

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 			a					(		unittest.TestCase			):


						def __init__(    self  ,   A_  ,   A_=7  ,   A_=3  ,   A_=18  ,   A_=30  ,   A_=400  ,   A_=True  ,   A_=None  ,   A_=True  ,   ):







										'''simple docstring'''


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





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







@require_torch
@require_pytesseract
class 			a					(		snake_case_							,	unittest.TestCase			):
						_lowercase        =     LayoutLMvaImageProcessor if is_pytesseract_available() else None


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      LayoutLMvaImageProcessingTester(self		)


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self.image_processor_tester.prepare_image_processor_dict()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.image_processing_class.from_dict(self.image_processor_dict		)
										self.assertEqual(image_processor.size  ,   {"height": 18, "width": 18}		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PIL images
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.image_processing_class(**self.image_processor_dict		)
										# create random numpy tensors
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      self.image_processing_class(**self.image_processor_dict		)
										# create random PyTorch tensors
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      LayoutLMvaImageProcessor()

										from datasets import load_dataset

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

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

										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						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
										_UpperCAmelCase    :						Union[str, Any]							=      LayoutLMvaImageProcessor(apply_ocr=_A		)

										_UpperCAmelCase    :						Optional[int]							=      image_processing(_A  ,   return_tensors="pt"		)

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





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (PNDMScheduler,)
						_lowercase        =     (("num_inference_steps", 5_0),)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						Optional[Any]							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						Dict							=      0.1 * sample
										_UpperCAmelCase    :						Optional[int]							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						Any							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						str							=      scheduler_class.from_pretrained(A_		)
																		new_scheduler.set_timesteps(A_		)
																		# copy over dummy past residuals
																		_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Any							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_UpperCAmelCase    :						Union[str, Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      dict(self.forward_default_kwargs		)
										_UpperCAmelCase    :						int							=      kwargs.pop("num_inference_steps"  ,   A_		)
										_UpperCAmelCase    :						int							=      self.dummy_sample
										_UpperCAmelCase    :						Any							=      0.1 * sample
										_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Optional[Any]							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Dict							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)

														# copy over dummy past residuals (must be after setting timesteps)
														_UpperCAmelCase    :						Any							=      dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(A_		)
																		_UpperCAmelCase    :						str							=      scheduler_class.from_pretrained(A_		)
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(A_		)

																		# copy over dummy past residual (must be after setting timesteps)
																		_UpperCAmelCase    :						List[Any]							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Union[str, Any]							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"

														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						List[str]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

														assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Union[str, Any]							=      self.get_scheduler_config(**A_		)
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						Optional[int]							=      self.dummy_model()
										_UpperCAmelCase    :						List[Any]							=      self.dummy_sample_deter
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.prk_timesteps		):
														_UpperCAmelCase    :						Dict							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						int							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps		):
														_UpperCAmelCase    :						Any							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						Any							=      scheduler.step_plms(A_  ,   A_  ,   A_		).prev_sample

										return sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      dict(self.forward_default_kwargs		)

										_UpperCAmelCase    :						str							=      kwargs.pop("num_inference_steps"  ,   A_		)

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						int							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Optional[Any]							=      scheduler_class(**A_		)

														_UpperCAmelCase    :						int							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														if num_inference_steps is not None and hasattr(A_  ,   "set_timesteps"		):
																		scheduler.set_timesteps(A_		)
														elif num_inference_steps is not None and not hasattr(A_  ,   "set_timesteps"		):
																		_UpperCAmelCase    :						Union[str, Any]							=      num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_UpperCAmelCase    :						Tuple							=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														_UpperCAmelCase    :						Any							=      dummy_past_residuals[:]

														_UpperCAmelCase    :						Any							=      scheduler.step_prk(A_  ,   0  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_prk(A_  ,   1  ,   A_  ,   **A_		).prev_sample

														self.assertEqual(output_a.shape  ,   sample.shape		)
														self.assertEqual(output_a.shape  ,   output_a.shape		)

														_UpperCAmelCase    :						Optional[int]							=      scheduler.step_plms(A_  ,   0  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_plms(A_  ,   1  ,   A_  ,   **A_		).prev_sample

														self.assertEqual(output_a.shape  ,   sample.shape		)
														self.assertEqual(output_a.shape  ,   output_a.shape		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for timesteps in [100, 1000]:
														self.check_over_configs(num_train_timesteps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=A_		)

										_UpperCAmelCase    :						List[str]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(steps_offset=1		)
										_UpperCAmelCase    :						str							=      scheduler_class(**A_		)
										scheduler.set_timesteps(10		)
										assert torch.equal(
										    scheduler.timesteps  ,   torch.LongTensor(
										        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]		)  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.00_01, 0.0_01]  ,   [0.0_02, 0.02]		):
														self.check_over_configs(beta_start=A_  ,   beta_end=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for t in [1, 5, 10]:
														self.check_over_forward(time_step=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for t, num_inference_steps in zip([1, 5, 10]  ,   [10, 50, 100]		):
														self.check_over_forward(num_inference_steps=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      27

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						str							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														_UpperCAmelCase    :						str							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Tuple							=      scheduler_class(**A_		)

														scheduler.set_timesteps(A_		)

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2]		):
																		_UpperCAmelCase    :						Dict							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with self.assertRaises(A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      self.scheduler_classes[0]
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Union[str, Any]							=      scheduler_class(**A_		)

														scheduler.step_plms(self.dummy_sample  ,   1  ,   self.dummy_sample		).prev_sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.full_loop()
										_UpperCAmelCase    :						int							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 1_98.13_18		) < 1e-2
										assert abs(result_mean.item() - 0.25_80		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.full_loop(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						List[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 67.39_86		) < 1e-2
										assert abs(result_mean.item() - 0.08_78		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.full_loop(set_alpha_to_one=A_  ,   beta_start=0.01		)
										_UpperCAmelCase    :						Dict							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[int]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 2_30.03_99		) < 1e-2
										assert abs(result_mean.item() - 0.29_95		) < 1e-3





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.full_loop(set_alpha_to_one=A_  ,   beta_start=0.01		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Dict							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 1_86.94_82		) < 1e-2
										assert abs(result_mean.item() - 0.24_34		) < 1e-3


