'''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 					__magic_name__		(					unittest.TestCase):







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







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class 					__magic_name__		(					_UpperCAmelCase,  unittest.TestCase):
			UpperCamelCase__       = LayoutLMvaImageProcessor if is_pytesseract_available() else None







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										lowercase_      :    Tuple       		= LayoutLMvaImageProcessingTester(self					)







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return self.image_processor_tester.prepare_image_processor_dict()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    str       		= self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowercase_       ,    """do_resize"""					)					)
										self.assertTrue(hasattr(lowercase_       ,    """size"""					)					)
										self.assertTrue(hasattr(lowercase_       ,    """apply_ocr"""					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										lowercase_      :    Union[str, Any]       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						Dict					):
										pass







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]					):
										# Initialize image_processing
										lowercase_      :    str       		= self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										lowercase_      :    int       		= prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase_					)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_       ,    Image.Image					)

										# Test not batched input
										lowercase_      :    Tuple       		= 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       ,    lowercase_					)
										self.assertIsInstance(encoding.boxes       ,    lowercase_					)

										# Test batched
										lowercase_      :    Dict       		= image_processing(lowercase_       ,    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 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]					):
										# Initialize image_processing
										lowercase_      :    Any       		= self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										lowercase_      :    int       		= prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase_       ,    numpify=lowercase_					)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_       ,    np.ndarray					)

										# Test not batched input
										lowercase_      :    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
										lowercase_      :    str       		= image_processing(lowercase_       ,    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 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										# Initialize image_processing
										lowercase_      :    Dict       		= self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										lowercase_      :    int       		= prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase_       ,    torchify=lowercase_					)
										for image in image_inputs:
																	self.assertIsInstance(lowercase_       ,    torch.Tensor					)

										# Test not batched input
										lowercase_      :    Union[str, 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_      :    Optional[Any]       		= image_processing(lowercase_       ,    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 			SCREAMING_SNAKE_CASE_  (			self       :						Any					):
										# with apply_OCR = True
										lowercase_      :    Tuple       		= LayoutLMvaImageProcessor()

										from datasets import load_dataset

										lowercase_      :    Any       		= load_dataset("""hf-internal-testing/fixtures_docvqa"""       ,    split="""test"""					)

										lowercase_      :    Tuple       		= Image.open(ds[0]["""file"""]					).convert("""RGB"""					)

										lowercase_      :    Union[str, Any]       		= image_processing(lowercase_       ,    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_      :    Tuple       		= [[[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       ,    lowercase_					)
										self.assertListEqual(encoding.boxes       ,    lowercase_					)

										# with apply_OCR = False
										lowercase_      :    List[Any]       		= LayoutLMvaImageProcessor(apply_ocr=lowercase_					)

										lowercase_      :    Any       		= image_processing(lowercase_       ,    return_tensors="""pt"""					)

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






'''simple docstring'''




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowercase		:				Optional[Any]	       =		argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not")
parser.add_argument("--steps", default=None, type=int, help="Num inference steps")
_lowercase		:				Dict	       =		parser.parse_args()


_lowercase		:				Dict	       =		"cpu"
_lowercase		:				str	       =		"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

_lowercase		:				Any	       =		"path-to-your-trained-model"
_lowercase		:				str	       =		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
						_lowercase		:				Optional[int]	       =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowercase		:				Any	       =		pipe.to(device)

# to channels last
_lowercase		:				Union[str, Any]	       =		pipe.unet.to(memory_format=torch.channels_last)
_lowercase		:				List[Any]	       =		pipe.vae.to(memory_format=torch.channels_last)
_lowercase		:				Union[str, Any]	       =		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
						_lowercase		:				Tuple	       =		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowercase		:				int	       =		torch.randn(2, 4, 64, 64)
_lowercase		:				int	       =		torch.rand(1) * 999
_lowercase		:				Union[str, Any]	       =		torch.randn(2, 77, 768)
_lowercase		:				Optional[Any]	       =		(sample, timestep, encoder_hidden_status)
try:
						_lowercase		:				int	       =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
						_lowercase		:				Optional[int]	       =		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase		:				List[Any]	       =		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase		:				str	       =		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
						_lowercase		:				int	       =		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowercase		:				int	       =		666
_lowercase		:				Any	       =		torch.Generator(device).manual_seed(seed)
_lowercase		:				int	       =		{"generator": generator}
if args.steps is not None:
						_lowercase		:				Optional[int]	       =		args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
						_lowercase		:				List[Any]	       =		pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("generated.png")





