'''simple docstring'''




import comet  # From: unbabel-comet
import torch

import datasets


_A								=   datasets.logging.get_logger(__name__)

_A								=   '\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

_A								=   '\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

_A								=   '\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'


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






   """simple docstring"""
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        return datasets.MetricInfo(
            description=_DESCRIPTION       ,				citation=_CITATION       ,				homepage='https://unbabel.github.io/COMET/html/index.html'       ,				inputs_description=_KWARGS_DESCRIPTION       ,				features=datasets.Features(
                {
                    'sources': datasets.Value('string'       ,				id='sequence'				),
                    'predictions': datasets.Value('string'       ,				id='sequence'				),
                    'references': datasets.Value('string'       ,				id='sequence'				),
                }				)       ,				codebase_urls=['https://github.com/Unbabel/COMET']       ,				reference_urls=[
                'https://github.com/Unbabel/COMET',
                'https://www.aclweb.org/anthology/2020.emnlp-main.213/',
                'http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6',
            ]       ,				)
   def 		_lowerCamelCase (self       ,				_a				)    ->     int:
        if self.config_name == "default":
             lowercase_					:       Tuple     				=					comet.load_from_checkpoint(comet.download_model('wmt20-comet-da'				)				)
        else:
             lowercase_					:       Dict     				=					comet.load_from_checkpoint(comet.download_model(self.config_name				)				)





   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a       ,				_a=None       ,				_a=False				)    ->     Optional[Any]:
        if gpus is None:
             lowercase_					:       Any     				=					1 if torch.cuda.is_available() else 0
        lowercase_					:       Dict     				=					{'src': sources, 'mt': predictions, 'ref': references}
        lowercase_					:       Union[str, Any]     				=					[dict(zip(_a       ,				_a				)				) for t in zip(*data.values()				)]
        lowercase_				,lowercase_					:       List[Any]     				=					self.scorer.predict(_a       ,				gpus=_a       ,				progress_bar=_a				)
        return {"mean_score": mean_score, "scores": scores}





'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_pytesseract_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor


class       UpperCAmelCase__  (       unittest.TestCase			):






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=7       ,				_a=3       ,				_a=18       ,				_a=30       ,				_a=400       ,				_a=True       ,				_a=None       ,				_a=True       ,				)    ->     int:
        lowercase_					:       List[str]     				=					size if size is not None else {'height': 18, 'width': 18}
        lowercase_					:       Dict     				=					parent
        lowercase_					:       Optional[Any]     				=					batch_size
        lowercase_					:       int     				=					num_channels
        lowercase_					:       List[Any]     				=					image_size
        lowercase_					:       Dict     				=					min_resolution
        lowercase_					:       List[Any]     				=					max_resolution
        lowercase_					:       Optional[Any]     				=					do_resize
        lowercase_					:       int     				=					size
        lowercase_					:       Optional[int]     				=					apply_ocr





   def 		_lowerCamelCase (self				)    ->     List[str]:
        return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}



@require_torch
@require_pytesseract
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       int     =    LayoutLMvaImageProcessor if is_pytesseract_available() else None
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        lowercase_					:       str     				=					LayoutLMvaImageProcessingTester(self				)
   @property
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Optional[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_a       ,				'do_resize'				)				)
        self.assertTrue(hasattr(_a       ,				'size'				)				)
        self.assertTrue(hasattr(_a       ,				'apply_ocr'				)				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Any     				=					self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size       ,				{'height': 18, 'width': 18}				)

        lowercase_					:       Tuple     				=					self.image_processing_class.from_dict(self.image_processor_dict       ,				size=42				)
        self.assertEqual(image_processor.size       ,				{'height': 42, 'width': 42}				)
   def 		_lowerCamelCase (self				)    ->     int:
        pass
   def 		_lowerCamelCase (self				)    ->     Dict:
        # Initialize image_processing
        lowercase_					:       int     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        lowercase_					:       Tuple     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				Image.Image				)

        # Test not batched input
        lowercase_					:       Any     				=					image_processing(image_inputs[0]       ,				return_tensors='pt'				)
        self.assertEqual(
            encoding.pixel_values.shape       ,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        self.assertIsInstance(encoding.words       ,				_a				)
        self.assertIsInstance(encoding.boxes       ,				_a				)

        # Test batched
        lowercase_					:       Union[str, Any]     				=					image_processing(_a       ,				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.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)
   def 		_lowerCamelCase (self				)    ->     Tuple:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        lowercase_					:       str     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				numpify=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				np.ndarray				)

        # Test not batched input
        lowercase_					:       List[Any]     				=					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.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        # Test batched
        lowercase_					:       str     				=					image_processing(_a       ,				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.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)
   def 		_lowerCamelCase (self				)    ->     Union[str, Any]:
        # Initialize image_processing
        lowercase_					:       List[Any]     				=					self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        lowercase_					:       str     				=					prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=_a       ,				torchify=_a				)
        for image in image_inputs:
             self.assertIsInstance(_a       ,				torch.Tensor				)

        # Test not batched input
        lowercase_					:       List[Any]     				=					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.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)

        # Test batched
        lowercase_					:       str     				=					image_processing(_a       ,				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.size['height'],
                self.image_processor_tester.size['width'],
            )       ,				)





   def 		_lowerCamelCase (self				)    ->     str:
        # with apply_OCR = True
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor()

        from datasets import load_dataset

        lowercase_					:       List[Any]     				=					load_dataset('hf-internal-testing/fixtures_docvqa'       ,				split='test'				)

        lowercase_					:       Union[str, Any]     				=					Image.open(ds[0]['file']				).convert('RGB'				)

        lowercase_					:       List[str]     				=					image_processing(_a       ,				return_tensors='pt'				)

        self.assertEqual(encoding.pixel_values.shape       ,				(1, 3, 224, 224)				)
        self.assertEqual(len(encoding.words				)       ,				len(encoding.boxes				)				)

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        lowercase_					:       str     				=					[['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']]  # noqa: E231
        lowercase_					:       Optional[Any]     				=					[[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714, 220, 726], [231, 715, 256, 726], [144, 732, 220, 745], [232, 736, 291, 747], [144, 769, 218, 782], [231, 770, 256, 782], [141, 788, 202, 801], [215, 791, 274, 804], [143, 826, 204, 838], [215, 826, 240, 838], [142, 844, 202, 857], [215, 847, 274, 859], [334, 57, 427, 69], [440, 57, 522, 69], [369, 75, 461, 88], [469, 75, 516, 88], [528, 76, 562, 88], [570, 76, 667, 88], [675, 75, 711, 87], [721, 79, 778, 88], [789, 75, 840, 88], [369, 97, 470, 107], [484, 94, 507, 106], [518, 94, 562, 107], [576, 94, 655, 110], [668, 94, 792, 109], [804, 95, 829, 107], [369, 113, 465, 125], [477, 116, 547, 125], [562, 113, 658, 125], [671, 116, 748, 125], [761, 113, 811, 125], [369, 131, 465, 143], [477, 133, 548, 143], [563, 130, 698, 145], [710, 130, 802, 146], [336, 171, 412, 183], [423, 171, 572, 183], [582, 170, 716, 184], [728, 171, 817, 187], [829, 171, 844, 186], [338, 197, 482, 212], [507, 196, 557, 209], [569, 196, 595, 208], [610, 196, 702, 209], [505, 214, 583, 226], [595, 214, 656, 227], [670, 215, 807, 227], [335, 259, 543, 274], [556, 259, 708, 272], [372, 279, 422, 291], [435, 279, 460, 291], [474, 279, 574, 292], [587, 278, 664, 291], [676, 278, 738, 291], [751, 279, 834, 291], [372, 298, 434, 310], [335, 341, 483, 354], [497, 341, 655, 354], [667, 341, 728, 354], [740, 341, 825, 354], [335, 360, 430, 372], [442, 360, 534, 372], [545, 359, 687, 372], [697, 360, 754, 372], [765, 360, 823, 373], [334, 378, 428, 391], [440, 378, 577, 394], [590, 378, 705, 391], [720, 378, 801, 391], [334, 397, 400, 409], [370, 416, 529, 429], [544, 416, 576, 432], [587, 416, 665, 428], [677, 416, 814, 429], [372, 435, 452, 450], [465, 434, 495, 447], [511, 434, 600, 447], [611, 436, 637, 447], [649, 436, 694, 451], [705, 438, 824, 447], [369, 453, 452, 466], [464, 454, 509, 466], [522, 453, 611, 469], [625, 453, 792, 469], [370, 472, 556, 488], [570, 472, 684, 487], [697, 472, 718, 485], [732, 472, 835, 488], [369, 490, 411, 503], [425, 490, 484, 503], [496, 490, 635, 506], [645, 490, 707, 503], [718, 491, 761, 503], [771, 490, 840, 503], [336, 510, 374, 521], [388, 510, 447, 522], [460, 510, 489, 521], [503, 510, 580, 522], [592, 509, 736, 525], [745, 509, 770, 522], [781, 509, 840, 522], [338, 528, 434, 541], [448, 528, 596, 541], [609, 527, 687, 540], [700, 528, 792, 541], [336, 546, 397, 559], [407, 546, 431, 559], [443, 546, 525, 560], [537, 546, 680, 562], [688, 546, 714, 559], [722, 546, 837, 562], [336, 565, 449, 581], [461, 565, 485, 577], [497, 565, 665, 581], [681, 565, 718, 577], [732, 565, 837, 580], [337, 584, 438, 597], [452, 583, 521, 596], [535, 584, 677, 599], [690, 583, 787, 596], [801, 583, 825, 596], [338, 602, 478, 615], [492, 602, 530, 614], [543, 602, 638, 615], [650, 602, 676, 614], [688, 602, 788, 615], [802, 602, 843, 614], [337, 621, 502, 633], [516, 621, 615, 637], [629, 621, 774, 636], [789, 621, 827, 633], [337, 639, 418, 652], [432, 640, 571, 653], [587, 639, 731, 655], [743, 639, 769, 652], [780, 639, 841, 652], [338, 658, 440, 673], [455, 658, 491, 670], [508, 658, 602, 671], [616, 658, 638, 670], [654, 658, 835, 674], [337, 677, 429, 689], [337, 714, 482, 726], [495, 714, 548, 726], [561, 714, 683, 726], [338, 770, 461, 782], [474, 769, 554, 785], [489, 788, 562, 803], [576, 788, 643, 801], [656, 787, 751, 804], [764, 788, 844, 801], [334, 825, 421, 838], [430, 824, 574, 838], [584, 824, 723, 841], [335, 844, 450, 857], [464, 843, 583, 860], [628, 862, 755, 875], [769, 861, 848, 878]]]  # noqa: E231
        # fmt: on

        self.assertListEqual(encoding.words       ,				_a				)
        self.assertListEqual(encoding.boxes       ,				_a				)

        # with apply_OCR = False
        lowercase_					:       Optional[int]     				=					LayoutLMvaImageProcessor(apply_ocr=_a				)

        lowercase_					:       Dict     				=					image_processing(_a       ,				return_tensors='pt'				)

        self.assertEqual(encoding.pixel_values.shape       ,				(1, 3, 224, 224)				)




