





import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   GPTaTokenizer
  A_      							=   GPTaTokenizerFast
  A_      							=   True
  A_      							=   {'add_prefix_space': True}
  A_      							=   False






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Optional[int]:






   '''simple docstring'''

   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   __lowerCAmelCase					:	Optional[int]      =       [
       """l""",
       """o""",
       """w""",
       """e""",
       """r""",
       """s""",
       """t""",
       """i""",
       """d""",
       """n""",
       """\u0120""",
       """\u0120l""",
       """\u0120n""",
       """\u0120lo""",
       """\u0120low""",
       """er""",
       """\u0120lowest""",
       """\u0120newer""",
       """\u0120wider""",
       """<unk>""",
       """<|endoftext|>""",
   ]
   __lowerCAmelCase					:	List[Any]      =       dict(zip(_snake_case       ,  range(len(_snake_case      )      )      )      )
   __lowerCAmelCase					:	int      =       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
   __lowerCAmelCase					:	str      =       {"""unk_token""": """<unk>"""}

   __lowerCAmelCase					:	List[Any]      =       os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]      )
   __lowerCAmelCase					:	int      =       os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""merges_file"""]      )
   with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""      ) as fp:
    fp.write(json.dumps(_snake_case      ) + """\n"""      )
   with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""      ) as fp:
    fp.write("""\n""".join(_snake_case      )      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  **_snake_case			:					int      )->Any:






   '''simple docstring'''

   kwargs.update(self.special_tokens_map      )
   return GPTaTokenizer.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					int       ,  **_snake_case			:					List[Any]      )->Tuple:






   '''simple docstring'''

   kwargs.update(self.special_tokens_map      )
   return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,  **_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					int      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       """lower newer"""
   __lowerCAmelCase					:	Optional[int]      =       """lower newer"""
   return input_text, output_text






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       GPTaTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map      )
   __lowerCAmelCase					:	Dict      =       """lower newer"""
   __lowerCAmelCase					:	Union[str, Any]      =       ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
   __lowerCAmelCase					:	Optional[Any]      =       tokenizer.tokenize(_snake_case       ,  add_prefix_space=_snake_case      )
   self.assertListEqual(_snake_case       ,  _snake_case      )

   __lowerCAmelCase					:	List[str]      =       tokens + [tokenizer.unk_token]
   __lowerCAmelCase					:	Tuple      =       [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case      )       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->str:






   '''simple docstring'''

   if not self.test_rust_tokenizer:
    return

   __lowerCAmelCase					:	List[str]      =       self.get_tokenizer()
   __lowerCAmelCase					:	Optional[int]      =       self.get_rust_tokenizer(add_prefix_space=_snake_case      )

   __lowerCAmelCase					:	str      =       """lower newer"""

   # Testing tokenization
   __lowerCAmelCase					:	Optional[int]      =       tokenizer.tokenize(_snake_case       ,  add_prefix_space=_snake_case      )
   __lowerCAmelCase					:	Dict      =       rust_tokenizer.tokenize(_snake_case      )
   self.assertListEqual(_snake_case       ,  _snake_case      )

   # Testing conversion to ids without special tokens
   __lowerCAmelCase					:	Dict      =       tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case       ,  add_prefix_space=_snake_case      )
   __lowerCAmelCase					:	int      =       rust_tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
   self.assertListEqual(_snake_case       ,  _snake_case      )

   # Testing conversion to ids with special tokens
   __lowerCAmelCase					:	Optional[Any]      =       self.get_rust_tokenizer(add_prefix_space=_snake_case      )
   __lowerCAmelCase					:	Any      =       tokenizer.encode(_snake_case       ,  add_prefix_space=_snake_case      )
   __lowerCAmelCase					:	List[Any]      =       rust_tokenizer.encode(_snake_case      )
   self.assertListEqual(_snake_case       ,  _snake_case      )

   # Testing the unknown token
   __lowerCAmelCase					:	str      =       tokens + [rust_tokenizer.unk_token]
   __lowerCAmelCase					:	List[str]      =       [14, 15, 10, 9, 3, 2, 15, 19]
   self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_snake_case      )       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					int       ,  *_snake_case			:					Any       ,  **_snake_case			:					Tuple      )->Tuple:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					Optional[Any]=15      )->Tuple:






   '''simple docstring'''

   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
    with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
     __lowerCAmelCase					:	Dict      =       self.rust_tokenizer_class.from_pretrained(_snake_case       ,  **_snake_case      )

     # Simple input
     __lowerCAmelCase					:	int      =       """This is a simple input"""
     __lowerCAmelCase					:	List[str]      =       ["""This is a simple input 1""", """This is a simple input 2"""]
     __lowerCAmelCase					:	int      =       ("""This is a simple input""", """This is a pair""")
     __lowerCAmelCase					:	Union[str, Any]      =       [
         ("""This is a simple input 1""", """This is a simple input 2"""),
         ("""This is a simple pair 1""", """This is a simple pair 2"""),
     ]

     # Simple input tests
     self.assertRaises(_snake_case       ,  tokenizer_r.encode       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""      )

     # Simple input
     self.assertRaises(_snake_case       ,  tokenizer_r.encode_plus       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""      )

     # Simple input
     self.assertRaises(
         _snake_case       ,  tokenizer_r.batch_encode_plus       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""       ,  )

     # Pair input
     self.assertRaises(_snake_case       ,  tokenizer_r.encode       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""      )

     # Pair input
     self.assertRaises(_snake_case       ,  tokenizer_r.encode_plus       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""      )

     # Pair input
     self.assertRaises(
         _snake_case       ,  tokenizer_r.batch_encode_plus       ,  _snake_case       ,  max_length=_snake_case       ,  padding="""max_length"""       ,  )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       GPTaTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""      )

   # Simple input
   __lowerCAmelCase					:	str      =       """This is a simple input"""
   __lowerCAmelCase					:	str      =       ["""This is a simple input looooooooong""", """This is a simple input"""]
   __lowerCAmelCase					:	Optional[Any]      =       ("""This is a simple input""", """This is a pair""")
   __lowerCAmelCase					:	Any      =       [
       ("""This is a simple input loooooong""", """This is a simple input"""),
       ("""This is a simple pair loooooong""", """This is a simple pair"""),
   ]

   __lowerCAmelCase					:	Optional[int]      =       tokenizer.pad_token_id

   __lowerCAmelCase					:	str      =       tokenizer(_snake_case       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""      )
   __lowerCAmelCase					:	str      =       tokenizer(_snake_case       ,  padding=_snake_case       ,  truncate=_snake_case       ,  return_tensors="""np"""      )
   __lowerCAmelCase					:	Dict      =       tokenizer(*_snake_case       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""      )
   __lowerCAmelCase					:	List[str]      =       tokenizer(_snake_case       ,  padding=_snake_case       ,  truncate=_snake_case       ,  return_tensors="""np"""      )

   # s
   # test single string max_length padding
   self.assertEqual(out_s["""input_ids"""].shape[-1]       ,  30      )
   self.assertTrue(pad_token_id in out_s["""input_ids"""]      )
   self.assertTrue(0 in out_s["""attention_mask"""]      )

   # s2
   # test automatic padding
   self.assertEqual(out_sa["""input_ids"""].shape[-1]       ,  33      )
   # long slice doesn't have padding
   self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]      )
   self.assertFalse(0 in out_sa["""attention_mask"""][0]      )
   # short slice does have padding
   self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]      )
   self.assertTrue(0 in out_sa["""attention_mask"""][1]      )

   # p
   # test single pair max_length padding
   self.assertEqual(out_p["""input_ids"""].shape[-1]       ,  60      )
   self.assertTrue(pad_token_id in out_p["""input_ids"""]      )
   self.assertTrue(0 in out_p["""attention_mask"""]      )

   # p2
   # test automatic padding pair
   self.assertEqual(out_pa["""input_ids"""].shape[-1]       ,  52      )
   # long slice pair doesn't have padding
   self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]      )
   self.assertFalse(0 in out_pa["""attention_mask"""][0]      )
   # short slice pair does have padding
   self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]      )
   self.assertTrue(0 in out_pa["""attention_mask"""][1]      )






  def 				UpperCAmelCase__						(							self			:					str      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       """$$$"""
   __lowerCAmelCase					:	Tuple      =       GPTaTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=_snake_case       ,  add_bos_token=_snake_case      )

   __lowerCAmelCase					:	Tuple      =       """This is a simple input"""
   __lowerCAmelCase					:	Dict      =       ["""This is a simple input 1""", """This is a simple input 2"""]

   __lowerCAmelCase					:	List[Any]      =       tokenizer.bos_token_id

   __lowerCAmelCase					:	Union[str, Any]      =       tokenizer(_snake_case      )
   __lowerCAmelCase					:	int      =       tokenizer(_snake_case      )

   self.assertEqual(out_s.input_ids[0]       ,  _snake_case      )
   self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids      )      )

   __lowerCAmelCase					:	Tuple      =       tokenizer.decode(out_s.input_ids      )
   __lowerCAmelCase					:	Optional[Any]      =       tokenizer.batch_decode(out_sa.input_ids      )

   self.assertEqual(decode_s.split()[0]       ,  _snake_case      )
   self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa      )      )






  def 				UpperCAmelCase__						(							self			:					List[str]      )->Dict:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					int      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       [self.get_tokenizer(do_lower_case=_snake_case       ,  add_bos_token=_snake_case      )]
   for tokenizer in tokenizers:
    with self.subTest(F'''{tokenizer.__class__.__name__}'''      ):
     __lowerCAmelCase					:	int      =       """Encode this."""
     __lowerCAmelCase					:	List[Any]      =       """This one too please."""
     __lowerCAmelCase					:	Union[str, Any]      =       tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
     encoded_sequence += tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
     __lowerCAmelCase					:	int      =       tokenizer.encode_plus(
         _snake_case       ,  _snake_case       ,  add_special_tokens=_snake_case       ,  return_special_tokens_mask=_snake_case       ,  )
     __lowerCAmelCase					:	str      =       encoded_sequence_dict["""input_ids"""]
     __lowerCAmelCase					:	List[str]      =       encoded_sequence_dict["""special_tokens_mask"""]
     self.assertEqual(len(_snake_case      )       ,  len(_snake_case      )      )

     __lowerCAmelCase					:	Tuple      =       [
         (x if not special_tokens_mask[i] else None) for i, x in enumerate(_snake_case      )
     ]
     __lowerCAmelCase					:	Union[str, Any]      =       [x for x in filtered_sequence if x is not None]
     self.assertEqual(_snake_case       ,  _snake_case      )






@require_tokenizers
class        snake_case_    (		unittest.TestCase						):






  def 				UpperCAmelCase__						(							self			:					int      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=_snake_case      )
   __lowerCAmelCase					:	List[Any]      =       """A photo of a cat"""

   __lowerCAmelCase					:	List[str]      =       tokenizer.encode(
       _snake_case       ,  )
   self.assertEqual(_snake_case       ,  [2, 250, 1345, 9, 10, 4758]      )
   tokenizer.save_pretrained("""test_opt"""      )

   __lowerCAmelCase					:	int      =       AutoTokenizer.from_pretrained("""./test_opt"""      )
   __lowerCAmelCase					:	Any      =       tokenizer.encode(
       _snake_case       ,  )
   self.assertEqual(_snake_case       ,  [2, 250, 1345, 9, 10, 4758]      )






  def 				UpperCAmelCase__						(							self			:					int      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  use_slow=_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       """A photo of a cat"""

   __lowerCAmelCase					:	List[Any]      =       tokenizer.encode(
       _snake_case       ,  )
   # Same as above
   self.assertEqual(_snake_case       ,  [2, 250, 1345, 9, 10, 4758]      )






  @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""      )
  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=_snake_case      )

   __lowerCAmelCase					:	str      =       """bos"""
   __lowerCAmelCase					:	Tuple      =       tokenizer.get_vocab()["""bos"""]

   __lowerCAmelCase					:	Any      =       """A photo of a cat"""
   __lowerCAmelCase					:	Optional[int]      =       tokenizer.encode(
       _snake_case       ,  )
   # We changed the bos token
   self.assertEqual(_snake_case       ,  [31957, 250, 1345, 9, 10, 4758]      )
   tokenizer.save_pretrained("""./tok"""      )
   __lowerCAmelCase					:	Optional[int]      =       AutoTokenizer.from_pretrained("""./tok"""      )
   self.assertTrue(tokenizer.is_fast      )
   __lowerCAmelCase					:	List[str]      =       tokenizer.encode(
       _snake_case       ,  )
   self.assertEqual(_snake_case       ,  [31957, 250, 1345, 9, 10, 4758]      )






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

import datasets


_UpperCAmelCase             =							'\\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'

_UpperCAmelCase             =							'\\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'

_UpperCAmelCase             =							'\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        snake_case_    (		datasets.Metric						):






  def 				UpperCAmelCase__						(							self			:					Tuple      )->str:






   '''simple docstring'''

   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			:					Dict       ,  _snake_case			:					List[Any]       ,  _snake_case			:					int       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       len(references[0]      )
   if any(len(_snake_case      ) != references_per_prediction for refs in references      ):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""      )
   __lowerCAmelCase					:	str      =       [[refs[i] for refs in references] for i in range(_snake_case      )]

   __lowerCAmelCase					:	Tuple      =       TER(
       normalized=_snake_case       ,  no_punct=_snake_case       ,  asian_support=_snake_case       ,  case_sensitive=_snake_case       ,  )
   __lowerCAmelCase					:	List[Any]      =       sb_ter.corpus_score(_snake_case       ,  _snake_case      )

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