

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   _a					(			unittest.TestCase				):




      """simple docstring"""

      def __init__(   self							:				Optional[int]						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Optional[int]=7						,			UpperCAmelCase							:				List[str]=3						,			UpperCAmelCase							:				List[Any]=18						,			UpperCAmelCase							:				Optional[int]=30						,			UpperCAmelCase							:				Any=400						,			UpperCAmelCase							:				List[str]=True						,			UpperCAmelCase							:				List[Any]=None						,			UpperCAmelCase							:				List[str]=True						,			):
            A_						  =				size if size is not None else {"height": 18, "width": 18}
            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 		__A	(   self							:				str	):
            return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}




@require_torch
@require_pytesseract
class   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Any      		=       LayoutLMvaImageProcessor if is_pytesseract_available() else None





      def 		__A	(   self							:				Any	):
            A_						  =				LayoutLMvaImageProcessingTester(self	)





      @property
      def 		__A	(   self							:				Dict	):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 		__A	(   self							:				Tuple	):
            A_						  =				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							:				Any	):
            A_						  =				self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size						,			{"height": 18, "width": 18}	)

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





      def 		__A	(   self							:				Optional[int]	):
            pass





      def 		__A	(   self							:				List[str]	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			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						,			UpperCAmelCase	)
            self.assertIsInstance(encoding.boxes						,			UpperCAmelCase	)

            # Test batched
            A_						  =				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							:				Union[str, Any]	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            A_						  =				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
            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(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							:				Dict	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            A_						  =				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
            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(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	):
            # with apply_OCR = True
            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(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
            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_						  =				[[[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
            A_						  =				LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase	)

            A_						  =				image_processing(UpperCAmelCase						,			return_tensors="pt"	)

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


A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



