


"""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 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     ShapEImgaImgPipeline
		SCREAMING_SNAKE_CASE_						:     List[str]								=     ["""image"""]
		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ["""image"""]
		SCREAMING_SNAKE_CASE_						:     Optional[int]								=     [
		    """num_images_per_prompt""",
		    """num_inference_steps""",
		    """generator""",
		    """latents""",
		    """guidance_scale""",
		    """frame_size""",
		    """output_type""",
		    """return_dict""",
		]
		SCREAMING_SNAKE_CASE_						:     Dict								=     False







		@property
		def 							__A						(	self				)     ->							Optional[int]:
								return 32







		@property
		def 							__A						(	self				)     ->							Union[str, Any]:
								return 32







		@property
		def 							__A						(	self				)     ->							Union[str, Any]:
								return self.time_input_dim * 4







		@property
		def 							__A						(	self				)     ->							Tuple:
								return 8







		@property
		def 							__A						(	self				)     ->							List[Any]:
								torch.manual_seed(0				)
								SCREAMING_SNAKE_CASE													=					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						,       )

								SCREAMING_SNAKE_CASE													=					CLIPVisionModel(lowerCAmelCase__				)
								return model







		@property
		def 							__A						(	self				)     ->							str:
								SCREAMING_SNAKE_CASE													=					CLIPImageProcessor(
								    crop_size=224						,       do_center_crop=lowerCAmelCase__						,       do_normalize=lowerCAmelCase__						,       do_resize=lowerCAmelCase__						,       image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]						,       image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]						,       resample=3						,       size=224						,       )

								return image_processor







		@property
		def 							__A						(	self				)     ->							str:
								torch.manual_seed(0				)

								SCREAMING_SNAKE_CASE													=					{
								    '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,
								}

								SCREAMING_SNAKE_CASE													=					PriorTransformer(**lowerCAmelCase__				)
								return model







		@property
		def 							__A						(	self				)     ->							Dict:
								torch.manual_seed(0				)

								SCREAMING_SNAKE_CASE													=					{
								    '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,
								    ),
								}
								SCREAMING_SNAKE_CASE													=					ShapERenderer(**lowerCAmelCase__				)
								return model







		def 							__A						(	self				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					self.dummy_prior
								SCREAMING_SNAKE_CASE													=					self.dummy_image_encoder
								SCREAMING_SNAKE_CASE													=					self.dummy_image_processor
								SCREAMING_SNAKE_CASE													=					self.dummy_renderer

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

								return components







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=0				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					floats_tensor((1, 3, 64, 64)						,       rng=random.Random(lowerCAmelCase__				)				).to(lowerCAmelCase__				)

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







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					'cpu'

								SCREAMING_SNAKE_CASE													=					self.get_dummy_components()

								SCREAMING_SNAKE_CASE													=					self.pipeline_class(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe.to(lowerCAmelCase__				)

								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

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

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

								SCREAMING_SNAKE_CASE													=					np.array(
								    [
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								        0.00_03_92_16,
								    ]				)

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







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







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					torch_device == 'cpu'
								SCREAMING_SNAKE_CASE													=					True
								self._test_inference_batch_single_identical(
								    batch_size=2						,       test_max_difference=lowerCAmelCase__						,       relax_max_difference=lowerCAmelCase__						,       )







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.get_dummy_components()
								SCREAMING_SNAKE_CASE													=					self.pipeline_class(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					pipe.to(lowerCAmelCase__				)
								pipe.set_progress_bar_config(disable=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					1
								SCREAMING_SNAKE_CASE													=					2

								SCREAMING_SNAKE_CASE													=					self.get_dummy_inputs(lowerCAmelCase__				)

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

								SCREAMING_SNAKE_CASE													=					pipe(**lowerCAmelCase__						,       num_images_per_prompt=lowerCAmelCase__				)[0]

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





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




		'''simple docstring'''







		def 							__A						(	self				)     ->							List[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







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

								SCREAMING_SNAKE_CASE													=					torch.Generator(device=lowerCAmelCase__				).manual_seed(0				)

								SCREAMING_SNAKE_CASE													=					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__				)






"""simple docstring"""




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=7						,       lowerCAmelCase__=3						,       lowerCAmelCase__=18						,       lowerCAmelCase__=30						,       lowerCAmelCase__=400						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       )     ->							int:
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'height': 18, 'width': 18}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize







		def 							__A						(	self				)     ->							Optional[Any]:
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
								            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
								        ]				),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}





@require_torch
@require_vision
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ImageGPTImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'clusters'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_resize'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'size'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_normalize'				)				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'height': 18, 'width': 18}				)

								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict						,       size=42				)
								self.assertEqual(image_processor.size						,       {'height': 42, 'width': 42}				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								SCREAMING_SNAKE_CASE													=					json.loads(image_processor.to_json_string()				)
								for key, value in self.image_processor_dict.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       obj[key]				)				)
														else:
																				self.assertEqual(obj[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       'image_processor.json'				)
														image_processor_first.to_json_file(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_json_file(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_pretrained(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		@unittest.skip('ImageGPT requires clusters at initialization'				)
		def 							__A						(	self				)     ->							Optional[Any]:
								pass





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					load_dataset('hf-internal-testing/fixtures_image_utils' ,			split='test'  )

						SCREAMING_SNAKE_CASE													=					Image.open(dataset[4]['file']  )
						SCREAMING_SNAKE_CASE													=					Image.open(dataset[5]['file']  )

						SCREAMING_SNAKE_CASE													=					[imagea, imagea]

						return images


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




		'''simple docstring'''







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

								SCREAMING_SNAKE_CASE													=					prepare_images()

								# test non-batched
								SCREAMING_SNAKE_CASE													=					image_processing(images[0]						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (1, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[306, 191, 191]
								self.assertEqual(encoding.input_ids[0, :3].tolist()						,       lowerCAmelCase__				)

								# test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (2, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[303, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()						,       lowerCAmelCase__				)


