




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       a__	( UpperCamelCase__       , unittest.TestCase				):
					a		:      Union[str, Any]		 =     ShapEImgaImgPipeline
					a		:      Any		 =     ["""image"""]
					a		:      Optional[int]		 =     ["""image"""]
					a		:      Tuple		 =     [
					    """num_images_per_prompt""",
					    """num_inference_steps""",
					    """generator""",
					    """latents""",
					    """guidance_scale""",
					    """frame_size""",
					    """output_type""",
					    """return_dict""",
					]
					a		:      List[Any]		 =     False







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



										'''simple docstring'''



										return 32







					@property
					def       lowerCAmelCase_    (     self			)  ->   int:



										'''simple docstring'''



										return 32







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



										'''simple docstring'''



										return self.time_input_dim * 4







					@property
					def       lowerCAmelCase_    (     self			)  ->   Any:



										'''simple docstring'''



										return 8







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



										'''simple docstring'''



										torch.manual_seed(0			)
										a     =				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  ,		)

										a     =				CLIPVisionModel(A			)
										return model







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



										'''simple docstring'''



										a     =				CLIPImageProcessor(
										    crop_size=224  ,		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=224  ,		)

										return image_processor







					@property
					def       lowerCAmelCase_    (     self			)  ->   str:



										'''simple docstring'''



										torch.manual_seed(0			)

										a     =				{
										    "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,
										}

										a     =				PriorTransformer(**A			)
										return model







					@property
					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										torch.manual_seed(0			)

										a     =				{
										    "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,
										    ),
										}
										a     =				ShapERenderer(**A			)
										return model







					def       lowerCAmelCase_    (     self			)  ->   Any:



										'''simple docstring'''



										a     =				self.dummy_prior
										a     =				self.dummy_image_encoder
										a     =				self.dummy_image_processor
										a     =				self.dummy_renderer

										a     =				HeunDiscreteScheduler(
										    beta_schedule="exp"  ,		num_train_timesteps=1024  ,		prediction_type="sample"  ,		use_karras_sigmas=A  ,		clip_sample=A  ,		clip_sample_range=1.0  ,		)
										a     =				{
										    "prior": prior,
										    "image_encoder": image_encoder,
										    "image_processor": image_processor,
										    "renderer": renderer,
										    "scheduler": scheduler,
										}

										return components







					def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   List[Any]:



										'''simple docstring'''



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

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







					def       lowerCAmelCase_    (     self			)  ->   Tuple:



										'''simple docstring'''



										a     =				"cpu"

										a     =				self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=A			)

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

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

										a     =				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			)  ->   Any:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



										'''simple docstring'''



										a     =				torch_device == "cpu"
										a     =				True
										self._test_inference_batch_single_identical(
										    batch_size=2  ,		test_max_difference=A  ,		relax_max_difference=A  ,		)







					def       lowerCAmelCase_    (     self			)  ->   Any:



										'''simple docstring'''



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

										a     =				1
										a     =				2

										a     =				self.get_dummy_inputs(A			)

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

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

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







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







					def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



										'''simple docstring'''



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







					def       lowerCAmelCase_    (     self			)  ->   str:



										'''simple docstring'''



										a     =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"			)
										a     =				load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/shap_e/test_shap_e_img2img_out.npy"			)
										a     =				ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"			)
										a     =				pipe.to(A			)
										pipe.set_progress_bar_config(disable=A			)

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

										a     =				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			)







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

