import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      lowercase_  : Tuple								=					'pt'
elif is_tf_available():
      lowercase_  : int								=					'tf'
else:
      lowercase_  : Optional[int]								=					'jax'






class 		_lowerCamelCase		( UpperCamelCase_     ,  unittest.TestCase     ):
    __a						=   PerceiverTokenizer
    __a						=   False
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     super().setUp()
     SCREAMING_SNAKE_CASE__: str=   PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname )
    @cached_property
    def     UpperCamelCase_					(	self )							->       int:
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver''' )
    def     UpperCamelCase_					(	self			,			**lowerCAmelCase )							->       PerceiverTokenizer:
     return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase=False			,			lowerCAmelCase=20			,			lowerCAmelCase=5 )							->       Tuple[str, list]:
     # XXX The default common tokenizer tests assume that every ID is decodable on its own.
     # This assumption is invalid for Perceiver because single bytes might not be
     # valid utf-8 (byte 128 for instance).
     # Here we're overriding the smallest possible method to provide
     # a clean sequence without making the same assumption.

     SCREAMING_SNAKE_CASE__: Dict=   []
     for i in range(len(lowerCAmelCase ) ):
      try:
       SCREAMING_SNAKE_CASE__: str=   tokenizer.decode([i]			,			clean_up_tokenization_spaces=lowerCAmelCase )
      except UnicodeDecodeError:
       pass
      toks.append((i, tok) )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   list(filter(lambda lowerCAmelCase : re.match(r'''^[ a-zA-Z]+$'''			,			t[1] )			,			lowerCAmelCase ) )
     SCREAMING_SNAKE_CASE__: Optional[int]=   list(filter(lambda lowerCAmelCase : [t[0]] == tokenizer.encode(t[1]			,			add_special_tokens=lowerCAmelCase )			,			lowerCAmelCase ) )
     if max_length is not None and len(lowerCAmelCase ) > max_length:
      SCREAMING_SNAKE_CASE__: List[Any]=   toks[:max_length]
     if min_length is not None and len(lowerCAmelCase ) < min_length and len(lowerCAmelCase ) > 0:
      while len(lowerCAmelCase ) < min_length:
       SCREAMING_SNAKE_CASE__: str=   toks + toks
        # toks_str = [t[1] for t in toks]
     SCREAMING_SNAKE_CASE__: Any=   [t[0] for t in toks]

     # Ensure consistency
     SCREAMING_SNAKE_CASE__: List[Any]=   tokenizer.decode(lowerCAmelCase			,			clean_up_tokenization_spaces=lowerCAmelCase )
     if " " not in output_txt and len(lowerCAmelCase ) > 1:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   (
          tokenizer.decode([toks_ids[0]]			,			clean_up_tokenization_spaces=lowerCAmelCase )
          + ''' '''
          + tokenizer.decode(toks_ids[1:]			,			clean_up_tokenization_spaces=lowerCAmelCase )
      )
     if with_prefix_space:
      SCREAMING_SNAKE_CASE__: Optional[Any]=   ''' ''' + output_txt
     SCREAMING_SNAKE_CASE__: str=   tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
     return output_txt, output_ids
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: int=   '''Unicode €.'''
     SCREAMING_SNAKE_CASE__: Dict=   tokenizer(lowerCAmelCase )
     SCREAMING_SNAKE_CASE__: int=   [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
     self.assertEqual(encoded['''input_ids''']			,			lowerCAmelCase )

     # decoding
     SCREAMING_SNAKE_CASE__: int=   tokenizer.decode(lowerCAmelCase )
     self.assertEqual(lowerCAmelCase			,			'''[CLS]Unicode €.[SEP]''' )

     SCREAMING_SNAKE_CASE__: Union[str, Any]=   tokenizer('''e è é ê ë''' )
     SCREAMING_SNAKE_CASE__: str=   [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
     self.assertEqual(encoded['''input_ids''']			,			lowerCAmelCase )
     # decoding
     SCREAMING_SNAKE_CASE__: str=   tokenizer.decode(lowerCAmelCase )
     self.assertEqual(lowerCAmelCase			,			'''[CLS]e è é ê ë[SEP]''' )

     # encode/decode, but with `encode` instead of `__call__`
     self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë''' ) )			,			'''[CLS]e è é ê ë[SEP]''' )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: Optional[int]=   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     SCREAMING_SNAKE_CASE__: str=   [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
     # fmt: on
     SCREAMING_SNAKE_CASE__: Any=   tokenizer(lowerCAmelCase			,			padding=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     self.assertIsInstance(lowerCAmelCase			,			lowerCAmelCase )

     if FRAMEWORK != "jax":
      SCREAMING_SNAKE_CASE__: List[str]=   list(batch.input_ids.numpy()[0] )
     else:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   list(batch.input_ids.tolist()[0] )

     self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )

     self.assertEqual((2, 38)			,			batch.input_ids.shape )
     self.assertEqual((2, 38)			,			batch.attention_mask.shape )
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: str=   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     SCREAMING_SNAKE_CASE__: Any=   tokenizer(lowerCAmelCase			,			padding=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     # check if input_ids are returned and no decoder_input_ids
     self.assertIn('''input_ids'''			,			lowerCAmelCase )
     self.assertIn('''attention_mask'''			,			lowerCAmelCase )
     self.assertNotIn('''decoder_input_ids'''			,			lowerCAmelCase )
     self.assertNotIn('''decoder_attention_mask'''			,			lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       List[str]:
     SCREAMING_SNAKE_CASE__: Any=   self.perceiver_tokenizer
     SCREAMING_SNAKE_CASE__: Dict=   [
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     SCREAMING_SNAKE_CASE__: List[str]=   tokenizer(
         text_target=lowerCAmelCase			,			max_length=32			,			padding='''max_length'''			,			truncation=lowerCAmelCase			,			return_tensors=lowerCAmelCase )
     self.assertEqual(32			,			targets['''input_ids'''].shape[1] )
    def     UpperCamelCase_					(	self )							->       Tuple:
     # safety check on max_len default value so we are sure the test works
     SCREAMING_SNAKE_CASE__: Tuple=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}' ):
       self.assertNotEqual(tokenizer.model_max_length			,			42 )

        # Now let's start the test
     SCREAMING_SNAKE_CASE__: Any=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}' ):
       # Isolate this from the other tests because we save additional tokens/etc
       SCREAMING_SNAKE_CASE__: Dict=   tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE__: int=   ''' He is very happy, UNwant\u00E9d,running'''
       SCREAMING_SNAKE_CASE__: str=   tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       tokenizer.save_pretrained(lowerCAmelCase )

       SCREAMING_SNAKE_CASE__: Union[str, Any]=   tokenizer.__class__.from_pretrained(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: Any=   after_tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )

       shutil.rmtree(lowerCAmelCase )

     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_tokenizers(model_max_length=42 )
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}' ):
       # Isolate this from the other tests because we save additional tokens/etc
       SCREAMING_SNAKE_CASE__: List[str]=   tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE__: List[Any]=   ''' He is very happy, UNwant\u00E9d,running'''
       tokenizer.add_tokens(['''bim''', '''bambam'''] )
       SCREAMING_SNAKE_CASE__: str=   tokenizer.additional_special_tokens
       additional_special_tokens.append('''new_additional_special_token''' )
       tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens} )
       SCREAMING_SNAKE_CASE__: List[Any]=   tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       tokenizer.save_pretrained(lowerCAmelCase )

       SCREAMING_SNAKE_CASE__: Optional[Any]=   tokenizer.__class__.from_pretrained(lowerCAmelCase )
       SCREAMING_SNAKE_CASE__: Optional[int]=   after_tokenizer.encode(lowerCAmelCase			,			add_special_tokens=lowerCAmelCase )
       self.assertListEqual(lowerCAmelCase			,			lowerCAmelCase )
       self.assertIn('''new_additional_special_token'''			,			after_tokenizer.additional_special_tokens )
       self.assertEqual(after_tokenizer.model_max_length			,			42 )

       SCREAMING_SNAKE_CASE__: Dict=   tokenizer.__class__.from_pretrained(lowerCAmelCase			,			model_max_length=43 )
       self.assertEqual(tokenizer.model_max_length			,			43 )

       shutil.rmtree(lowerCAmelCase )
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   []
     if self.test_slow_tokenizer:
      tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

     if self.test_rust_tokenizer:
      tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

     for tokenizer_class, tokenizer_utils in tokenizer_list:
      with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(lowerCAmelCase )

       with open(os.path.join(lowerCAmelCase			,			'''special_tokens_map.json''' )			,			encoding='''utf-8''' ) as json_file:
        SCREAMING_SNAKE_CASE__: Optional[int]=   json.load(lowerCAmelCase )

       with open(os.path.join(lowerCAmelCase			,			'''tokenizer_config.json''' )			,			encoding='''utf-8''' ) as json_file:
        SCREAMING_SNAKE_CASE__: List[Any]=   json.load(lowerCAmelCase )

       SCREAMING_SNAKE_CASE__: str=   [f'<extra_id_{i}>' for i in range(125 )]

       SCREAMING_SNAKE_CASE__: Dict=   added_tokens_extra_ids + [
           '''an_additional_special_token'''
       ]
       SCREAMING_SNAKE_CASE__: List[str]=   added_tokens_extra_ids + [
           '''an_additional_special_token'''
       ]

       with open(os.path.join(lowerCAmelCase			,			'''special_tokens_map.json''' )			,			'''w'''			,			encoding='''utf-8''' ) as outfile:
        json.dump(lowerCAmelCase			,			lowerCAmelCase )
       with open(os.path.join(lowerCAmelCase			,			'''tokenizer_config.json''' )			,			'''w'''			,			encoding='''utf-8''' ) as outfile:
        json.dump(lowerCAmelCase			,			lowerCAmelCase )

       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
       # "special_tokens_map.json" files
       SCREAMING_SNAKE_CASE__: Optional[Any]=   tokenizer_class.from_pretrained(
           lowerCAmelCase			,			)
       self.assertIn(
           '''an_additional_special_token'''			,			tokenizer_without_change_in_init.additional_special_tokens )
       self.assertEqual(
           ['''an_additional_special_token''']			,			tokenizer_without_change_in_init.convert_ids_to_tokens(
               tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token'''] ) )			,			)

       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
       SCREAMING_SNAKE_CASE__: Optional[int]=   added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,			lstrip=lowerCAmelCase )]
       SCREAMING_SNAKE_CASE__: int=   tokenizer_class.from_pretrained(
           lowerCAmelCase			,			additional_special_tokens=lowerCAmelCase			,			)

       self.assertIn('''a_new_additional_special_token'''			,			tokenizer.additional_special_tokens )
       self.assertEqual(
           ['''a_new_additional_special_token''']			,			tokenizer.convert_ids_to_tokens(
               tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token'''] ) )			,			)
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([178] )			,			'''�''' )
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     pass
    def     UpperCamelCase_					(	self )							->       Union[str, Any]:
     pass
    def     UpperCamelCase_					(	self )							->       Any:
     pass
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     pass



    def     UpperCamelCase_					(	self )							->       str:
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     SCREAMING_SNAKE_CASE__: List[str]=   self.get_tokenizers(fast=lowerCAmelCase			,			do_lower_case=lowerCAmelCase )
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}' ):
       SCREAMING_SNAKE_CASE__: List[Any]=   ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
       SCREAMING_SNAKE_CASE__: Dict=   tokenizer.convert_tokens_to_string(lowerCAmelCase )

       self.assertIsInstance(lowerCAmelCase			,			lowerCAmelCase )



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

import datasets


_A  =	"\\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"

_A  =	"\\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"

_A  =	"\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       _lowerCAmelCase   (      datasets.Metric			):




				def 	__a		(   self	)							-> 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 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							_UpperCamelCase = False							,							)							-> Any:
							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_         =      TER(
							    normalized=_UpperCamelCase							,							no_punct=_UpperCamelCase							,							asian_support=_UpperCamelCase							,							case_sensitive=_UpperCamelCase							,							)
							lowerCAmelCase_         =      sb_ter.corpus_score(_UpperCamelCase							,							_UpperCamelCase	)

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



