



"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					UnCLIPImageVariationPipeline
			UpperCamelCase        =					IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
			UpperCamelCase        =					IMAGE_VARIATION_BATCH_PARAMS

			UpperCamelCase        =					[
			    '''generator''',
			    '''return_dict''',
			    '''decoder_num_inference_steps''',
			    '''super_res_num_inference_steps''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[Any]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   str:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return self.time_input_dim



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						return 1_00



			@property
			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
						return tokenizer



			@property
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPTextConfig(
						    bos_token_id=0			,		eos_token_id=2			,		hidden_size=self.text_embedder_hidden_size			,		projection_dim=self.text_embedder_hidden_size			,		intermediate_size=37			,		layer_norm_eps=1E-05			,		num_attention_heads=4			,		num_hidden_layers=5			,		pad_token_id=1			,		vocab_size=10_00			,		)
						return CLIPTextModelWithProjection(A)



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase			=       CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size			,		projection_dim=self.text_embedder_hidden_size			,		num_hidden_layers=5			,		num_attention_heads=4			,		image_size=32			,		intermediate_size=37			,		patch_size=1			,		)
						return CLIPVisionModelWithProjection(A)



			@property
			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'clip_embeddings_dim': self.text_embedder_hidden_size,
						    'time_embed_dim': self.time_embed_dim,
						    'cross_attention_dim': self.cross_attention_dim,
						}

						_UpperCAmelCase			=       UnCLIPTextProjModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Optional[Any]:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       {
						    'sample_size': 32,
						    # RGB in channels
						    'in_channels': 3,
						    # Out channels is double in channels because predicts mean and variance
						    'out_channels': 6,
						    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
						    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
						    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
						    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
						    'layers_per_block': 1,
						    'cross_attention_dim': self.cross_attention_dim,
						    'attention_head_dim': 4,
						    'resnet_time_scale_shift': 'scale_shift',
						    'class_embed_type': 'identity',
						}

						_UpperCAmelCase			=       UNetaDConditionModel(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""simple docstring"""






						return {
						    "sample_size": 64,
						    "layers_per_block": 1,
						    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
						    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
						    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    "in_channels": 6,
						    "out_channels": 3,
						}



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Any:



						"""simple docstring"""






						torch.manual_seed(0)

						_UpperCAmelCase			=       UNetaDModel(**self.dummy_super_res_kwargs)
						return model



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						torch.manual_seed(1)

						_UpperCAmelCase			=       UNetaDModel(**self.dummy_super_res_kwargs)
						return model



			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dummy_decoder
						_UpperCAmelCase			=       self.dummy_text_proj
						_UpperCAmelCase			=       self.dummy_text_encoder
						_UpperCAmelCase			=       self.dummy_tokenizer
						_UpperCAmelCase			=       self.dummy_super_res_first
						_UpperCAmelCase			=       self.dummy_super_res_last

						_UpperCAmelCase			=       UnCLIPScheduler(
						    variance_type='learned_range'			,		prediction_type='epsilon'			,		num_train_timesteps=10_00			,		)

						_UpperCAmelCase			=       UnCLIPScheduler(
						    variance_type='fixed_small_log'			,		prediction_type='epsilon'			,		num_train_timesteps=10_00			,		)

						_UpperCAmelCase			=       CLIPImageProcessor(crop_size=32			,		size=32)

						_UpperCAmelCase			=       self.dummy_image_encoder

						return {
						    "decoder": decoder,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "text_proj": text_proj,
						    "feature_extractor": feature_extractor,
						    "image_encoder": image_encoder,
						    "super_res_first": super_res_first,
						    "super_res_last": super_res_last,
						    "decoder_scheduler": decoder_scheduler,
						    "super_res_scheduler": super_res_scheduler,
						}



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  str			,		A       :  str=0			,		A       :  Optional[Any]=True)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 32, 32)			,		rng=random.Random(A)).to(A)
						if str(A).startswith('mps'):
									_UpperCAmelCase			=       torch.manual_seed(A)
						else:
									_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(A)

						if pil_image:
									_UpperCAmelCase			=       input_image * 0.5 + 0.5
									_UpperCAmelCase			=       input_image.clamp(0			,		1)
									_UpperCAmelCase			=       input_image.cpu().permute(0			,		2			,		3			,		1).float().numpy()
									_UpperCAmelCase			=       DiffusionPipeline.numpy_to_pil(A)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}



			def    _lowerCamelCase	(						self       :  Any)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

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

						assert image.shape == (1, 64, 64, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.9_9_9_7,
						        0.0_0_0_2,
						        0.9_9_9_7,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.0_0_2_3,
						        0.9_9_9_7,
						        0.9_9_6_9,
						        0.9_9_7_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



			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

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

						assert image.shape == (1, 64, 64, 3)

						_UpperCAmelCase			=       np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1])

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



			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						_UpperCAmelCase			=       [
						    pipeline_inputs['image'],
						    pipeline_inputs['image'],
						]

						_UpperCAmelCase			=       pipe(**A)
						_UpperCAmelCase			=       output.images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						_UpperCAmelCase			=       [
						    tuple_pipeline_inputs['image'],
						    tuple_pipeline_inputs['image'],
						]

						_UpperCAmelCase			=       pipe(
						    **A			,		return_dict=A			,		)[0]

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

						assert image.shape == (2, 64, 64, 3)

						_UpperCAmelCase			=       np.array(
						    [
						        0.9_9_9_7,
						        0.9_9_8_9,
						        0.0_0_0_8,
						        0.0_0_2_1,
						        0.9_9_6_0,
						        0.0_0_1_8,
						        0.0_0_1_4,
						        0.0_0_0_2,
						        0.9_9_3_3,
						    ])

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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       torch.device('cpu')







						class       __lowerCAmelCase   :
									UpperCamelCase        =					1





						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)
						_UpperCAmelCase			=       pipe.decoder.dtype
						_UpperCAmelCase			=       1

						_UpperCAmelCase			=       (
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						_UpperCAmelCase			=       pipe.prepare_latents(
						    A			,		dtype=A			,		device=A			,		generator=A			,		latents=A			,		scheduler=DummyScheduler())

						_UpperCAmelCase			=       (
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						_UpperCAmelCase			=       pipe.prepare_latents(
						    A			,		dtype=A			,		device=A			,		generator=A			,		latents=A			,		scheduler=DummyScheduler())

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)

						_UpperCAmelCase			=       pipe(
						    **A			,		decoder_latents=A			,		super_res_latents=A).images

						_UpperCAmelCase			=       self.get_dummy_inputs(A			,		pil_image=A)
						# Don't pass image, instead pass embedding
						_UpperCAmelCase			=       pipeline_inputs.pop('image')
						_UpperCAmelCase			=       pipe.image_encoder(A).image_embeds

						_UpperCAmelCase			=       pipe(
						    **A			,		decoder_latents=A			,		super_res_latents=A			,		image_embeddings=A			,		).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a).max() < 1E-4



			@skip_mps
			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						_UpperCAmelCase			=       1E-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=A			,		expected_max_diff=A)



			@skip_mps
			def    _lowerCamelCase	(						self       :  Dict)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						_UpperCAmelCase			=       [
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						self._test_inference_batch_single_identical(
						    test_max_difference=A			,		relax_max_difference=A			,		additional_params_copy_to_batched_inputs=A			,		)



			def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       [
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]

						if torch_device == "mps":
									# TODO: MPS errors with larger batch sizes
									_UpperCAmelCase			=       [2, 3]
									self._test_inference_batch_consistent(
									    batch_sizes=A			,		additional_params_copy_to_batched_inputs=A			,		)
						else:
									self._test_inference_batch_consistent(
									    additional_params_copy_to_batched_inputs=A)



			@skip_mps
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()



			@skip_mps
			def    _lowerCamelCase	(						self       :  Tuple)       ->   Tuple:



						"""simple docstring"""






						return super().test_save_load_local()



			@skip_mps
			def    _lowerCamelCase	(						self       :  int)       ->   int:



						"""simple docstring"""






						return super().test_save_load_optional_components()







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



			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
						_UpperCAmelCase			=       load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy')

						_UpperCAmelCase			=       UnCLIPImageVariationPipeline.from_pretrained(
						    'kakaobrain/karlo-v1-alpha-image-variations'			,		torch_dtype=torch.floataa)
						_UpperCAmelCase			=       pipeline.to(A)
						pipeline.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device='cpu').manual_seed(0)
						_UpperCAmelCase			=       pipeline(
						    A			,		generator=A			,		output_type='np'			,		)

						_UpperCAmelCase			=       output.images[0]

						assert image.shape == (2_56, 2_56, 3)

						assert_mean_pixel_difference(A			,		A			,		15)





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       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					ShapEImgaImgPipeline
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					['''image''']
			UpperCamelCase        =					[
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCamelCase        =					False



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return 32



			@property
			def    _lowerCamelCase	(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						return self.time_input_dim * 4



			@property
			def    _lowerCamelCase	(						self       :  List[str])       ->   List[Any]:



						"""simple docstring"""






						return 8



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						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(A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       CLIPImageProcessor(
						    crop_size=2_24			,		do_center_crop=A			,		do_normalize=A			,		do_resize=A			,		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=2_24			,		)

						return image_processor



			@property
			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						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(**A)
						return model



			@property
			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[str]:



						"""simple docstring"""






						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(**A)
						return model



			def    _lowerCamelCase	(						self       :  Any)       ->   Any:



						"""simple docstring"""






						_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=10_24			,		prediction_type='sample'			,		use_karras_sigmas=A			,		clip_sample=A			,		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       :  List[str]			,		A       :  Optional[Any]			,		A       :  Tuple=0)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       floats_tensor((1, 3, 64, 64)			,		rng=random.Random(A)).to(A)

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



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       'cpu'

						_UpperCAmelCase			=       self.get_dummy_components()

						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)

						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       pipe(**self.get_dummy_inputs(A))
						_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       :  int)       ->   Tuple:



						"""simple docstring"""






						self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       torch_device == 'cpu'
						_UpperCAmelCase			=       True
						self._test_inference_batch_single_identical(
						    batch_size=2			,		test_max_difference=A			,		relax_max_difference=A			,		)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.get_dummy_components()
						_UpperCAmelCase			=       self.pipeline_class(**A)
						_UpperCAmelCase			=       pipe.to(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       1
						_UpperCAmelCase			=       2

						_UpperCAmelCase			=       self.get_dummy_inputs(A)

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

						_UpperCAmelCase			=       pipe(**A			,		num_images_per_prompt=A)[0]

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







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



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



			def    _lowerCamelCase	(						self       :  Dict)       ->   List[str]:



						"""simple docstring"""






						_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(A)
						pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			=       torch.Generator(device=A).manual_seed(0)

						_UpperCAmelCase			=       pipe(
						    A			,		generator=A			,		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(A			,		A)
