCase       			=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>''',
            '''.''',
        ]							,	)


  @cached_property
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    return XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''  )


  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    with tempfile.NamedTemporaryFile() as f:
      shutil.copyfile(A__							,	f.name  )
      _UpperCamelCase       			=XGLMTokenizer(f.name							,	keep_accents=A__  )
      _UpperCamelCase       			=pickle.dumps(A__  )
    pickle.loads(A__  )


  def   UpperCamelCase__      (				self    :					int  )     ->						Tuple:
    if not self.test_rust_tokenizer:
      return

    _UpperCamelCase       			=self.get_tokenizer()
    _UpperCamelCase       			=self.get_rust_tokenizer()

    _UpperCamelCase       			="""I was born in 92000, and this is falsé."""

    _UpperCamelCase       			=tokenizer.tokenize(A__  )
    _UpperCamelCase       			=rust_tokenizer.tokenize(A__  )
    self.assertListEqual(A__							,	A__  )

    _UpperCamelCase       			=tokenizer.encode(A__							,	add_special_tokens=A__  )
    _UpperCamelCase       			=rust_tokenizer.encode(A__							,	add_special_tokens=A__  )
    self.assertListEqual(A__							,	A__  )

    _UpperCamelCase       			=self.get_rust_tokenizer()
    _UpperCamelCase       			=tokenizer.encode(A__  )
    _UpperCamelCase       			=rust_tokenizer.encode(A__  )
    self.assertListEqual(A__							,	A__  )


  @slow
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Tuple:
    _UpperCamelCase       			="""Hello World!"""
    _UpperCamelCase       			=[2, 3_1227, 4447, 35]

    self.assertListEqual(A__							,	self.big_tokenizer.encode(A__  )  )


  @slow
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						List[str]:
    _UpperCamelCase       			=(
        """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
        """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
    )
    # fmt: off
    _UpperCamelCase       			=[2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 7_1630, 2_8085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 1_3675, 377, 652, 7580, 1_0341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 20_2277, 1_7892, 33, 60, 87, 4, 3234, 157, 61, 2667, 5_2376, 19, 88, 23, 735]
    # fmt: on

    self.assertListEqual(A__							,	self.big_tokenizer.encode(A__  )  )




  @slow
  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						str:
    # fmt: off
    _UpperCamelCase       			={
        """input_ids""": [[2, 10_8825, 1163, 15, 8_8010, 473, 1_5898, 157, 1_3672, 1857, 312, 8, 23_8021, 1163, 53, 1_3672, 1857, 312, 8, 5_3283, 18_2396, 8, 1_8566, 16, 3_6733, 4101, 8, 230, 24_4017, 12_2553, 7, 15, 13_2597, 4, 293, 1_2511, 7610, 4, 3414, 13_2597, 9, 4, 3_2361, 362, 4, 734, 2_8512, 3_2569, 18, 4, 3_2361, 2_6096, 1_4982, 73, 1_8715, 2_1433, 23_5261, 15, 492, 1_2427, 16, 53, 1_8715, 2_1433, 6_5454, 15, 2_3659, 563, 16, 278, 597, 2843, 595, 7931, 18_2396, 6_4186, 22, 886, 595, 13_2981, 53, 2_5540, 3449, 4_3982, 3_9901, 5951, 878, 330, 4, 2_7694, 8_0269, 312, 53, 6517, 1_1780, 611, 2_0408, 5], [2, 6, 13_2597, 67, 4_2897, 33, 592, 8, 16_3729, 2_5540, 361, 13_6997, 10_9514, 17_3230, 7, 501, 60, 10_2913, 196, 5631, 235, 6_3243, 473, 6, 23_1757, 74, 5277, 7905, 53, 3095, 3_7317, 22, 454, 18_3874, 5], [2, 268, 3_1298, 4_6530, 6, 13_2935, 4_3831, 7, 597, 32, 24, 3688, 9865, 5]],
        """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, 1, 1, 1]]
    }  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=A__							,	model_name='''facebook/xglm-564M'''							,	padding=A__							,	)













'''simple docstring'''






import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


__lowerCamelCase						: str		=   '\\n@inproceedings{snover-etal-2006-study,\n    title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n    author = "Snover, Matthew  and\n      Dorr, Bonnie  and\n      Schwartz, Rich  and\n      Micciulla, Linnea  and\n      Makhoul, John",\n    booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n    month = aug # " 8-12",\n    year = "2006",\n    address = "Cambridge, Massachusetts, USA",\n    publisher = "Association for Machine Translation in the Americas",\n    url = "https://aclanthology.org/2006.amta-papers.25",\n    pages = "223--231",\n}\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__lowerCamelCase						: Optional[int]		=   '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

__lowerCamelCase						: str		=   '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'







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


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					str  )     ->						List[str]:
    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='''http://www.cs.umd.edu/~snover/tercom/'''							,	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#ter''']							,	reference_urls=[
            '''https://github.com/jhclark/tercom''',
        ]							,	)




  def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					bool = False							,	)     ->						str:
    _UpperCamelCase       			=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'''  )
    _UpperCamelCase       			=[[refs[i] for refs in references] for i in range(UpperCamelCase__  )]

    _UpperCamelCase       			=TER(
        normalized=UpperCamelCase__							,	no_punct=UpperCamelCase__							,	asian_support=UpperCamelCase__							,	case_sensitive=UpperCamelCase__							,	)
    _UpperCamelCase       			=sb_ter.corpus_score(UpperCamelCase__							,	UpperCamelCase__  )

    return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}






