





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							):







					'''simple docstring'''




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







					def 	_lowerCamelCase						(  self       ):
									return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






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







					'''simple docstring'''




					__lowerCAmelCase					=					LayoutLMvaImageProcessor if is_pytesseract_available() else None







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					LayoutLMvaImageProcessingTester(self       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					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       ):
									__a	:  Union[str, Any]         =					self.image_processing_class.from_dict(self.image_processor_dict       )
									self.assertEqual(image_processor.size		,     {'''height''': 18, '''width''': 18}       )

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







					def 	_lowerCamelCase						(  self       ):
									pass







					def 	_lowerCamelCase						(  self       ):
									__a	:  str         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PIL images
									__a	:  Any         =					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	:  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		,     _A       )
									self.assertIsInstance(encoding.boxes		,     _A       )

									# Test batched
									__a	:  Optional[Any]         =					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       ):
									__a	:  Tuple         =					self.image_processing_class(**self.image_processor_dict       )
									# create random numpy tensors
									__a	:  Union[str, Any]         =					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	:  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
									__a	:  Dict         =					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       ):
									__a	:  Union[str, Any]         =					self.image_processing_class(**self.image_processor_dict       )
									# create random PyTorch tensors
									__a	:  int         =					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	:  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
									__a	:  Optional[Any]         =					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       ):
									__a	:  Tuple         =					LayoutLMvaImageProcessor()

									from datasets import load_dataset

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

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

									__a	:  str         =					image_processing(_A		,     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	:  List[str]         =					[['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	:  Union[str, 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		,     _A       )
									self.assertListEqual(encoding.boxes		,     _A       )

									# with apply_OCR = False
									__a	:  Union[str, Any]         =					LayoutLMvaImageProcessor(apply_ocr=_A       )

									__a	:  Optional[int]         =					image_processing(_A		,     return_tensors='''pt'''       )

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A	     =					{
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''InstructBlipQFormerModel''',
		    '''InstructBlipPreTrainedModel''',
		    '''InstructBlipForConditionalGeneration''',
		    '''InstructBlipVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    InstructBlipConfig,
		    InstructBlipQFormerConfig,
		    InstructBlipVisionConfig,
		)
		from .processing_instructblip import InstructBlipProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_instructblip import (
				    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    InstructBlipForConditionalGeneration,
				    InstructBlipPreTrainedModel,
				    InstructBlipQFormerModel,
				    InstructBlipVisionModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)