






"""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     ,     a_     ,     a_=7     ,     a_=3     ,     a_=1_8     ,     a_=3_0     ,     a_=4_0_0     ,     a_=True     ,     a_=None     ,     a_=True     ,     )	->List[str]:







        '''simple docstring'''


        _a							=	size if size is not None else {'''height''': 1_8, '''width''': 1_8}
        _a							=	parent
        _a							=	batch_size
        _a							=	num_channels
        _a							=	image_size
        _a							=	min_resolution
        _a							=	max_resolution
        _a							=	do_resize
        _a							=	size
        _a							=	apply_ocr






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







        '''simple docstring'''


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







@require_torch
@require_pytesseract
class    lowercase_    (__A,    unittest.TestCase     ):
      A__	:		Tuple       			=				LayoutLMvaImageProcessor if is_pytesseract_available() else None






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	LayoutLMvaImageProcessingTester(self	)






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







        '''simple docstring'''


        return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	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	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size     ,     {"height": 1_8, "width": 1_8}	)

        _a							=	self.image_processing_class.from_dict(self.image_processor_dict     ,     size=4_2	)
        self.assertEqual(image_processor.size     ,     {"height": 4_2, "width": 4_2}	)






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        _a							=	self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        _a							=	prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=a_	)
        for image in image_inputs:
          self.assertIsInstance(a_     ,     Image.Image	)

        # Test not batched input
        _a							=	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
        _a							=	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	)	->List[Any]:







        '''simple docstring'''


        _a							=	self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        _a							=	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
        _a							=	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
        _a							=	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	)	->int:







        '''simple docstring'''


        _a							=	self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        _a							=	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
        _a							=	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
        _a							=	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	)	->List[str]:







        '''simple docstring'''


        _a							=	LayoutLMvaImageProcessor()

        from datasets import load_dataset

        _a							=	load_dataset("hf-internal-testing/fixtures_docvqa"     ,     split="test"	)

        _a							=	Image.open(ds[0]["file"]	).convert("RGB"	)

        _a							=	image_processing(a_     ,     return_tensors="pt"	)

        self.assertEqual(encoding.pixel_values.shape     ,     (1, 3, 2_2_4, 2_2_4)	)
        self.assertEqual(len(encoding.words	)     ,     len(encoding.boxes	)	)

        # fmt: off
        # the words and boxes were obtained with Tesseract 4.1.1
        _a							=	[['''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
        _a							=	[[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
        # fmt: on

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

        # with apply_OCR = False
        _a							=	LayoutLMvaImageProcessor(apply_ocr=a_	)

        _a							=	image_processing(a_     ,     return_tensors="pt"	)

        self.assertEqual(encoding.pixel_values.shape     ,     (1, 3, 2_2_4, 2_2_4)	)










"""simple docstring"""




def        lowerCAmelCase (      UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							str					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	(boundary[1] - boundary[0]) / steps
  _a							=	boundary[0]
  _a							=	boundary[1]
  _a							=	make_points(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
  _a							=	0.0
  y += (h / 2.0) * f(UpperCamelCase_					)
  for i in x_i:
    # print(i)
    y += h * f(UpperCamelCase_					)
  y += (h / 2.0) * f(UpperCamelCase_					)
  return y






def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							Any     , UpperCamelCase_:							Any					)       ->							str:







  '''simple docstring'''







  _a							=	a + h
  while x < (b - h):
    yield x
    _a							=	x + h






def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							int:  # enter your function here







  '''simple docstring'''







  _a							=	(x - 0) * (x - 0)
  return y






def        lowerCAmelCase (      )       ->							Tuple:







  '''simple docstring'''







  _a							=	0.0  # Lower bound of integration
  _a							=	1.0  # Upper bound of integration
  _a							=	10.0  # define number of steps or resolution
  _a							=	[a, b]  # define boundary of integration
  _a							=	method_a(UpperCamelCase_     , UpperCamelCase_					)
  print(f'''y = {y}'''					)


if __name__ == "__main__":
     main()


