





'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DetrImageProcessor







class 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  def __init__(				self    :					Optional[int]							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					Any=7							,	UpperCamelCase__    :					int=3							,	UpperCamelCase__    :					Dict=30							,	UpperCamelCase__    :					str=400							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					Optional[int]=None							,	UpperCamelCase__    :					List[str]=True							,	UpperCamelCase__    :					str=1 / 255							,	UpperCamelCase__    :					Optional[int]=True							,	UpperCamelCase__    :					Any=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					str=[0.5, 0.5, 0.5]							,	UpperCamelCase__    :					str=True							,	)     ->						Union[str, Any]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    _UpperCamelCase       			=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=num_channels
    _UpperCamelCase       			=min_resolution
    _UpperCamelCase       			=max_resolution
    _UpperCamelCase       			=do_resize
    _UpperCamelCase       			=size
    _UpperCamelCase       			=do_rescale
    _UpperCamelCase       			=rescale_factor
    _UpperCamelCase       			=do_normalize
    _UpperCamelCase       			=image_mean
    _UpperCamelCase       			=image_std
    _UpperCamelCase       			=do_pad


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Optional[int]:
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_pad": self.do_pad,
    }




  def   UpperCamelCase__      (				self    :					int							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Union[str, Any]=False  )     ->						int:
    if not batched:
      _UpperCamelCase       			=image_inputs[0]
      if isinstance(UpperCamelCase__							,	Image.Image  ):
        _UpperCamelCase			,     _UpperCamelCase       			=image.size
      else:
        _UpperCamelCase			,     _UpperCamelCase       			=image.shape[1], image.shape[2]
      if w < h:
        _UpperCamelCase       			=int(self.size['''shortest_edge'''] * h / w  )
        _UpperCamelCase       			=self.size['''shortest_edge''']
      elif w > h:
        _UpperCamelCase       			=self.size['''shortest_edge''']
        _UpperCamelCase       			=int(self.size['''shortest_edge'''] * w / h  )
      else:
        _UpperCamelCase       			=self.size['''shortest_edge''']
        _UpperCamelCase       			=self.size['''shortest_edge''']

    else:
      _UpperCamelCase       			=[]
      for image in image_inputs:
        _UpperCamelCase			,     _UpperCamelCase       			=self.get_expected_values([image]  )
        expected_values.append((expected_height, expected_width)  )
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[0]  )[0]
      _UpperCamelCase       			=max(UpperCamelCase__							,	key=lambda UpperCamelCase__  : item[1]  )[1]

    return expected_height, expected_width







@require_torch
@require_vision
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				DetrImageProcessor if is_vision_available() else None


  def   UpperCamelCase__      (				self    :					int  )     ->						int:
    _UpperCamelCase       			=DetrImageProcessingTester(self  )


  @property
  def   UpperCamelCase__      (				self    :					Dict  )     ->						Tuple:
    return self.image_processor_tester.prepare_image_processor_dict()


  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_mean'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''image_std'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_normalize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_rescale'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''rescale_factor'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_resize'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''size'''  )  )
    self.assertTrue(hasattr(UpperCamelCase__							,	'''do_pad'''  )  )


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			=self.image_processing_class.from_dict(self.image_processor_dict  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 18, '''longest_edge''': 1333}  )
    self.assertEqual(image_processor.do_pad							,	UpperCamelCase__  )

    _UpperCamelCase       			=self.image_processing_class.from_dict(
        self.image_processor_dict							,	size=42							,	max_size=84							,	pad_and_return_pixel_mask=UpperCamelCase__  )
    self.assertEqual(image_processor.size							,	{'''shortest_edge''': 42, '''longest_edge''': 84}  )
    self.assertEqual(image_processor.do_pad							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						str:
    pass


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Dict:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PIL images
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	Image.Image  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values
    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[Any]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random numpy tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	numpify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	np.ndarray  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    # Initialize image_processing
    _UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
    # create random PyTorch tensors
    _UpperCamelCase       			=prepare_image_inputs(self.image_processor_tester							,	equal_resolution=UpperCamelCase__							,	torchify=UpperCamelCase__  )
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase__							,	torch.Tensor  )

    # Test not batched input
    _UpperCamelCase       			=image_processing(image_inputs[0]							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

    # Test batched
    _UpperCamelCase       			=image_processing(UpperCamelCase__							,	return_tensors='''pt'''  ).pixel_values

    _UpperCamelCase			,     _UpperCamelCase       			=self.image_processor_tester.get_expected_values(UpperCamelCase__							,	batched=UpperCamelCase__  )

    self.assertEqual(
        encoded_images.shape							,	(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )							,	)


  @slow
  def   UpperCamelCase__      (				self    :					Tuple  )     ->						str:
    # prepare image and target
    _UpperCamelCase       			=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,	'''r'''  ) as f:
      _UpperCamelCase       			=json.loads(f.read()  )

    _UpperCamelCase       			={'''image_id''': 3_9769, '''annotations''': target}

    # encode them
    _UpperCamelCase       			=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''  )
    _UpperCamelCase       			=image_processing(images=UpperCamelCase__							,	annotations=UpperCamelCase__							,	return_tensors='''pt'''  )

    # verify pixel values
    _UpperCamelCase       			=torch.Size([1, 3, 800, 1066]  )
    self.assertEqual(encoding['''pixel_values'''].shape							,	UpperCamelCase__  )

    _UpperCamelCase       			=torch.tensor([0.2796, 0.3138, 0.3481]  )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,	UpperCamelCase__							,	atol=1E-4  )  )

    # verify area
    _UpperCamelCase       			=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,	UpperCamelCase__  )  )
    # verify boxes
    _UpperCamelCase       			=torch.Size([6, 4]  )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,	UpperCamelCase__							,	atol=1E-3  )  )
    # verify image_id
    _UpperCamelCase       			=torch.tensor([3_9769]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,	UpperCamelCase__  )  )
    # verify is_crowd
    _UpperCamelCase       			=torch.tensor([0, 0, 0, 0, 0, 0]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,	UpperCamelCase__  )  )
    # verify class_labels
    _UpperCamelCase       			=torch.tensor([75, 75, 63, 65, 17, 17]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,	UpperCamelCase__  )  )
    # verify orig_size
    _UpperCamelCase       			=torch.tensor([480, 640]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,	UpperCamelCase__  )  )
    # verify size
    _UpperCamelCase       			=torch.tensor([800, 1066]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,	UpperCamelCase__  )  )




  @slow
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						List[Any]:
    # prepare image, target and masks_path
    _UpperCamelCase       			=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,	'''r'''  ) as f:
      _UpperCamelCase       			=json.loads(f.read()  )

    _UpperCamelCase       			={'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

    _UpperCamelCase       			=pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

    # encode them
    _UpperCamelCase       			=DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''  )
    _UpperCamelCase       			=image_processing(images=UpperCamelCase__							,	annotations=UpperCamelCase__							,	masks_path=UpperCamelCase__							,	return_tensors='''pt'''  )

    # verify pixel values
    _UpperCamelCase       			=torch.Size([1, 3, 800, 1066]  )
    self.assertEqual(encoding['''pixel_values'''].shape							,	UpperCamelCase__  )

    _UpperCamelCase       			=torch.tensor([0.2796, 0.3138, 0.3481]  )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,	UpperCamelCase__							,	atol=1E-4  )  )

    # verify area
    _UpperCamelCase       			=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,	UpperCamelCase__  )  )
    # verify boxes
    _UpperCamelCase       			=torch.Size([6, 4]  )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,	UpperCamelCase__  )
    _UpperCamelCase       			=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,	UpperCamelCase__							,	atol=1E-3  )  )
    # verify image_id
    _UpperCamelCase       			=torch.tensor([3_9769]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,	UpperCamelCase__  )  )
    # verify is_crowd
    _UpperCamelCase       			=torch.tensor([0, 0, 0, 0, 0, 0]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,	UpperCamelCase__  )  )
    # verify class_labels
    _UpperCamelCase       			=torch.tensor([17, 17, 63, 75, 75, 93]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,	UpperCamelCase__  )  )
    # verify masks
    _UpperCamelCase       			=82_2873
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,	UpperCamelCase__  )
    # verify orig_size
    _UpperCamelCase       			=torch.tensor([480, 640]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,	UpperCamelCase__  )  )
    # verify size
    _UpperCamelCase       			=torch.tensor([800, 1066]  )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,	UpperCamelCase__  )  )













'''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}






