




"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 PoolFormerImageProcessor






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=7	,				_a=3	,				_a=30	,				_a=400	,				_a=True	,				_a=None	,				_a=0.9	,				_a=None	,				_a=True	,				_a=[0.5, 0.5, 0.5]	,				_a=[0.5, 0.5, 0.5]	,				):





							"""simple docstring"""
							lowerCamelCase       =      size if size is not None else {"""shortest_edge""": 30}
							lowerCamelCase       =      crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
							lowerCamelCase       =      parent
							lowerCamelCase       =      batch_size
							lowerCamelCase       =      num_channels
							lowerCamelCase       =      min_resolution
							lowerCamelCase       =      max_resolution
							lowerCamelCase       =      do_resize_and_center_crop
							lowerCamelCase       =      size
							lowerCamelCase       =      crop_pct
							lowerCamelCase       =      crop_size
							lowerCamelCase       =      do_normalize
							lowerCamelCase       =      image_mean
							lowerCamelCase       =      image_std





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}






@require_torch
@require_vision
class 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		PoolFormerImageProcessor if is_vision_available() else None





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      PoolFormerImageProcessingTester(self				)





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.image_processor_tester.prepare_image_processor_dict()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(_a	,				"""do_resize_and_center_crop"""				)				)
							self.assertTrue(hasattr(_a	,				"""size"""				)				)
							self.assertTrue(hasattr(_a	,				"""crop_pct"""				)				)
							self.assertTrue(hasattr(_a	,				"""do_normalize"""				)				)
							self.assertTrue(hasattr(_a	,				"""image_mean"""				)				)
							self.assertTrue(hasattr(_a	,				"""image_std"""				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size	,				{"""shortest_edge""": 30}				)
							self.assertEqual(image_processor.crop_size	,				{"""height""": 30, """width""": 30}				)

							lowerCamelCase       =      self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84				)
							self.assertEqual(image_processor.size	,				{"""shortest_edge""": 42}				)
							self.assertEqual(image_processor.crop_size	,				{"""height""": 84, """width""": 84}				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCamelCase       =      prepare_image_inputs(self.image_processor_tester	,				equal_resolution=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				Image.Image				)

							# Test not batched input
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCamelCase       =      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
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processing
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCamelCase       =      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
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    )	,				)









"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							torch.manual_seed(0				)
							lowerCamelCase       =      UNetaDModel(
							    block_out_channels=(32, 64)	,				layers_per_block=2	,				sample_size=32	,				in_channels=3	,				out_channels=3	,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,				)
							return model





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.dummy_uncond_unet
							lowerCamelCase       =      ScoreSdeVeScheduler()

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=2	,				output_type="""numpy"""	,				generator=_a	,				return_dict=_a				)[
							    0
							]

							lowerCamelCase       =      image[0, -3:, -3:, -1]
							lowerCamelCase       =      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowerCamelCase       =      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1e-2






@slow
@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      """google/ncsnpp-church-256"""
							lowerCamelCase       =      UNetaDModel.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVeScheduler.from_pretrained(_a				)

							lowerCamelCase       =      ScoreSdeVePipeline(unet=_a	,				scheduler=_a				)
							sde_ve.to(_a				)
							sde_ve.set_progress_bar_config(disable=_a				)

							lowerCamelCase       =      torch.manual_seed(0				)
							lowerCamelCase       =      sde_ve(num_inference_steps=10	,				output_type="""numpy"""	,				generator=_a				).images

							lowerCamelCase       =      image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							lowerCamelCase       =      np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]				)

							assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2



