



'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@property
						def     __snake_case   ( self				)     ->     Dict:
							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     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     self.dummy_uncond_unet
							lowerCAmelCase   =     PNDMScheduler()

							lowerCAmelCase   =     PNDMPipeline(unet=A_     ,	scheduler=A_				)
							pndm.to(A_				)
							pndm.set_progress_bar_config(disable=A_				)

							lowerCAmelCase   =     torch.manual_seed(0				)
							lowerCAmelCase   =     pndm(generator=A_     ,	num_inference_steps=20     ,	output_type="""numpy"""				).images

							lowerCAmelCase   =     torch.manual_seed(0				)
							lowerCAmelCase   =     pndm(generator=A_     ,	num_inference_steps=20     ,	output_type="""numpy"""     ,	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([1.0, 1.0, 0.0, 1.0, 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 __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     """google/ddpm-cifar10-32"""

							lowerCAmelCase   =     UNetaDModel.from_pretrained(A_				)
							lowerCAmelCase   =     PNDMScheduler()

							lowerCAmelCase   =     PNDMPipeline(unet=A_     ,	scheduler=A_				)
							pndm.to(A_				)
							pndm.set_progress_bar_config(disable=A_				)
							lowerCAmelCase   =     torch.manual_seed(0				)
							lowerCAmelCase   =     pndm(generator=A_     ,	output_type="""numpy"""				).images

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

							assert image.shape == (1, 32, 32, 3)
							lowerCAmelCase   =     np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]				)

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




'''simple docstring'''

import unittest

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


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)