






'''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			:		List[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[str]=7     ,      UpperCAmelCase			:		Optional[Any]=3     ,      UpperCAmelCase			:		Optional[Any]=18     ,      UpperCAmelCase			:		Optional[int]=30     ,      UpperCAmelCase			:		str=400     ,      UpperCAmelCase			:		str=True     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		List[Any]=True     ,      )  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Dict				  =size if size is not None else {'''height''': 18, '''width''': 18}
       lowercase					:   Any				  =parent
       lowercase					:   Optional[Any]				  =batch_size
       lowercase					:   List[Any]				  =num_channels
       lowercase					:   str				  =image_size
       lowercase					:   Optional[Any]				  =min_resolution
       lowercase					:   Optional[Any]				  =max_resolution
       lowercase					:   Any				  =do_resize
       lowercase					:   Optional[int]				  =size
       lowercase					:   Tuple				  =apply_ocr






      def 				A__      ( self			:		Optional[int]		)  ->  Optional[int]:


       '''simple docstring'''

       return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class UpperCAmelCase_					(    __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			LayoutLMvaImageProcessor if is_pytesseract_available() else None






      def 				A__      ( self			:		int		)  ->  str:


       '''simple docstring'''

       lowercase					:   Any				  =LayoutLMvaImageProcessingTester(self		)






      @property
      def 				A__      ( self			:		str		)  ->  List[Any]:


       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()






      def 				A__      ( self			:		Dict		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Any				  =self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''do_resize'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''size'''		)		)
       self.assertTrue(hasattr(UpperCAmelCase     ,      '''apply_ocr'''		)		)






      def 				A__      ( self			:		int		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size     ,      {'''height''': 18, '''width''': 18}		)

       lowercase					:   Any				  =self.image_processing_class.from_dict(self.image_processor_dict     ,      size=42		)
       self.assertEqual(image_processor.size     ,      {'''height''': 42, '''width''': 42}		)






      def 				A__      ( self			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       pass






      def 				A__      ( self			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   str				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       lowercase					:   List[Any]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      Image.Image		)

       # Test not batched input
       lowercase					:   Optional[int]				  =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     ,      UpperCAmelCase		)
       self.assertIsInstance(encoding.boxes     ,      UpperCAmelCase		)

       # Test batched
       lowercase					:   Optional[Any]				  =image_processing(UpperCAmelCase     ,      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 				A__      ( self			:		List[Any]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       lowercase					:   Optional[Any]				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      numpify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      np.ndarray		)

       # Test not batched input
       lowercase					:   str				  =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					:   Any				  =image_processing(UpperCAmelCase     ,      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 				A__      ( self			:		Tuple		)  ->  int:


       '''simple docstring'''

       lowercase					:   Dict				  =self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       lowercase					:   Any				  =prepare_image_inputs(self.image_processor_tester     ,      equal_resolution=UpperCAmelCase     ,      torchify=UpperCAmelCase		)
       for image in image_inputs:
        self.assertIsInstance(UpperCAmelCase     ,      torch.Tensor		)

       # Test not batched input
       lowercase					:   Dict				  =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(UpperCAmelCase     ,      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 				A__      ( self			:		List[Any]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   int				  =LayoutLMvaImageProcessor()

       from datasets import load_dataset

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

       lowercase					:   str				  =Image.open(ds[0]['''file''']		).convert('''RGB'''		)

       lowercase					:   Union[str, Any]				  =image_processing(UpperCAmelCase     ,      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					:   Optional[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
       lowercase					:   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     ,      UpperCAmelCase		)
       self.assertListEqual(encoding.boxes     ,      UpperCAmelCase		)

       # with apply_OCR = False
       lowercase					:   Tuple				  =LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase		)

       lowercase					:   str				  =image_processing(UpperCAmelCase     ,      return_tensors='''pt'''		)

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











'''simple docstring'''



from __future__ import annotations

SCREAMING_SNAKE_CASE						=	8.988E9  # units = N * m^s * C^-2


def 							lowercase_				(    __A :       float					,      __A :       float					,      __A :       float					,      __A :       float			)       ->      dict[str, float]:



 """simple docstring"""




 lowercase					:   Dict				  =abs(chargea * chargea			)

 if (force, chargea, chargea, distance).count(0			) != 1:
  raise ValueError('''One and only one argument must be 0'''			)
 if distance < 0:
  raise ValueError('''Distance cannot be negative'''			)
 if force == 0:
  lowercase					:   Union[str, Any]				  =COULOMBS_CONSTANT * charge_product / (distance**2)
  return {"force": force}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge1": chargea}
 elif chargea == 0:
  lowercase					:   int				  =abs(__A			) * (distance**2) / (COULOMBS_CONSTANT * chargea)
  return {"charge2": chargea}
 elif distance == 0:
  lowercase					:   Tuple				  =(COULOMBS_CONSTANT * charge_product / abs(__A			)) ** 0.5
  return {"distance": distance}
 raise ValueError('''Exactly one argument must be 0'''			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



