"""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   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    ):






          """simple docstring"""

          snake_case								=			size if size is not None else {'height': 18, 'width': 18}
          snake_case								=			parent
          snake_case								=			batch_size
          snake_case								=			num_channels
          snake_case								=			image_size
          snake_case								=			min_resolution
          snake_case								=			max_resolution
          snake_case								=			do_resize
          snake_case								=			size
          snake_case								=			apply_ocr

    def 		snake_case (				self    ):






          """simple docstring"""

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





@require_torch
@require_pytesseract
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    Union[str, Any]			  =						LayoutLMvaImageProcessor if is_pytesseract_available() else None

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			LayoutLMvaImageProcessingTester(self    )

    @property
    def 		snake_case (				self    ):






          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size		,    {'height': 18, 'width': 18}    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

          pass

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          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
          snake_case								=			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								=			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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          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
          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.size['height'],
                  self.image_processor_tester.size['width'],
              )		,    )

          # Test batched
          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.size['height'],
                  self.image_processor_tester.size['width'],
              )		,    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          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
          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.size['height'],
                  self.image_processor_tester.size['width'],
              )		,    )

          # Test batched
          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.size['height'],
                  self.image_processor_tester.size['width'],
              )		,    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			LayoutLMvaImageProcessor()

          from datasets import load_dataset

          snake_case								=			load_dataset('hf-internal-testing/fixtures_docvqa'		,    split='test'    )

          snake_case								=			Image.open(ds[0]['file']    ).convert('RGB'    )

          snake_case								=			image_processing(lowerCAmelCase		,    return_tensors='pt'    )

          self.assertEqual(encoding.pixel_values.shape		,    (1, 3, 2_24, 2_24)    )
          self.assertEqual(len(encoding.words    )		,    len(encoding.boxes    )    )

          # fmt: off
          # the words and boxes were obtained with Tesseract 4.1.1
          snake_case								=			[['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								=			[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
          # fmt: on

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

          # with apply_OCR = False
          snake_case								=			LayoutLMvaImageProcessor(apply_ocr=lowerCAmelCase    )

          snake_case								=			image_processing(lowerCAmelCase		,    return_tensors='pt'    )

          self.assertEqual(encoding.pixel_values.shape		,    (1, 3, 2_24, 2_24)    )



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


