




'''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        lowercase_      (						unittest.TestCase							):





     def __init__(     self     :					Union[str, Any]     ,							__lowerCamelCase     :					str     ,							__lowerCamelCase     :					Union[str, Any]=7     ,							__lowerCamelCase     :					Any=3     ,							__lowerCamelCase     :					Tuple=18     ,							__lowerCamelCase     :					List[Any]=30     ,							__lowerCamelCase     :					str=400     ,							__lowerCamelCase     :					List[str]=True     ,							__lowerCamelCase     :					List[Any]=None     ,							__lowerCamelCase     :					Any=True     ,							):
           snake_case__       :   Optional[int]	      =    size if size is not None else {"""height""": 18, """width""": 18}
           snake_case__       :   Dict	      =    parent
           snake_case__       :   Tuple	      =    batch_size
           snake_case__       :   int	      =    num_channels
           snake_case__       :   List[str]	      =    image_size
           snake_case__       :   Optional[int]	      =    min_resolution
           snake_case__       :   List[str]	      =    max_resolution
           snake_case__       :   List[str]	      =    do_resize
           snake_case__       :   str	      =    size
           snake_case__       :   int	      =    apply_ocr





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class        lowercase_      (						_SCREAMING_SNAKE_CASE						,       unittest.TestCase							):
     A_											=			LayoutLMvaImageProcessor if is_pytesseract_available() else None





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Union[str, Any]	      =    LayoutLMvaImageProcessingTester(self			)





     @property
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return self.image_processor_tester.prepare_image_processor_dict()





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Dict	      =    self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'do_resize'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'size'			)			)
           self.assertTrue(hasattr(__lowerCamelCase     ,							'apply_ocr'			)			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Dict	      =    self.image_processing_class.from_dict(self.image_processor_dict			)
           self.assertEqual(image_processor.size     ,							{'height': 18, 'width': 18}			)

           snake_case__       :   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     :					str			):
           pass





     def 			_lowerCAmelCase      (     self     :					str			):
           # Initialize image_processing
           snake_case__       :   Union[str, Any]	      =    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[str]	      =    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     ,							__lowerCamelCase			)
           self.assertIsInstance(encoding.boxes     ,							__lowerCamelCase			)

           # Test batched
           snake_case__       :   Union[str, Any]	      =    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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					Any			):
           # Initialize image_processing
           snake_case__       :   Any	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random numpy tensors
           snake_case__       :   List[Any]	      =    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__       :   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
           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,
                   self.image_processor_tester.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           # Initialize image_processing
           snake_case__       :   Dict	      =    self.image_processing_class(**self.image_processor_dict			)
           # create random PyTorch tensors
           snake_case__       :   Union[str, Any]	      =    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__       :   Optional[int]	      =    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
           snake_case__       :   Tuple	      =    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.size['height'],
                   self.image_processor_tester.size['width'],
               )     ,							)





     def 			_lowerCAmelCase      (     self     :					int			):
           # with apply_OCR = True
           snake_case__       :   Optional[int]	      =    LayoutLMvaImageProcessor()

           from datasets import load_dataset

           snake_case__       :   int	      =    load_dataset('hf-internal-testing/fixtures_docvqa'     ,							split='test'			)

           snake_case__       :   Any	      =    Image.open(ds[0]['file']			).convert('RGB'			)

           snake_case__       :   Dict	      =    image_processing(__lowerCamelCase     ,							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
           snake_case__       :   Union[str, Any]	      =    [["""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
           snake_case__       :   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     ,							__lowerCamelCase			)
           self.assertListEqual(encoding.boxes     ,							__lowerCamelCase			)

           # with apply_OCR = False
           snake_case__       :   Dict	      =    LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase			)

           snake_case__       :   Dict	      =    image_processing(__lowerCamelCase     ,							return_tensors='pt'			)

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




'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A   :    List[Any]											= logging.get_logger(__name__)

__A   :    List[Any]											= {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roberta-prelayernorm'

    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Tuple=5_02_65       ,		lowerCamelCase       :    Optional[int]=7_68       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    Optional[int]=12       ,		lowerCamelCase       :    int=30_72       ,		lowerCamelCase       :    Optional[int]="gelu"       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    Tuple=0.1       ,		lowerCamelCase       :    Optional[int]=5_12       ,		lowerCamelCase       :    Union[str, Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    Any=1E-12       ,		lowerCamelCase       :    int=1       ,		lowerCamelCase       :    List[Any]=0       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    Optional[Any]="absolute"       ,		lowerCamelCase       :    List[Any]=True       ,		lowerCamelCase       :    Tuple=None       ,		**lowerCamelCase       :    int       ,		)  ->    Optional[Any]:
      super().__init__(pad_token_id=lowerCamelCase       ,		bos_token_id=lowerCamelCase       ,		eos_token_id=lowerCamelCase       ,		**lowerCamelCase						)

      lowerCAmelCase_     : Dict     =  vocab_size
      lowerCAmelCase_     : str     =  hidden_size
      lowerCAmelCase_     : Dict     =  num_hidden_layers
      lowerCAmelCase_     : List[Any]     =  num_attention_heads
      lowerCAmelCase_     : List[str]     =  hidden_act
      lowerCAmelCase_     : Optional[Any]     =  intermediate_size
      lowerCAmelCase_     : List[str]     =  hidden_dropout_prob
      lowerCAmelCase_     : Tuple     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Optional[int]     =  max_position_embeddings
      lowerCAmelCase_     : List[Any]     =  type_vocab_size
      lowerCAmelCase_     : List[Any]     =  initializer_range
      lowerCAmelCase_     : Union[str, Any]     =  layer_norm_eps
      lowerCAmelCase_     : List[str]     =  position_embedding_type
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : Union[str, Any]     =  classifier_dropout







class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @property
    def __lowercase  (  self       :    List[Any]						)  ->    Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCAmelCase_     : Dict     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
        lowerCAmelCase_     : Optional[int]     =  {0: """batch""", 1: """sequence"""}
      return OrderedDict(
          [
              ("""input_ids""", dynamic_axis),
              ("""attention_mask""", dynamic_axis),
          ]						)



