






'''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 DeformableDetrImageProcessor







class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def __init__(		self			:List[str]       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]=7       ,							lowerCAmelCase__			:List[str]=3       ,							lowerCAmelCase__			:Optional[Any]=30       ,							lowerCAmelCase__			:Optional[Any]=400       ,							lowerCAmelCase__			:str=True       ,							lowerCAmelCase__			:Any=None       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:int=[0.5, 0.5, 0.5]       ,							lowerCAmelCase__			:List[Any]=[0.5, 0.5, 0.5]       ,							lowerCAmelCase__			:Union[str, Any]=True       ,							lowerCAmelCase__			:Union[str, Any]=1 / 255       ,							lowerCAmelCase__			:Dict=True       ,							)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Dict   =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
										snake_case_				:					int   =       parent
										snake_case_				:					Optional[int]   =       batch_size
										snake_case_				:					Tuple   =       num_channels
										snake_case_				:					Any   =       min_resolution
										snake_case_				:					Optional[Any]   =       max_resolution
										snake_case_				:					Optional[int]   =       do_resize
										snake_case_				:					List[str]   =       size
										snake_case_				:					Optional[Any]   =       do_normalize
										snake_case_				:					List[str]   =       image_mean
										snake_case_				:					Tuple   =       image_std
										snake_case_				:					Tuple   =       do_rescale
										snake_case_				:					List[str]   =       rescale_factor
										snake_case_				:					str   =       do_pad

							def 							_A	(		self			:Optional[int]				)				->  Union[str, Any]:


										'''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			:int       ,							lowerCAmelCase__			:Tuple       ,							lowerCAmelCase__			:Optional[int]=False				)				->  List[str]:


										'''simple docstring'''




										if not batched:
													snake_case_				:					Dict   =       image_inputs[0]
													if isinstance(lowerCAmelCase__       ,							Image.Image				):
																snake_case_,	snake_case_				:					Optional[int]   =       image.size
													else:
																snake_case_,	snake_case_				:					Union[str, Any]   =       image.shape[1], image.shape[2]
													if w < h:
																snake_case_				:					Tuple   =       int(self.size["shortest_edge"] * h / w				)
																snake_case_				:					List[str]   =       self.size["shortest_edge"]
													elif w > h:
																snake_case_				:					List[Any]   =       self.size["shortest_edge"]
																snake_case_				:					Union[str, Any]   =       int(self.size["shortest_edge"] * w / h				)
													else:
																snake_case_				:					Any   =       self.size["shortest_edge"]
																snake_case_				:					str   =       self.size["shortest_edge"]

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

										return expected_height, expected_width







@require_torch
@require_vision
class    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	DeformableDetrImageProcessor if is_vision_available() else None

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


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       DeformableDetrImageProcessingTester(self				)

							@property
							def 							_A	(		self			:List[str]				)				->  Dict:


										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()

							def 							_A	(		self			:Dict				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					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__       ,							"do_rescale"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"do_pad"				)				)
										self.assertTrue(hasattr(lowerCAmelCase__       ,							"size"				)				)

							def 							_A	(		self			:Optional[int]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 18, "longest_edge": 1_333}				)
										self.assertEqual(image_processor.do_pad       ,							lowerCAmelCase__				)

										snake_case_				:					str   =       self.image_processing_class.from_dict(
										    self.image_processor_dict       ,							size=42       ,							max_size=84       ,							pad_and_return_pixel_mask=lowerCAmelCase__				)
										self.assertEqual(image_processor.size       ,							{"shortest_edge": 42, "longest_edge": 84}				)
										self.assertEqual(image_processor.do_pad       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Optional[Any]				)				->  Union[str, Any]:


										'''simple docstring'''




										pass

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


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										snake_case_				:					Tuple   =       prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=lowerCAmelCase__				)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__       ,							Image.Image				)

										# Test not batched input
										snake_case_				:					List[Any]   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_,	snake_case_				:					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
										snake_case_,	snake_case_				:					Any   =       self.image_processor_tester.get_expected_values(lowerCAmelCase__       ,							batched=lowerCAmelCase__				)

										snake_case_				:					Optional[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			:List[str]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										snake_case_				:					Tuple   =       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
										snake_case_				:					Optional[Any]   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_,	snake_case_				:					str   =       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
										snake_case_				:					Optional[Any]   =       image_processing(lowerCAmelCase__       ,							return_tensors="pt"				).pixel_values

										snake_case_,	snake_case_				:					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,
										    )       ,							)

							def 							_A	(		self			:Any				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										snake_case_				:					Dict   =       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
										snake_case_				:					Tuple   =       image_processing(image_inputs[0]       ,							return_tensors="pt"				).pixel_values

										snake_case_,	snake_case_				:					Optional[int]   =       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
										snake_case_				:					Tuple   =       image_processing(lowerCAmelCase__       ,							return_tensors="pt"				).pixel_values

										snake_case_,	snake_case_				:					List[str]   =       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			:Optional[Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					str   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
										with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,							"r"				) as f:
													snake_case_				:					List[str]   =       json.loads(f.read()				)

										snake_case_				:					str   =       {"image_id": 39_769, "annotations": target}

										# encode them
										snake_case_				:					List[str]   =       DeformableDetrImageProcessor()
										snake_case_				:					Optional[Any]   =       image_processing(images=lowerCAmelCase__       ,							annotations=lowerCAmelCase__       ,							return_tensors="pt"				)

										# verify pixel values
										snake_case_				:					List[Any]   =       torch.Size([1, 3, 800, 1_066]				)
										self.assertEqual(encoding["pixel_values"].shape       ,							lowerCAmelCase__				)

										snake_case_				:					Union[str, Any]   =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)

										# verify area
										snake_case_				:					int   =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,							lowerCAmelCase__				)				)
										# verify boxes
										snake_case_				:					List[str]   =       torch.Size([6, 4]				)
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,							lowerCAmelCase__				)
										snake_case_				:					int   =       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)
										# verify image_id
										snake_case_				:					List[str]   =       torch.tensor([39_769]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,							lowerCAmelCase__				)				)
										# verify is_crowd
										snake_case_				:					Tuple   =       torch.tensor([0, 0, 0, 0, 0, 0]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,							lowerCAmelCase__				)				)
										# verify class_labels
										snake_case_				:					Union[str, Any]   =       torch.tensor([75, 75, 63, 65, 17, 17]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,							lowerCAmelCase__				)				)
										# verify orig_size
										snake_case_				:					List[str]   =       torch.tensor([480, 640]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,							lowerCAmelCase__				)				)
										# verify size
										snake_case_				:					str   =       torch.tensor([800, 1_066]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,							lowerCAmelCase__				)				)






							@slow
							def 							_A	(		self			:Optional[int]				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					str   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
										with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,							"r"				) as f:
													snake_case_				:					Optional[Any]   =       json.loads(f.read()				)

										snake_case_				:					Any   =       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

										snake_case_				:					Tuple   =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"				)

										# encode them
										snake_case_				:					Union[str, Any]   =       DeformableDetrImageProcessor(format="coco_panoptic"				)
										snake_case_				:					List[Any]   =       image_processing(images=lowerCAmelCase__       ,							annotations=lowerCAmelCase__       ,							masks_path=lowerCAmelCase__       ,							return_tensors="pt"				)

										# verify pixel values
										snake_case_				:					Optional[int]   =       torch.Size([1, 3, 800, 1_066]				)
										self.assertEqual(encoding["pixel_values"].shape       ,							lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
										self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,							lowerCAmelCase__       ,							atol=1E-4				)				)

										# verify area
										snake_case_				:					Optional[int]   =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,							lowerCAmelCase__				)				)
										# verify boxes
										snake_case_				:					str   =       torch.Size([6, 4]				)
										self.assertEqual(encoding["labels"][0]["boxes"].shape       ,							lowerCAmelCase__				)
										snake_case_				:					int   =       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,							lowerCAmelCase__       ,							atol=1E-3				)				)
										# verify image_id
										snake_case_				:					Any   =       torch.tensor([39_769]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,							lowerCAmelCase__				)				)
										# verify is_crowd
										snake_case_				:					Tuple   =       torch.tensor([0, 0, 0, 0, 0, 0]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,							lowerCAmelCase__				)				)
										# verify class_labels
										snake_case_				:					Optional[Any]   =       torch.tensor([17, 17, 63, 75, 75, 93]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,							lowerCAmelCase__				)				)
										# verify masks
										snake_case_				:					Tuple   =       822_873
										self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,							lowerCAmelCase__				)
										# verify orig_size
										snake_case_				:					int   =       torch.tensor([480, 640]				)
										self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,							lowerCAmelCase__				)				)
										# verify size
										snake_case_				:					Tuple   =       torch.tensor([800, 1_066]				)
										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


__lowerCamelCase   :					str   							=					'''\
@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",
}
'''

__lowerCamelCase   :					Dict   							=					'''\
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.
'''

__lowerCamelCase   :					int   							=					'''
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			:Optional[Any]				)				->  Optional[int]:


										'''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			:Any       ,							lowerCAmelCase__			:Any       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							lowerCAmelCase__			:bool = False       ,							)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       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"				)
										snake_case_				:					List[str]   =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]

										snake_case_				:					List[str]   =       TER(
										    normalized=lowerCAmelCase__       ,							no_punct=lowerCAmelCase__       ,							asian_support=lowerCAmelCase__       ,							case_sensitive=lowerCAmelCase__       ,							)
										snake_case_				:					Any   =       sb_ter.corpus_score(lowerCAmelCase__       ,							lowerCAmelCase__				)

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




