






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				:	str		,     __a				:	List[Any]		,     __a				:	Union[str, Any]=7		,     __a				:	Optional[Any]=3		,     __a				:	Optional[int]=18		,     __a				:	Tuple=30		,     __a				:	str=400		,     __a				:	int=True		,     __a				:	str=None		,     __a				:	Union[str, Any]=True		,     )			->  Dict:


												"""simple docstring"""

												__lowercase      :							str				=     size if size is not None else {"""height""": 18, """width""": 18}
												__lowercase      :							List[Any]				=     parent
												__lowercase      :							Any				=     batch_size
												__lowercase      :							Any				=     num_channels
												__lowercase      :							Dict				=     image_size
												__lowercase      :							int				=     min_resolution
												__lowercase      :							Union[str, Any]				=     max_resolution
												__lowercase      :							Dict				=     do_resize
												__lowercase      :							str				=     size
												__lowercase      :							Tuple				=     apply_ocr



					def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[Any]:


												"""simple docstring"""

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







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

					'''simple docstring'''

					_A						:			Dict									=      LayoutLMvaImageProcessor if is_pytesseract_available() else None



					def       lowerCAmelCase				(  self				:	List[str]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     LayoutLMvaImageProcessingTester(self							)



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


												"""simple docstring"""

												return self.image_processor_tester.prepare_image_processor_dict()



					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[str]				=     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				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

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

												__lowercase      :							Union[str, 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				:	Union[str, Any]							)			->  Optional[Any]:


												"""simple docstring"""

												pass



					def       lowerCAmelCase				(  self				:	Dict							)			->  Optional[int]:


												"""simple docstring"""

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

												# Test not batched input
												__lowercase      :							Union[str, Any]				=     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
												__lowercase      :							List[str]				=     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				:	Optional[Any]							)			->  Tuple:


												"""simple docstring"""

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


												"""simple docstring"""

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


												"""simple docstring"""

												__lowercase      :							Dict				=     LayoutLMvaImageProcessor()

												from datasets import load_dataset

												__lowercase      :							List[str]				=     load_dataset("""hf-internal-testing/fixtures_docvqa"""		,     split="""test"""							)

												__lowercase      :							Optional[int]				=     Image.open(ds[0]["""file"""]							).convert("""RGB"""							)

												__lowercase      :							Tuple				=     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
												__lowercase      :							Tuple				=     [["""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      :							List[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
												__lowercase      :							List[str]				=     LayoutLMvaImageProcessor(apply_ocr=__a							)

												__lowercase      :							Union[str, Any]				=     image_processing(__a		,     return_tensors="""pt"""							)

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







from argparse import ArgumentParser

from . import BaseTransformersCLICommand
def       snake_case_		(     lowerCAmelCase_       :						Dict      ):
							return DownloadCommand(args.model       ,       args.cache_dir       ,       args.force       ,       args.trust_remote_code      )



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					@staticmethod
					def       lowerCAmelCase				(  __a				:	ArgumentParser							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     parser.add_parser("""download"""							)
												download_parser.add_argument(
												    """--cache-dir"""		,     type=__a		,     default=__a		,     help="""Path to location to store the models"""							)
												download_parser.add_argument(
												    """--force"""		,     action="""store_true"""		,     help="""Force the model to be download even if already in cache-dir"""							)
												download_parser.add_argument(
												    """--trust-remote-code"""		,     action="""store_true"""		,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""		,     )
												download_parser.add_argument("""model"""		,     type=__a		,     help="""Name of the model to download"""							)
												download_parser.set_defaults(func=__a							)



					def __init__(  self				:	Dict		,     __a				:	str		,     __a				:	str		,     __a				:	bool		,     __a				:	bool							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     model
												__lowercase      :							List[Any]				=     cache
												__lowercase      :							Any				=     force
												__lowercase      :							Optional[int]				=     trust_remote_code



					def       lowerCAmelCase				(  self				:	str							)			->  List[str]:


												"""simple docstring"""

												from ..models.auto import AutoModel, AutoTokenizer

												AutoModel.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)
												AutoTokenizer.from_pretrained(
												    self._model		,     cache_dir=self._cache		,     force_download=self._force		,     trust_remote_code=self._trust_remote_code							)