






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 ConditionalDetrImageProcessor
class     lowerCamelCase_					(   unittest.TestCase			):







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=7							,       __lowerCAmelCase=3							,       __lowerCAmelCase=3_0							,       __lowerCAmelCase=4_0_0							,       __lowerCAmelCase=True							,       __lowerCAmelCase=None							,       __lowerCAmelCase=True							,       __lowerCAmelCase=[0.5, 0.5, 0.5]							,       __lowerCAmelCase=[0.5, 0.5, 0.5]							,       __lowerCAmelCase=True							,       __lowerCAmelCase=1 / 2_5_5							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								__magic_name__		:str   =   size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
								__magic_name__		:Union[str, Any]   =   parent
								__magic_name__		:Optional[Any]   =   batch_size
								__magic_name__		:Tuple   =   num_channels
								__magic_name__		:int   =   min_resolution
								__magic_name__		:Union[str, Any]   =   max_resolution
								__magic_name__		:Any   =   do_resize
								__magic_name__		:Any   =   size
								__magic_name__		:int   =   do_normalize
								__magic_name__		:Dict   =   image_mean
								__magic_name__		:Optional[int]   =   image_std
								__magic_name__		:int   =   do_rescale
								__magic_name__		:List[str]   =   rescale_factor
								__magic_name__		:Union[str, Any]   =   do_pad







				def       A						(    self  ):
								"""simple docstring"""
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase=False  ):
								"""simple docstring"""
								if not batched:
												__magic_name__		:Optional[Any]   =   image_inputs[0]
												if isinstance(__lowerCAmelCase							,       Image.Image  ):
																__magic_name__  ,					__magic_name__		:Tuple   =   image.size
												else:
																__magic_name__  ,					__magic_name__		:Optional[int]   =   image.shape[1], image.shape[2]
												if w < h:
																__magic_name__		:Tuple   =   int(self.size['''shortest_edge'''] * h / w  )
																__magic_name__		:Optional[Any]   =   self.size['''shortest_edge''']
												elif w > h:
																__magic_name__		:Any   =   self.size['''shortest_edge''']
																__magic_name__		:Optional[Any]   =   int(self.size['''shortest_edge'''] * w / h  )
												else:
																__magic_name__		:Tuple   =   self.size['''shortest_edge''']
																__magic_name__		:List[str]   =   self.size['''shortest_edge''']

								else:
												__magic_name__		:Optional[int]   =   []
												for image in image_inputs:
																__magic_name__  ,					__magic_name__		:Tuple   =   self.get_expected_values([image]  )
																expected_values.append((expected_height, expected_width)  )
												__magic_name__		:List[str]   =   max(__lowerCAmelCase							,       key=lambda __lowerCAmelCase  : item[0]  )[0]
												__magic_name__		:str   =   max(__lowerCAmelCase							,       key=lambda __lowerCAmelCase  : item[1]  )[1]

								return expected_height, expected_width







@require_torch
@require_vision
class     lowerCamelCase_					(   lowerCamelCase      ,					unittest.TestCase			):
				a__       							=	ConditionalDetrImageProcessor if is_vision_available() else None







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   ConditionalDetrImageProcessingTester(self  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.image_processor_tester.prepare_image_processor_dict()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_mean'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''image_std'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_normalize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''do_resize'''  )  )
								self.assertTrue(hasattr(__lowerCAmelCase							,       '''size'''  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size							,       {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}  )
								self.assertEqual(image_processor.do_pad							,       __lowerCAmelCase  )

								__magic_name__		:Tuple   =   self.image_processing_class.from_dict(
								    self.image_processor_dict							,       size=4_2							,       max_size=8_4							,       pad_and_return_pixel_mask=__lowerCAmelCase  )
								self.assertEqual(image_processor.size							,       {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}  )
								self.assertEqual(image_processor.do_pad							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								pass







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Union[str, Any]   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								__magic_name__		:Optional[Any]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       Image.Image  )

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

								__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__  ,					__magic_name__		:int   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

								__magic_name__		:int   =   image_processing(__lowerCAmelCase							,       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       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:Dict   =   self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								__magic_name__		:Optional[int]   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       numpify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       np.ndarray  )

								# Test not batched input
								__magic_name__		:Optional[int]   =   image_processing(image_inputs[0]							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Optional[Any]   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__		:Any   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:int   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

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







				def       A						(    self  ):
								"""simple docstring"""
								# Initialize image_processing
								__magic_name__		:List[str]   =   self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								__magic_name__		:Any   =   prepare_image_inputs(self.image_processor_tester							,       equal_resolution=__lowerCAmelCase							,       torchify=__lowerCAmelCase  )
								for image in image_inputs:
												self.assertIsInstance(__lowerCAmelCase							,       torch.Tensor  )

								# Test not batched input
								__magic_name__		:Union[str, Any]   =   image_processing(image_inputs[0]							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Union[str, Any]   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase  )

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

								# Test batched
								__magic_name__		:Any   =   image_processing(__lowerCAmelCase							,       return_tensors='''pt'''  ).pixel_values

								__magic_name__  ,					__magic_name__		:Any   =   self.image_processor_tester.get_expected_values(__lowerCAmelCase							,       batched=__lowerCAmelCase  )

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







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								# prepare image and target
								__magic_name__		:Optional[int]   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,       '''r'''  ) as f:
												__magic_name__		:str   =   json.loads(f.read()  )

								__magic_name__		:List[Any]   =   {'''image_id''': 3_9_7_6_9, '''annotations''': target}

								# encode them
								__magic_name__		:Optional[int]   =   ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''  )
								__magic_name__		:Any   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Optional[Any]   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

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

								# verify area
								__magic_name__		:Any   =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:Optional[int]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:Any   =   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:Optional[Any]   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Any   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:List[Any]   =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify orig_size
								__magic_name__		:List[str]   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:Union[str, Any]   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )







				@slow
				def       A						(    self  ):
								"""simple docstring"""
								# prepare image, target and masks_path
								__magic_name__		:Tuple   =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,       '''r'''  ) as f:
												__magic_name__		:str   =   json.loads(f.read()  )

								__magic_name__		:int   =   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

								__magic_name__		:int   =   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''  )

								# encode them
								__magic_name__		:str   =   ConditionalDetrImageProcessor(format='''coco_panoptic'''  )
								__magic_name__		:int   =   image_processing(images=__lowerCAmelCase							,       annotations=__lowerCAmelCase							,       masks_path=__lowerCAmelCase							,       return_tensors='''pt'''  )

								# verify pixel values
								__magic_name__		:Optional[int]   =   torch.Size([1, 3, 8_0_0, 1_0_6_6]  )
								self.assertEqual(encoding['''pixel_values'''].shape							,       __lowerCAmelCase  )

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

								# verify area
								__magic_name__		:List[Any]   =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,       __lowerCAmelCase  )  )
								# verify boxes
								__magic_name__		:Union[str, Any]   =   torch.Size([6, 4]  )
								self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,       __lowerCAmelCase							,       atol=1E-3  )  )
								# verify image_id
								__magic_name__		:Tuple   =   torch.tensor([3_9_7_6_9]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,       __lowerCAmelCase  )  )
								# verify is_crowd
								__magic_name__		:Dict   =   torch.tensor([0, 0, 0, 0, 0, 0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,       __lowerCAmelCase  )  )
								# verify class_labels
								__magic_name__		:Optional[int]   =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,       __lowerCAmelCase  )  )
								# verify masks
								__magic_name__		:str   =   8_2_2_8_7_3
								self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,       __lowerCAmelCase  )
								# verify orig_size
								__magic_name__		:Dict   =   torch.tensor([4_8_0, 6_4_0]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,       __lowerCAmelCase  )  )
								# verify size
								__magic_name__		:Tuple   =   torch.tensor([8_0_0, 1_0_6_6]  )
								self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,       __lowerCAmelCase  )  )







'''simple docstring'''




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

import datasets


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

__lowercase        =    '''\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

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

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

__lowercase        =    '''
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)
    \'num_edits\' (int): The cumulative number of edits
    \'ref_length\' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?",
        ...                     "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}

    Example 2:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}

    Example 3:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}

    Example 4:
        >>> predictions = ["does this sentence match??",
        ...                     "what about this sentence?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}

    Example 5:
        >>> predictions = ["does this sentence match??",
        ...                    "what about this sentence?",
        ...                    "What did the TER metric user say to the developer?"]
        >>> references = [["does this sentence match", "does this sentence match!?!"],
        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],
        ...             ["Your jokes are...", "...TERrible"]]
        >>> ter = datasets.load_metric("ter")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}
'''






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


			'''simple docstring'''






			def 				a_			(  self):




					"""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 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       __lowerCAmelCase = False			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    len(references[0])
					if any(len(__lowerCAmelCase) != 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(__lowerCAmelCase)]

					lowerCAmelCase     =    TER(
					    normalized=__lowerCAmelCase			,       no_punct=__lowerCAmelCase			,       asian_support=__lowerCAmelCase			,       case_sensitive=__lowerCAmelCase			,       )
					lowerCAmelCase     =    sb_ter.corpus_score(__lowerCAmelCase			,       __lowerCAmelCase)

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




