






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 							__UpperCamelCase     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							ShapEImgaImgPipeline
	__snake_case						:Union[str, Any] =							['image']
	__snake_case						:int =							['image']
	__snake_case						:List[Any] =							[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__snake_case						:Dict =							False







	@property
	def 			_a (	self   :				Optional[int]      )		->			Dict:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				Tuple      )		->			Union[str, Any]:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







		return self.time_input_dim * 4







	@property
	def 			_a (	self   :				List[Any]      )		->			Dict:
		"""simple docstring"""







		return 8







	@property
	def 			_a (	self   :				Dict      )		->			Optional[Any]:
		"""simple docstring"""







		torch.manual_seed(0      )
		__lowercase   							=	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							,					)

		__lowercase   							=	CLIPVisionModel(_lowerCAmelCase      )
		return model







	@property
	def 			_a (	self   :				Dict      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	CLIPImageProcessor(
		    crop_size=224							,					do_center_crop=_lowerCAmelCase							,					do_normalize=_lowerCAmelCase							,					do_resize=_lowerCAmelCase							,					image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]							,					image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]							,					resample=3							,					size=224							,					)

		return image_processor







	@property
	def 			_a (	self   :				str      )		->			int:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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,
		}

		__lowercase   							=	PriorTransformer(**_lowerCAmelCase      )
		return model







	@property
	def 			_a (	self   :				Dict      )		->			Optional[Any]:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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,
		    ),
		}
		__lowercase   							=	ShapERenderer(**_lowerCAmelCase      )
		return model







	def 			_a (	self   :				List[str]      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	self.dummy_prior
		__lowercase   							=	self.dummy_image_encoder
		__lowercase   							=	self.dummy_image_processor
		__lowercase   							=	self.dummy_renderer

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

		return components







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				List[str]=0      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	floats_tensor((1, 3, 64, 64)							,					rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )

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







	def 			_a (	self   :				Optional[Any]      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""

		__lowercase   							=	self.get_dummy_components()

		__lowercase   							=	self.pipeline_class(**_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )

		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

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

		__lowercase   							=	np.array(
		    [
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		    ]      )

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







	def 			_a (	self   :				str      )		->			str:
		"""simple docstring"""







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







	def 			_a (	self   :				int      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	torch_device == """cpu"""
		__lowercase   							=	True
		self._test_inference_batch_single_identical(
		    batch_size=2							,					test_max_difference=_lowerCAmelCase							,					relax_max_difference=_lowerCAmelCase							,					)







	def 			_a (	self   :				Dict      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	self.pipeline_class(**_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	1
		__lowercase   							=	2

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )

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

		__lowercase   							=	pipe(**_lowerCAmelCase							,					num_images_per_prompt=_lowerCAmelCase      )[0]

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


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







	def 			_a (	self   :				List[Any]      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				str      )		->			Tuple:
		"""simple docstring"""







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

		__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )

		__lowercase   							=	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      )




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    42
  _a		     =    42


if is_transformers_available() and is_torch_available():
  from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


