


"""simple docstring"""




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import YolosImageProcessor
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=7						,       lowerCAmelCase__=3						,       lowerCAmelCase__=30						,       lowerCAmelCase__=400						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       lowerCAmelCase__=[0.5, 0.5, 0.5]						,       lowerCAmelCase__=[0.5, 0.5, 0.5]						,       lowerCAmelCase__=True						,       lowerCAmelCase__=1 / 255						,       lowerCAmelCase__=True						,       )     ->							Dict:
								# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize
								SCREAMING_SNAKE_CASE													=					image_mean
								SCREAMING_SNAKE_CASE													=					image_std
								SCREAMING_SNAKE_CASE													=					do_rescale
								SCREAMING_SNAKE_CASE													=					rescale_factor
								SCREAMING_SNAKE_CASE													=					do_pad







		def 							__A						(	self				)     ->							List[str]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_pad": self.do_pad,
								}







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=False				)     ->							Dict:
								if not batched:
														SCREAMING_SNAKE_CASE													=					image_inputs[0]
														if isinstance(lowerCAmelCase__						,       Image.Image				):
																				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					image.size
														else:
																				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					image.shape[1], image.shape[2]
														if w < h:
																				SCREAMING_SNAKE_CASE													=					int(self.size['shortest_edge'] * h / w				)
																				SCREAMING_SNAKE_CASE													=					self.size['shortest_edge']
														elif w > h:
																				SCREAMING_SNAKE_CASE													=					self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE													=					int(self.size['shortest_edge'] * w / h				)
														else:
																				SCREAMING_SNAKE_CASE													=					self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE													=					self.size['shortest_edge']

								else:
														SCREAMING_SNAKE_CASE													=					[]
														for image in image_inputs:
																				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.get_expected_values([image]				)
																				expected_values.append((expected_height, expected_width)				)
														SCREAMING_SNAKE_CASE													=					max(lowerCAmelCase__						,       key=lambda lowerCAmelCase__				: item[0]				)[0]
														SCREAMING_SNAKE_CASE													=					max(lowerCAmelCase__						,       key=lambda lowerCAmelCase__				: item[1]				)[1]

								return expected_height, expected_width





@require_torch
@require_vision
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     YolosImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					YolosImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'image_mean'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'image_std'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_normalize'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_resize'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'size'				)				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'shortest_edge': 18, 'longest_edge': 1_333}				)
								self.assertEqual(image_processor.do_pad						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(
								    self.image_processor_dict						,       size=42						,       max_size=84						,       pad_and_return_pixel_mask=lowerCAmelCase__				)
								self.assertEqual(image_processor.size						,       {'shortest_edge': 42, 'longest_edge': 84}				)
								self.assertEqual(image_processor.do_pad						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								pass







		def 							__A						(	self				)     ->							Optional[int]:
								# Initialize image_processing
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								SCREAMING_SNAKE_CASE													=					prepare_image_inputs(self.image_processor_tester						,       equal_resolution=lowerCAmelCase__				)
								for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase__						,       Image.Image				)

								# Test not batched input
								SCREAMING_SNAKE_CASE													=					image_processing(image_inputs[0]						,       return_tensors='pt'				).pixel_values

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

								self.assertEqual(
								    encoded_images.shape						,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,       )

								# Test batched
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__						,       batched=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					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,
								        expected_height,
								        expected_width,
								    )						,       )







		def 							__A						(	self				)     ->							str:
								# Initialize image_processing
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								SCREAMING_SNAKE_CASE													=					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
								SCREAMING_SNAKE_CASE													=					image_processing(image_inputs[0]						,       return_tensors='pt'				).pixel_values

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

								self.assertEqual(
								    encoded_images.shape						,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,       )

								# Test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				).pixel_values

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__						,       batched=lowerCAmelCase__				)

								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )						,       )







		def 							__A						(	self				)     ->							Union[str, Any]:
								# Initialize image_processing
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE													=					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
								SCREAMING_SNAKE_CASE													=					image_processing(image_inputs[0]						,       return_tensors='pt'				).pixel_values

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__				)

								self.assertEqual(
								    encoded_images.shape						,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,       )

								# Test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				).pixel_values

								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.image_processor_tester.get_expected_values(lowerCAmelCase__						,       batched=lowerCAmelCase__				)

								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    )						,       )







		def 							__A						(	self				)     ->							Optional[int]:
								# Initialize image_processings
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(do_resize=lowerCAmelCase__						,       do_normalize=lowerCAmelCase__						,       do_rescale=lowerCAmelCase__				)
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE													=					prepare_image_inputs(self.image_processor_tester						,       equal_resolution=lowerCAmelCase__						,       torchify=lowerCAmelCase__				)
								for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase__						,       torch.Tensor				)

								# Test whether the method "pad" and calling the image processor return the same tensors
								SCREAMING_SNAKE_CASE													=					image_processing_a.pad(lowerCAmelCase__						,       return_tensors='pt'				)
								SCREAMING_SNAKE_CASE													=					image_processing_a(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertTrue(
								    torch.allclose(encoded_images_with_method['pixel_values']						,       encoded_images['pixel_values']						,       atol=1e-4				)				)







		@slow
		def 							__A						(	self				)     ->							Optional[Any]:
								# prepare image and target
								SCREAMING_SNAKE_CASE													=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
								with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'						,       'r'				) as f:
														SCREAMING_SNAKE_CASE													=					json.loads(f.read()				)

								SCREAMING_SNAKE_CASE													=					{'image_id': 39_769, 'annotations': target}

								# encode them
								SCREAMING_SNAKE_CASE													=					YolosImageProcessor.from_pretrained('hustvl/yolos-small'				)
								SCREAMING_SNAKE_CASE													=					image_processing(images=lowerCAmelCase__						,       annotations=lowerCAmelCase__						,       return_tensors='pt'				)

								# verify pixel values
								SCREAMING_SNAKE_CASE													=					torch.Size([1, 3, 800, 1_066]				)
								self.assertEqual(encoding['pixel_values'].shape						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,       lowerCAmelCase__						,       atol=1e-4				)				)

								# verify area
								SCREAMING_SNAKE_CASE													=					torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,       lowerCAmelCase__				)				)
								# verify boxes
								SCREAMING_SNAKE_CASE													=					torch.Size([6, 4]				)
								self.assertEqual(encoding['labels'][0]['boxes'].shape						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,       lowerCAmelCase__						,       atol=1e-3				)				)
								# verify image_id
								SCREAMING_SNAKE_CASE													=					torch.tensor([39_769]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,       lowerCAmelCase__				)				)
								# verify is_crowd
								SCREAMING_SNAKE_CASE													=					torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,       lowerCAmelCase__				)				)
								# verify class_labels
								SCREAMING_SNAKE_CASE													=					torch.tensor([75, 75, 63, 65, 17, 17]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,       lowerCAmelCase__				)				)
								# verify orig_size
								SCREAMING_SNAKE_CASE													=					torch.tensor([480, 640]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,       lowerCAmelCase__				)				)
								# verify size
								SCREAMING_SNAKE_CASE													=					torch.tensor([800, 1_066]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,       lowerCAmelCase__				)				)







		@slow
		def 							__A						(	self				)     ->							Tuple:
								# prepare image, target and masks_path
								SCREAMING_SNAKE_CASE													=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
								with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'						,       'r'				) as f:
														SCREAMING_SNAKE_CASE													=					json.loads(f.read()				)

								SCREAMING_SNAKE_CASE													=					{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

								SCREAMING_SNAKE_CASE													=					pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

								# encode them
								SCREAMING_SNAKE_CASE													=					YolosImageProcessor(format='coco_panoptic'				)
								SCREAMING_SNAKE_CASE													=					image_processing(images=lowerCAmelCase__						,       annotations=lowerCAmelCase__						,       masks_path=lowerCAmelCase__						,       return_tensors='pt'				)

								# verify pixel values
								SCREAMING_SNAKE_CASE													=					torch.Size([1, 3, 800, 1_066]				)
								self.assertEqual(encoding['pixel_values'].shape						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
								self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]						,       lowerCAmelCase__						,       atol=1e-4				)				)

								# verify area
								SCREAMING_SNAKE_CASE													=					torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['area']						,       lowerCAmelCase__				)				)
								# verify boxes
								SCREAMING_SNAKE_CASE													=					torch.Size([6, 4]				)
								self.assertEqual(encoding['labels'][0]['boxes'].shape						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]						,       lowerCAmelCase__						,       atol=1e-3				)				)
								# verify image_id
								SCREAMING_SNAKE_CASE													=					torch.tensor([39_769]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']						,       lowerCAmelCase__				)				)
								# verify is_crowd
								SCREAMING_SNAKE_CASE													=					torch.tensor([0, 0, 0, 0, 0, 0]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']						,       lowerCAmelCase__				)				)
								# verify class_labels
								SCREAMING_SNAKE_CASE													=					torch.tensor([17, 17, 63, 75, 75, 93]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']						,       lowerCAmelCase__				)				)
								# verify masks
								SCREAMING_SNAKE_CASE													=					822_873
								self.assertEqual(encoding['labels'][0]['masks'].sum().item()						,       lowerCAmelCase__				)
								# verify orig_size
								SCREAMING_SNAKE_CASE													=					torch.tensor([480, 640]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']						,       lowerCAmelCase__				)				)
								# verify size
								SCREAMING_SNAKE_CASE													=					torch.tensor([800, 1_066]				)
								self.assertTrue(torch.allclose(encoding['labels'][0]['size']						,       lowerCAmelCase__				)				)






"""simple docstring"""




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__UpperCamelCase						=      HfApi()

__UpperCamelCase						=      {}
# fmt: off
__UpperCamelCase						=      torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
__UpperCamelCase						=      torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
__UpperCamelCase						=      torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
__UpperCamelCase						=      torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
__UpperCamelCase						=      torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
__UpperCamelCase						=      torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
__UpperCamelCase						=      torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
__UpperCamelCase						=      torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
__UpperCamelCase						=      torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
__UpperCamelCase						=      torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
__UpperCamelCase						=      torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
__UpperCamelCase						=      torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
__UpperCamelCase						=      torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
__UpperCamelCase						=      torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
__UpperCamelCase						=      torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

__UpperCamelCase						=      api.list_models(filter='''diffusers''')
for mod in models:
	if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
		__UpperCamelCase						=      '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]

		print(f'''Started running {mod.modelId}!!!''')

		if mod.modelId.startswith('''CompVis'''):
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
		else:
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint)

		torch.manual_seed(0)
		random.seed(0)

		__UpperCamelCase						=      torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
		__UpperCamelCase						=      torch.tensor([10] * noise.shape[0])
		with torch.no_grad():
			__UpperCamelCase						=      model(noise, time_step).sample

		assert torch.allclose(
		    logits[0, 0, 0, :30], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1E-3
		)
		print(f'''{mod.modelId} has passed successfully!!!''')


