'''simple docstring'''




import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import YolosForObjectDetection, YolosModel
						from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=13			,       __lowerCAmelCase=[30, 30]			,       __lowerCAmelCase=2			,       __lowerCAmelCase=3			,       __lowerCAmelCase=True			,       __lowerCAmelCase=True			,       __lowerCAmelCase=32			,       __lowerCAmelCase=5			,       __lowerCAmelCase=4			,       __lowerCAmelCase=37			,       __lowerCAmelCase="gelu"			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase=0.1			,       __lowerCAmelCase=10			,       __lowerCAmelCase=0.02			,       __lowerCAmelCase=3			,       __lowerCAmelCase=None			,       __lowerCAmelCase=8			,       __lowerCAmelCase=10			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    parent
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    patch_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    is_training
					lowerCAmelCase     =    use_labels
					lowerCAmelCase     =    hidden_size
					lowerCAmelCase     =    num_hidden_layers
					lowerCAmelCase     =    num_attention_heads
					lowerCAmelCase     =    intermediate_size
					lowerCAmelCase     =    hidden_act
					lowerCAmelCase     =    hidden_dropout_prob
					lowerCAmelCase     =    attention_probs_dropout_prob
					lowerCAmelCase     =    type_sequence_label_size
					lowerCAmelCase     =    initializer_range
					lowerCAmelCase     =    num_labels
					lowerCAmelCase     =    scope
					lowerCAmelCase     =    n_targets
					lowerCAmelCase     =    num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					lowerCAmelCase     =    (image_size[1] // patch_size) * (image_size[0] // patch_size)
					lowerCAmelCase     =    num_patches + 1 + self.num_detection_tokens



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

					lowerCAmelCase     =    None
					if self.use_labels:
							# labels is a list of Dict (each Dict being the labels for a given example in the batch)
							lowerCAmelCase     =    []
							for i in range(self.batch_size):
									lowerCAmelCase     =    {}
									lowerCAmelCase     =    torch.randint(
									    high=self.num_labels			,       size=(self.n_targets,)			,       device=__lowerCAmelCase)
									lowerCAmelCase     =    torch.rand(self.n_targets			,       4			,       device=__lowerCAmelCase)
									labels.append(__lowerCAmelCase)

					lowerCAmelCase     =    self.get_config()

					return config, pixel_values, labels



			def 				a_			(  self):




					"""simple docstring"""


					return YolosConfig(
					    image_size=self.image_size			,       patch_size=self.patch_size			,       num_channels=self.num_channels			,       hidden_size=self.hidden_size			,       num_hidden_layers=self.num_hidden_layers			,       num_attention_heads=self.num_attention_heads			,       intermediate_size=self.intermediate_size			,       hidden_act=self.hidden_act			,       hidden_dropout_prob=self.hidden_dropout_prob			,       attention_probs_dropout_prob=self.attention_probs_dropout_prob			,       is_decoder=__lowerCAmelCase			,       initializer_range=self.initializer_range			,       num_detection_tokens=self.num_detection_tokens			,       num_labels=self.num_labels			,       )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    YolosModel(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					lowerCAmelCase     =    model(__lowerCAmelCase)
					self.parent.assertEqual(
					    result.last_hidden_state.shape			,       (self.batch_size, self.expected_seq_len, self.hidden_size))



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    YolosForObjectDetection(__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()

					lowerCAmelCase     =    model(pixel_values=__lowerCAmelCase)
					lowerCAmelCase     =    model(__lowerCAmelCase)

					self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
					self.parent.assertEqual(result.pred_boxes.shape			,       (self.batch_size, self.num_detection_tokens, 4))

					lowerCAmelCase     =    model(pixel_values=__lowerCAmelCase			,       labels=__lowerCAmelCase)

					self.parent.assertEqual(result.loss.shape			,       ())
					self.parent.assertEqual(result.logits.shape			,       (self.batch_size, self.num_detection_tokens, self.num_labels + 1))
					self.parent.assertEqual(result.pred_boxes.shape			,       (self.batch_size, self.num_detection_tokens, 4))







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.prepare_config_and_inputs()
					lowerCAmelCase   ,	lowerCAmelCase   ,	lowerCAmelCase     =    config_and_inputs
					lowerCAmelCase     =    {"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
			UpperCAmelCase_     :	Any						=			(
			    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
			)

			UpperCAmelCase_     :	Dict						=			False
			UpperCAmelCase_     :	Optional[int]						=			False
			UpperCAmelCase_     :	Optional[Any]						=			False
			UpperCAmelCase_     :	Optional[int]						=			False



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




					"""simple docstring"""


					lowerCAmelCase     =    super()._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase			,       return_labels=__lowerCAmelCase)

					if return_labels:
							if model_class.__name__ == "YolosForObjectDetection":
									lowerCAmelCase     =    []
									for i in range(self.model_tester.batch_size):
											lowerCAmelCase     =    {}
											lowerCAmelCase     =    torch.ones(
											    size=(self.model_tester.n_targets,)			,       device=__lowerCAmelCase			,       dtype=torch.long)
											lowerCAmelCase     =    torch.ones(
											    self.model_tester.n_targets			,       4			,       device=__lowerCAmelCase			,       dtype=torch.float)
											labels.append(__lowerCAmelCase)
									lowerCAmelCase     =    labels

					return inputs_dict



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    YolosModelTester(self)
					lowerCAmelCase     =    ConfigTester(self			,       config_class=__lowerCAmelCase			,       has_text_modality=__lowerCAmelCase			,       hidden_size=37)



			def 				a_			(  self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							self.assertIsInstance(model.get_input_embeddings()			,       (nn.Module))
							lowerCAmelCase     =    model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(__lowerCAmelCase			,       nn.Linear))



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							lowerCAmelCase     =    inspect.signature(model.forward)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase     =    [*signature.parameters.keys()]

							lowerCAmelCase     =    ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase     =    True

					# in YOLOS, the seq_len is different
					lowerCAmelCase     =    self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
							lowerCAmelCase     =    True
							lowerCAmelCase     =    False
							lowerCAmelCase     =    True
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							with torch.no_grad():
									lowerCAmelCase     =    model(**self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase))
							lowerCAmelCase     =    outputs.attentions
							self.assertEqual(len(__lowerCAmelCase)			,       self.model_tester.num_hidden_layers)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							lowerCAmelCase     =    True
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							with torch.no_grad():
									lowerCAmelCase     =    model(**self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase))
							lowerCAmelCase     =    outputs.attentions
							self.assertEqual(len(__lowerCAmelCase)			,       self.model_tester.num_hidden_layers)

							self.assertListEqual(
							    list(attentions[0].shape[-3:])			,       [self.model_tester.num_attention_heads, seq_len, seq_len]			,       )
							lowerCAmelCase     =    len(__lowerCAmelCase)

							# Check attention is always last and order is fine
							lowerCAmelCase     =    True
							lowerCAmelCase     =    True
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							with torch.no_grad():
									lowerCAmelCase     =    model(**self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase))

							lowerCAmelCase     =    1
							self.assertEqual(out_len + added_hidden_states			,       len(__lowerCAmelCase))

							lowerCAmelCase     =    outputs.attentions

							self.assertEqual(len(__lowerCAmelCase)			,       self.model_tester.num_hidden_layers)
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:])			,       [self.model_tester.num_attention_heads, seq_len, seq_len]			,       )



			def 				a_			(  self):




					"""simple docstring"""


					def check_hidden_states_output(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()

							with torch.no_grad():
									lowerCAmelCase     =    model(**self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase))

							lowerCAmelCase     =    outputs.hidden_states

							lowerCAmelCase     =    getattr(
							    self.model_tester			,       """expected_num_hidden_layers"""			,       self.model_tester.num_hidden_layers + 1)
							self.assertEqual(len(__lowerCAmelCase)			,       __lowerCAmelCase)

							# YOLOS has a different seq_length
							lowerCAmelCase     =    self.model_tester.expected_seq_len

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:])			,       [seq_length, self.model_tester.hidden_size]			,       )

					lowerCAmelCase   ,	lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    True
							check_hidden_states_output(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCAmelCase     =    True

							check_hidden_states_output(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*__lowerCAmelCase)







			@slow
			def 				a_			(  self):




					"""simple docstring"""


					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase     =    YolosModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







def 							snake_case__				(     )       ->      Any:

		'''simple docstring'''


		lowerCAmelCase     =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image






@require_torch
@require_vision
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			@cached_property
			def 				a_			(  self):




					"""simple docstring"""


					return AutoImageProcessor.from_pretrained("""hustvl/yolos-small""") if is_vision_available() else None







			@slow
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""").to(__lowerCAmelCase)

					lowerCAmelCase     =    self.default_image_processor
					lowerCAmelCase     =    prepare_img()
					lowerCAmelCase     =    image_processor(images=__lowerCAmelCase			,       return_tensors="""pt""").to(__lowerCAmelCase)

					# forward pass
					with torch.no_grad():
							lowerCAmelCase     =    model(inputs.pixel_values)

					# verify outputs
					lowerCAmelCase     =    torch.Size((1, 100, 92))
					self.assertEqual(outputs.logits.shape			,       __lowerCAmelCase)

					lowerCAmelCase     =    torch.tensor(
					    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			,       device=__lowerCAmelCase			,       )
					lowerCAmelCase     =    torch.tensor(
					    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			,       device=__lowerCAmelCase)
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]			,       __lowerCAmelCase			,       atol=1E-4))
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]			,       __lowerCAmelCase			,       atol=1E-4))

					# verify postprocessing
					lowerCAmelCase     =    image_processor.post_process_object_detection(
					    __lowerCAmelCase			,       threshold=0.3			,       target_sizes=[image.size[::-1]])[0]
					lowerCAmelCase     =    torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]).to(__lowerCAmelCase)
					lowerCAmelCase     =    [75, 75, 17, 63, 17]
					lowerCAmelCase     =    torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]).to(__lowerCAmelCase)

					self.assertEqual(len(results["""scores"""])			,       5)
					self.assertTrue(torch.allclose(results["""scores"""]			,       __lowerCAmelCase			,       atol=1E-4))
					self.assertSequenceEqual(results["""labels"""].tolist()			,       __lowerCAmelCase)
					self.assertTrue(torch.allclose(results["""boxes"""][0, :]			,       __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}




