





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 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




	def __init__(						self							:     int					,      __lowerCamelCase							:     str					,      __lowerCamelCase							:     str=7					,      __lowerCamelCase							:     int=3					,      __lowerCamelCase							:     List[Any]=18					,      __lowerCamelCase							:     Optional[Any]=30					,      __lowerCamelCase							:     int=400					,      __lowerCamelCase							:     Optional[int]=True					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     Union[str, Any]=True					,      )       -> str:

			'''simple docstring'''




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






	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Optional[Any]:

			'''simple docstring'''




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





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







	'''simple docstring'''
	__UpperCamelCase   =		LayoutLMvaImageProcessor if is_pytesseract_available() else None




	def    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     LayoutLMvaImageProcessingTester(self   )




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

			'''simple docstring'''




			return self.image_processor_tester.prepare_image_processor_dict()




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

			'''simple docstring'''




			__lowercase											=     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    UpperCAmelCase			(						self							:     Union[str, Any]   )       -> int:

			'''simple docstring'''




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

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




	def    UpperCAmelCase			(						self							:     Tuple   )       -> Dict:

			'''simple docstring'''




			pass




	def    UpperCAmelCase			(						self							:     str   )       -> Optional[int]:

			'''simple docstring'''




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

			# Test not batched input
			__lowercase											=     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
			__lowercase											=     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    UpperCAmelCase			(						self							:     List[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			__lowercase											=     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
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Tuple   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__lowercase											=     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
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     LayoutLMvaImageProcessor()

			from datasets import load_dataset

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

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

			__lowercase											=     image_processing(__lowerCamelCase					,      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											=     [['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											=     [[[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					,      __lowerCamelCase   )
			self.assertListEqual(encoding.boxes					,      __lowerCamelCase   )

			# with apply_OCR = False
			__lowercase											=     LayoutLMvaImageProcessor(apply_ocr=__lowerCamelCase   )

			__lowercase											=     image_processing(__lowerCamelCase					,      return_tensors='pt'   )

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











from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			super().__init__(*__lowerCamelCase					,      **__lowerCamelCase   )
			self.check_model_type(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

			if self.framework == "pt":
					__lowercase											=     model_outputs.logits.sigmoid()[0]
					__lowercase						,      __lowercase											=     probs.topk(__lowerCamelCase   )
			else:
					raise ValueError(F"Unsupported framework: {self.framework}"   )

			__lowercase											=     scores.tolist()
			__lowercase											=     ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase					,      __lowerCamelCase   )]




