"""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  UpperCamelCase					(				unittest.TestCase   ):







       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,							)      ->      List[str]:
            # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
            snake_case_					       =					size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					num_channels
            snake_case_					       =					min_resolution
            snake_case_					       =					max_resolution
            snake_case_					       =					do_resize
            snake_case_					       =					size
            snake_case_					       =					do_normalize
            snake_case_					       =					image_mean
            snake_case_					       =					image_std
            snake_case_					       =					do_rescale
            snake_case_					       =					rescale_factor
            snake_case_					       =					do_pad







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

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

            return expected_height, expected_width




@require_torch
@require_vision
class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      YolosImageProcessor if is_vision_available() else None







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					YolosImageProcessingTester(self)







       @property
       def 		a_						( self)      ->      List[str]:
            return self.image_processor_tester.prepare_image_processor_dict()







       def 		a_						( self)      ->      Dict:
            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)      ->      int:
            snake_case_					       =					self.image_processing_class.from_dict(self.image_processor_dict)
            self.assertEqual(image_processor.size,							{'shortest_edge': 18, 'longest_edge': 1333})
            self.assertEqual(image_processor.do_pad,							lowerCAmelCase__)

            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)      ->      List[str]:
            pass







       def 		a_						( self)      ->      Union[str, Any]:
            # Initialize image_processing
            snake_case_					       =					self.image_processing_class(**self.image_processor_dict)
            # create random PIL images
            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
            snake_case_					       =					image_processing(image_inputs[0],							return_tensors='pt').pixel_values

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

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

            snake_case_       ,	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
            snake_case_					       =					image_processing(lowerCAmelCase__,							return_tensors='pt').pixel_values

            snake_case_       ,	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
            snake_case_					       =					self.image_processing_class(**self.image_processor_dict)
            # create random PyTorch tensors
            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
            snake_case_					       =					image_processing(image_inputs[0],							return_tensors='pt').pixel_values

            snake_case_       ,	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
            snake_case_					       =					image_processing(lowerCAmelCase__,							return_tensors='pt').pixel_values

            snake_case_       ,	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
            snake_case_					       =					self.image_processing_class(**self.image_processor_dict)
            snake_case_					       =					self.image_processing_class(do_resize=lowerCAmelCase__,							do_normalize=lowerCAmelCase__,							do_rescale=lowerCAmelCase__)
            # create random PyTorch tensors
            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
            snake_case_					       =					image_processing_a.pad(lowerCAmelCase__,							return_tensors='pt')
            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)      ->      Union[str, Any]:
            # prepare image and target
            snake_case_					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
            with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt',							'r') as f:
                 snake_case_					       =					json.loads(f.read())

            snake_case_					       =					{'image_id': 3_9769, 'annotations': target}

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

            # verify pixel values
            snake_case_					       =					torch.Size([1, 3, 800, 1066])
            self.assertEqual(encoding['pixel_values'].shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor([0.2796, 0.3138, 0.3481])
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],							lowerCAmelCase__,							atol=1e-4))

            # verify area
            snake_case_					       =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
            self.assertTrue(torch.allclose(encoding['labels'][0]['area'],							lowerCAmelCase__))
            # verify boxes
            snake_case_					       =					torch.Size([6, 4])
            self.assertEqual(encoding['labels'][0]['boxes'].shape,							lowerCAmelCase__)
            snake_case_					       =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],							lowerCAmelCase__,							atol=1e-3))
            # verify image_id
            snake_case_					       =					torch.tensor([3_9769])
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],							lowerCAmelCase__))
            # verify is_crowd
            snake_case_					       =					torch.tensor([0, 0, 0, 0, 0, 0])
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],							lowerCAmelCase__))
            # verify class_labels
            snake_case_					       =					torch.tensor([75, 75, 63, 65, 17, 17])
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],							lowerCAmelCase__))
            # verify orig_size
            snake_case_					       =					torch.tensor([480, 640])
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],							lowerCAmelCase__))
            # verify size
            snake_case_					       =					torch.tensor([800, 1066])
            self.assertTrue(torch.allclose(encoding['labels'][0]['size'],							lowerCAmelCase__))







       @slow
       def 		a_						( self)      ->      List[Any]:
            # prepare image, target and masks_path
            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:
                 snake_case_					       =					json.loads(f.read())

            snake_case_					       =					{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

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

            # verify pixel values
            snake_case_					       =					torch.Size([1, 3, 800, 1066])
            self.assertEqual(encoding['pixel_values'].shape,							lowerCAmelCase__)

            snake_case_					       =					torch.tensor([0.2796, 0.3138, 0.3481])
            self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],							lowerCAmelCase__,							atol=1e-4))

            # verify area
            snake_case_					       =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
            self.assertTrue(torch.allclose(encoding['labels'][0]['area'],							lowerCAmelCase__))
            # verify boxes
            snake_case_					       =					torch.Size([6, 4])
            self.assertEqual(encoding['labels'][0]['boxes'].shape,							lowerCAmelCase__)
            snake_case_					       =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],							lowerCAmelCase__,							atol=1e-3))
            # verify image_id
            snake_case_					       =					torch.tensor([3_9769])
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],							lowerCAmelCase__))
            # verify is_crowd
            snake_case_					       =					torch.tensor([0, 0, 0, 0, 0, 0])
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],							lowerCAmelCase__))
            # verify class_labels
            snake_case_					       =					torch.tensor([17, 17, 63, 75, 75, 93])
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],							lowerCAmelCase__))
            # verify masks
            snake_case_					       =					82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item(),							lowerCAmelCase__)
            # verify orig_size
            snake_case_					       =					torch.tensor([480, 640])
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],							lowerCAmelCase__))
            # verify size
            snake_case_					       =					torch.tensor([800, 1066])
            self.assertTrue(torch.allclose(encoding['labels'][0]['size'],							lowerCAmelCase__))


"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = ShapEImgaImgPipeline
					snake_case__				    = ["image"]
					snake_case__				    = ["image"]
					snake_case__				    = [
					    "num_images_per_prompt",
					    "num_inference_steps",
					    "generator",
					    "latents",
					    "guidance_scale",
					    "frame_size",
					    "output_type",
					    "return_dict",
					]
					snake_case__				    = False







					@property
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									return 32







					@property
					def 		__lowerCAmelCase						(				self   :	int			):
									return self.time_input_dim * 4







					@property
					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									return 8







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									torch.manual_seed(0			)
									UpperCAmelCase__             =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size    ,image_size=64    ,projection_dim=self.text_embedder_hidden_size    ,intermediate_size=37    ,num_attention_heads=4    ,num_channels=3    ,num_hidden_layers=5    ,patch_size=1    ,)

									UpperCAmelCase__             =							CLIPVisionModel(lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							CLIPImageProcessor(
									    crop_size=224    ,do_center_crop=lowerCamelCase__    ,do_normalize=lowerCamelCase__    ,do_resize=lowerCamelCase__    ,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,resample=3    ,size=224    ,)

									return image_processor







					@property
					def 		__lowerCAmelCase						(				self   :	str			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'embedding_proj_norm_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									UpperCAmelCase__             =							PriorTransformer(**lowerCamelCase__			)
									return model







					@property
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									torch.manual_seed(0			)

									UpperCAmelCase__             =							{
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									UpperCAmelCase__             =							ShapERenderer(**lowerCamelCase__			)
									return model







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							self.dummy_prior
									UpperCAmelCase__             =							self.dummy_image_encoder
									UpperCAmelCase__             =							self.dummy_image_processor
									UpperCAmelCase__             =							self.dummy_renderer

									UpperCAmelCase__             =							HeunDiscreteScheduler(
									    beta_schedule='exp'    ,num_train_timesteps=1_024    ,prediction_type='sample'    ,use_karras_sigmas=lowerCamelCase__    ,clip_sample=lowerCamelCase__    ,clip_sample_range=1.0    ,)
									UpperCAmelCase__             =							{
									    'prior': prior,
									    'image_encoder': image_encoder,
									    'image_processor': image_processor,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	str=0			):
									UpperCAmelCase__             =							floats_tensor((1, 3, 64, 64)    ,rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)

									if str(lowerCamelCase__			).startswith('mps'			):
													UpperCAmelCase__             =							torch.manual_seed(lowerCamelCase__			)
									else:
													UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
									UpperCAmelCase__             =							{
									    'image': input_image,
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							'cpu'

									UpperCAmelCase__             =							self.get_dummy_components()

									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)

									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
									UpperCAmelCase__             =							output.images[0]
									UpperCAmelCase__             =							image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									UpperCAmelCase__             =							np.array(
									    [
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									        0.0_0_0_3_9_2_1_6,
									    ]			)

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







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							torch_device == 'cpu'
									UpperCAmelCase__             =							True
									self._test_inference_batch_single_identical(
									    batch_size=2    ,test_max_difference=lowerCamelCase__    ,relax_max_difference=lowerCamelCase__    ,)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							self.get_dummy_components()
									UpperCAmelCase__             =							self.pipeline_class(**lowerCamelCase__			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							1
									UpperCAmelCase__             =							2

									UpperCAmelCase__             =							self.get_dummy_inputs(lowerCamelCase__			)

									for key in inputs.keys():
													if key in self.batch_params:
																	UpperCAmelCase__             =							batch_size * [inputs[key]]

									UpperCAmelCase__             =							pipe(**lowerCamelCase__    ,num_images_per_prompt=lowerCamelCase__			)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'			)
									UpperCAmelCase__             =							load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_img2img_out.npy'			)
									UpperCAmelCase__             =							ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'			)
									UpperCAmelCase__             =							pipe.to(lowerCamelCase__			)
									pipe.set_progress_bar_config(disable=lowerCamelCase__			)

									UpperCAmelCase__             =							torch.Generator(device=lowerCamelCase__			).manual_seed(0			)

									UpperCAmelCase__             =							pipe(
									    lowerCamelCase__    ,generator=lowerCamelCase__    ,guidance_scale=3.0    ,num_inference_steps=64    ,frame_size=64    ,output_type='np'    ,).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,lowerCamelCase__			)



