'''simple docstring'''




import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor






class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=7			,       __lowerCAmelCase=3			,       __lowerCAmelCase=18			,       __lowerCAmelCase=30			,       __lowerCAmelCase=400			,       __lowerCAmelCase=True			,       __lowerCAmelCase=None			,       __lowerCAmelCase=True			,       __lowerCAmelCase=None			,       __lowerCAmelCase=True			,       __lowerCAmelCase=[0.5, 0.5, 0.5]			,       __lowerCAmelCase=[0.5, 0.5, 0.5]			,       __lowerCAmelCase=False			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    size if size is not None else {"""height""": 20, """width""": 20}
					lowerCAmelCase     =    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
					lowerCAmelCase     =    parent
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    min_resolution
					lowerCAmelCase     =    max_resolution
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    do_center_crop
					lowerCAmelCase     =    crop_size
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    image_mean
					lowerCAmelCase     =    image_std
					lowerCAmelCase     =    do_reduce_labels







			def 				a_			(  self):




					"""simple docstring"""


					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_reduce_labels": self.do_reduce_labels,
					}







def 							snake_case__				(     )       ->      Any:

		'''simple docstring'''


		lowerCAmelCase     =    load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""					)

		lowerCAmelCase     =    Image.open(dataset[0]["""file"""]					)
		lowerCAmelCase     =    Image.open(dataset[1]["""file"""]					)

		return image, map






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

		'''simple docstring'''


		lowerCAmelCase     =    load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""					)

		lowerCAmelCase     =    Image.open(ds[0]["""file"""]					)
		lowerCAmelCase     =    Image.open(ds[1]["""file"""]					)
		lowerCAmelCase     =    Image.open(ds[2]["""file"""]					)
		lowerCAmelCase     =    Image.open(ds[3]["""file"""]					)

		return [imagea, imagea], [mapa, mapa]






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


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[Any]						=			BeitImageProcessor if is_vision_available() else None



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    BeitImageProcessingTester(self)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_resize"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """size"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_center_crop"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """center_crop"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """do_normalize"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """image_mean"""))
					self.assertTrue(hasattr(__lowerCAmelCase			,       """image_std"""))



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class.from_dict(self.image_processor_dict)
					self.assertEqual(image_processor.size			,       {"""height""": 20, """width""": 20})
					self.assertEqual(image_processor.crop_size			,       {"""height""": 18, """width""": 18})
					self.assertEqual(image_processor.do_reduce_labels			,       __lowerCAmelCase)

					lowerCAmelCase     =    self.image_processing_class.from_dict(
					    self.image_processor_dict			,       size=42			,       crop_size=84			,       reduce_labels=__lowerCAmelCase)
					self.assertEqual(image_processor.size			,       {"""height""": 42, """width""": 42})
					self.assertEqual(image_processor.crop_size			,       {"""height""": 84, """width""": 84})
					self.assertEqual(image_processor.do_reduce_labels			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random PIL images
					lowerCAmelCase     =    prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowerCAmelCase)
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase			,       Image.Image)

					# Test not batched input
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    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,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random numpy tensors
					lowerCAmelCase     =    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
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    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,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase     =    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
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       return_tensors="""pt""").pixel_values
					self.assertEqual(
					    encoded_images.shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )

					# Test batched
					lowerCAmelCase     =    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,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)
					# create random PyTorch tensors
					lowerCAmelCase     =    prepare_image_inputs(self.image_processor_tester			,       equal_resolution=__lowerCAmelCase			,       torchify=__lowerCAmelCase)
					lowerCAmelCase     =    []
					for image in image_inputs:
							self.assertIsInstance(__lowerCAmelCase			,       torch.Tensor)
							maps.append(torch.zeros(image.shape[-2:]).long())

					# Test not batched input
					lowerCAmelCase     =    image_processing(image_inputs[0]			,       maps[0]			,       return_tensors="""pt""")
					self.assertEqual(
					    encoding["""pixel_values"""].shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(
					    encoding["""labels"""].shape			,       (
					        1,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(encoding["""labels"""].dtype			,       torch.long)
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 255)

					# Test batched
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       __lowerCAmelCase			,       return_tensors="""pt""")
					self.assertEqual(
					    encoding["""pixel_values"""].shape			,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(
					    encoding["""labels"""].shape			,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(encoding["""labels"""].dtype			,       torch.long)
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 255)

					# Test not batched input (PIL images)
					lowerCAmelCase   ,	lowerCAmelCase     =    prepare_semantic_single_inputs()

					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       __lowerCAmelCase			,       return_tensors="""pt""")
					self.assertEqual(
					    encoding["""pixel_values"""].shape			,       (
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(
					    encoding["""labels"""].shape			,       (
					        1,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(encoding["""labels"""].dtype			,       torch.long)
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 255)

					# Test batched input (PIL images)
					lowerCAmelCase   ,	lowerCAmelCase     =    prepare_semantic_batch_inputs()

					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       __lowerCAmelCase			,       return_tensors="""pt""")
					self.assertEqual(
					    encoding["""pixel_values"""].shape			,       (
					        2,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(
					    encoding["""labels"""].shape			,       (
					        2,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )			,       )
					self.assertEqual(encoding["""labels"""].dtype			,       torch.long)
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 255)







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.image_processing_class(**self.image_processor_dict)

					# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
					lowerCAmelCase   ,	lowerCAmelCase     =    prepare_semantic_single_inputs()
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       __lowerCAmelCase			,       return_tensors="""pt""")
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 150)

					lowerCAmelCase     =    True
					lowerCAmelCase     =    image_processing(__lowerCAmelCase			,       __lowerCAmelCase			,       return_tensors="""pt""")
					self.assertTrue(encoding["""labels"""].min().item() >= 0)
					self.assertTrue(encoding["""labels"""].max().item() <= 255)





'''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}




