






"""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      UpperCamelCase		(unittest.TestCase     ):






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






						def      __snake_case      (		self  :int				)      ->Union[str, Any]:
										return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}


@require_torch
@require_pytesseract
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		LayoutLMvaImageProcessor if is_pytesseract_available() else None






						def      __snake_case      (		self  :List[str]				)      ->Optional[Any]:
										lowercase    :      Union[str, Any]       = LayoutLMvaImageProcessingTester(self				)






						@property
						def      __snake_case      (		self  :Union[str, Any]				)      ->int:
										return self.image_processor_tester.prepare_image_processor_dict()






						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase    :      int       = self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__magic_name__			, """do_resize"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """size"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """apply_ocr"""				)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->str:
										lowercase    :      List[str]       = self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size			, {"""height""": 18, """width""": 18}				)

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






						def      __snake_case      (		self  :str				)      ->str:
										pass






						def      __snake_case      (		self  :Optional[int]				)      ->Dict:
										# Initialize image_processing
										lowercase    :      Optional[Any]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										lowercase    :      Union[str, Any]       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, Image.Image				)

										# Test not batched input
										lowercase    :      Dict       = 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			, __magic_name__				)
										self.assertIsInstance(encoding.boxes			, __magic_name__				)

										# Test batched
										lowercase    :      int       = image_processing(__magic_name__			, 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      __snake_case      (		self  :List[str]				)      ->Any:
										# Initialize image_processing
										lowercase    :      Union[str, Any]       = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										lowercase    :      Tuple       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, numpify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, np.ndarray				)

										# Test not batched input
										lowercase    :      List[str]       = 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(__magic_name__			, 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      __snake_case      (		self  :List[Any]				)      ->Tuple:
										# Initialize image_processing
										lowercase    :      Optional[Any]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										lowercase    :      List[Any]       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, torchify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, torch.Tensor				)

										# Test not batched input
										lowercase    :      str       = 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[str]       = image_processing(__magic_name__			, 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      __snake_case      (		self  :Tuple				)      ->Any:
										# with apply_OCR = True
										lowercase    :      Dict       = LayoutLMvaImageProcessor()

										from datasets import load_dataset

										lowercase    :      Dict       = load_dataset("""hf-internal-testing/fixtures_docvqa"""			, split="""test"""				)

										lowercase    :      Union[str, Any]       = Image.open(ds[0]["""file"""]				).convert("""RGB"""				)

										lowercase    :      Tuple       = image_processing(__magic_name__			, 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    :      Any       = [["""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    :      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			, __magic_name__				)
										self.assertListEqual(encoding.boxes			, __magic_name__				)

										# with apply_OCR = False
										lowercase    :      List[str]       = LayoutLMvaImageProcessor(apply_ocr=__magic_name__				)

										lowercase    :      int       = image_processing(__magic_name__			, return_tensors="""pt"""				)

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








"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

										self.register_modules(unet=__magic_name__			, scheduler=__magic_name__				)






						@torch.no_grad()
						def __call__(		self  :Optional[int]			, __magic_name__  :int = 1			, __magic_name__  :Optional[Union[torch.Generator, List[torch.Generator]]] = None			, __magic_name__  :float = 0.0			, __magic_name__  :int = 50			, __magic_name__  :Optional[bool] = None			, __magic_name__  :Optional[str] = "pil"			, __magic_name__  :bool = True			, )      ->Union[ImagePipelineOutput, Tuple]:

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

										if isinstance(__magic_name__			, __magic_name__				) and len(__magic_name__				) != batch_size:
														raise ValueError(
														    f"""You have passed a list of generators of length {len(__magic_name__				)}, but requested an effective batch"""
														    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

										# set step values
										self.scheduler.set_timesteps(__magic_name__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
