




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

import datasets


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

lowerCamelCase__			:       List[str]         =							'\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'

lowerCamelCase__			:       List[Any]         =							'\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n    predictions (list of str): The system stream (a sequence of segments).\n    references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n                                    Only applies if `normalized = True`. Defaults to `False`.\n    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n    \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n    \'num_edits\' (int): The cumulative number of edits\n    \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n    Example 1:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?",\n        ...                     "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n    Example 2:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n    Example 3:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         normalized=True,\n        ...                         case_sensitive=True)\n        >>> print(results)\n        {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n    Example 4:\n        >>> predictions = ["does this sentence match??",\n        ...                     "what about this sentence?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n    Example 5:\n        >>> predictions = ["does this sentence match??",\n        ...                    "what about this sentence?",\n        ...                    "What did the TER metric user say to the developer?"]\n        >>> references = [["does this sentence match", "does this sentence match!?!"],\n        ...             ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n        ...             ["Your jokes are...", "...TERrible"]]\n        >>> ter = datasets.load_metric("ter")\n        >>> results = ter.compute(predictions=predictions,\n        ...                         references=references,\n        ...                         ignore_punct=True,\n        ...                         case_sensitive=False)\n        >>> print(results)\n        {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'





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


	'''simple docstring'''







	def 				lowerCAmelCase_  (							self  :		Tuple						):


							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 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		Union[str, Any]    ,    _lowerCAmelCase  :		bool = False    ,    _lowerCAmelCase  :		bool = False    ,    _lowerCAmelCase  :		bool = False    ,    _lowerCAmelCase  :		bool = False    ,    ):
							SCREAMING_SNAKE_CASE_     						=      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'						)
							SCREAMING_SNAKE_CASE_     						=      [[refs[i] for refs in references] for i in range(_lowerCAmelCase						)]

							SCREAMING_SNAKE_CASE_     						=      TER(
							    normalized=_lowerCAmelCase    ,    no_punct=_lowerCAmelCase    ,    asian_support=_lowerCAmelCase    ,    case_sensitive=_lowerCAmelCase    ,    )
							SCREAMING_SNAKE_CASE_     						=      sb_ter.corpus_score(_lowerCAmelCase    ,    _lowerCAmelCase						)

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





import unittest

import numpy as np

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 LevitImageProcessor





class 	lowerCamelCase_							(				unittest.TestCase ):


 '''simple docstring'''







 def __init__(							self  :		str    ,    _lowerCAmelCase  :		Optional[Any]    ,    _lowerCAmelCase  :		Any=7    ,    _lowerCAmelCase  :		Tuple=3    ,    _lowerCAmelCase  :		List[str]=18    ,    _lowerCAmelCase  :		Any=30    ,    _lowerCAmelCase  :		List[Any]=400    ,    _lowerCAmelCase  :		Dict=True    ,    _lowerCAmelCase  :		Tuple=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		Union[str, Any]=None    ,    _lowerCAmelCase  :		Tuple=True    ,    _lowerCAmelCase  :		List[Any]=[0.5, 0.5, 0.5]    ,    _lowerCAmelCase  :		Union[str, Any]=[0.5, 0.5, 0.5]    ,    ):
       SCREAMING_SNAKE_CASE_     						=      size if size is not None else {'shortest_edge': 18}
       SCREAMING_SNAKE_CASE_     						=      crop_size if crop_size is not None else {'height': 18, 'width': 18}
       SCREAMING_SNAKE_CASE_     						=      parent
       SCREAMING_SNAKE_CASE_     						=      batch_size
       SCREAMING_SNAKE_CASE_     						=      num_channels
       SCREAMING_SNAKE_CASE_     						=      image_size
       SCREAMING_SNAKE_CASE_     						=      min_resolution
       SCREAMING_SNAKE_CASE_     						=      max_resolution
       SCREAMING_SNAKE_CASE_     						=      do_resize
       SCREAMING_SNAKE_CASE_     						=      size
       SCREAMING_SNAKE_CASE_     						=      do_center_crop
       SCREAMING_SNAKE_CASE_     						=      crop_size
       SCREAMING_SNAKE_CASE_     						=      do_normalize
       SCREAMING_SNAKE_CASE_     						=      image_mean
       SCREAMING_SNAKE_CASE_     						=      image_std




 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }





@require_torch
@require_vision
class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   LevitImageProcessor if is_vision_available() else None



 def 				lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_     						=      LevitImageProcessingTester(self						)



 @property
 def 				lowerCAmelCase_  (							self  :		Dict						):
       return self.image_processor_tester.prepare_image_processor_dict()



 def 				lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_     						=      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_center_crop'						)						)
       self.assertTrue(hasattr(_lowerCAmelCase    ,    'size'						)						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 18}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 18, 'width': 18}						)

       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class.from_dict(self.image_processor_dict    ,    size=42    ,    crop_size=84						)
       self.assertEqual(image_processor.size    ,    {'shortest_edge': 42}						)
       self.assertEqual(image_processor.crop_size    ,    {'height': 84, 'width': 84}						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       pass



 def 				lowerCAmelCase_  (							self  :		int						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       SCREAMING_SNAKE_CASE_     						=      prepare_image_inputs(self.image_processor_tester    ,    equal_resolution=_lowerCAmelCase						)
       for image in image_inputs:
             self.assertIsInstance(_lowerCAmelCase    ,    Image.Image						)

       # Test not batched input
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		str						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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 				lowerCAmelCase_  (							self  :		Tuple						):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE_     						=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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
       SCREAMING_SNAKE_CASE_     						=      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'],
           )    ,    )