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





							@property
							def       snake_case__						(      self				:							Any	)							->	List[str]:
								return 3_2





							@property
							def       snake_case__						(      self				:							str	)							->	str:
								return 3_2





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[Any]:
								return self.time_input_dim * 4





							@property
							def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
								return 8





							@property
							def       snake_case__						(      self				:							Tuple	)							->	Any:
								torch.manual_seed(0	)
								__UpperCAmelCase							=			CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size     ,		image_size=6_4     ,		projection_dim=self.text_embedder_hidden_size     ,		intermediate_size=3_7     ,		num_attention_heads=4     ,		num_channels=3     ,		num_hidden_layers=5     ,		patch_size=1     ,		)

								__UpperCAmelCase							=			CLIPVisionModel(lowerCamelCase__	)
								return model





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	Tuple:
								__UpperCAmelCase							=			CLIPImageProcessor(
								    crop_size=2_2_4     ,		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=2_2_4     ,		)

								return image_processor





							@property
							def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    "num_attention_heads": 2,
								    "attention_head_dim": 1_6,
								    "embedding_dim": self.time_input_dim,
								    "num_embeddings": 3_2,
								    "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       snake_case__						(      self				:							List[Any]	)							->	Optional[Any]:
								torch.manual_seed(0	)

								__UpperCAmelCase							=			{
								    "param_shapes": (
								        (self.renderer_dim, 9_3),
								        (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": 1_2,
								    "background": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								__UpperCAmelCase							=			ShapERenderer(**lowerCamelCase__	)
								return model





							def       snake_case__						(      self				:							int	)							->	List[str]:
								__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_0_2_4     ,		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       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[Any]     ,		__a				:							Optional[int]=0	)							->	Optional[Any]:
								__UpperCAmelCase							=			floats_tensor((1, 3, 6_4, 6_4)     ,		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": 3_2,
								    "output_type": "np",
								}
								return inputs





							def       snake_case__						(      self				:							List[str]	)							->	List[str]:
								__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 == (2_0, 3_2, 3_2, 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       snake_case__						(      self				:							Optional[Any]	)							->	str:
								self._test_inference_batch_consistent(batch_sizes=[1, 2]	)





							def       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
								__UpperCAmelCase							=			torch_device == "cpu"
								__UpperCAmelCase							=			True
								self._test_inference_batch_single_identical(
								    batch_size=2     ,		test_max_difference=lowerCamelCase__     ,		relax_max_difference=lowerCamelCase__     ,		)






							def       snake_case__						(      self				:							int	)							->	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 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Optional[int]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def       snake_case__						(      self				:							Optional[int]	)							->	Optional[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=6_4     ,		frame_size=6_4     ,		output_type='''np'''     ,		).images[0]

								assert images.shape == (2_0, 6_4, 6_4, 3)

								assert_mean_pixel_difference(lowerCamelCase__     ,		lowerCamelCase__	)

'''simple docstring'''


from ...configuration_utils import PretrainedConfig






class 						A   (   UpperCAmelCase   ):
							a_        =      '''bert-generation'''





							def __init__(      self				:							str     ,		__a				:							str=5_0_3_5_8     ,		__a				:							int=1_0_2_4     ,		__a				:							Optional[Any]=2_4     ,		__a				:							Any=1_6     ,		__a				:							int=4_0_9_6     ,		__a				:							Any="gelu"     ,		__a				:							Union[str, Any]=0.1     ,		__a				:							Any=0.1     ,		__a				:							Union[str, Any]=5_1_2     ,		__a				:							int=0.0_2     ,		__a				:							str=1e-12     ,		__a				:							List[str]=0     ,		__a				:							Optional[int]=2     ,		__a				:							Tuple=1     ,		__a				:							str="absolute"     ,		__a				:							Optional[Any]=True     ,		**__a				:							Tuple     ,		)							->	Any:
								super().__init__(pad_token_id=__a     ,		bos_token_id=__a     ,		eos_token_id=__a     ,		**__a	)

								__UpperCAmelCase							=			vocab_size
								__UpperCAmelCase							=			hidden_size
								__UpperCAmelCase							=			num_hidden_layers
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			hidden_act
								__UpperCAmelCase							=			intermediate_size
								__UpperCAmelCase							=			hidden_dropout_prob
								__UpperCAmelCase							=			attention_probs_dropout_prob
								__UpperCAmelCase							=			max_position_embeddings
								__UpperCAmelCase							=			initializer_range
								__UpperCAmelCase							=			layer_norm_eps
								__UpperCAmelCase							=			position_embedding_type
								__UpperCAmelCase							=			use_cache
