


'''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	(						lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Tuple				   =    ShapEImgaImgPipeline
		A_     :      Tuple				   =    ["""image"""]
		A_     :      int				   =    ["""image"""]
		A_     :      Dict				   =    [
		    """num_images_per_prompt""",
		    """num_inference_steps""",
		    """generator""",
		    """latents""",
		    """guidance_scale""",
		    """frame_size""",
		    """output_type""",
		    """return_dict""",
		]
		A_     :      Optional[Any]				   =    False







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







		@property
		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[Any]:
			return 32







		@property
		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			return self.time_input_dim * 4







		@property
		def        __lowerCAmelCase		( self		:						int      )  ->			Any:
			return 8







		@property
		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			int:
			torch.manual_seed(0      )
			__magic_name__	:   str					     =					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  ,					)

			__magic_name__	:   Optional[Any]					     =					CLIPVisionModel(__UpperCAmelCase      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			__magic_name__	:   Dict					     =					CLIPImageProcessor(
			    crop_size=224  ,					do_center_crop=__UpperCAmelCase  ,					do_normalize=__UpperCAmelCase  ,					do_resize=__UpperCAmelCase  ,					image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,					image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,					resample=3  ,					size=224  ,					)

			return image_processor







		@property
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			torch.manual_seed(0      )

			__magic_name__	:   Union[str, Any]					     =					{
			    '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,
			}

			__magic_name__	:   Any					     =					PriorTransformer(**__UpperCAmelCase      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			int:
			torch.manual_seed(0      )

			__magic_name__	:   Optional[Any]					     =					{
			    '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,
			    ),
			}
			__magic_name__	:   str					     =					ShapERenderer(**__UpperCAmelCase      )
			return model







		def        __lowerCAmelCase		( self		:						int      )  ->			Optional[Any]:
			__magic_name__	:   Any					     =					self.dummy_prior
			__magic_name__	:   List[str]					     =					self.dummy_image_encoder
			__magic_name__	:   Optional[int]					     =					self.dummy_image_processor
			__magic_name__	:   int					     =					self.dummy_renderer

			__magic_name__	:   Union[str, Any]					     =					HeunDiscreteScheduler(
			    beta_schedule='exp'  ,					num_train_timesteps=1024  ,					prediction_type='sample'  ,					use_karras_sigmas=__UpperCAmelCase  ,					clip_sample=__UpperCAmelCase  ,					clip_sample_range=1.0  ,					)
			__magic_name__	:   Optional[int]					     =					{
			    'prior': prior,
			    'image_encoder': image_encoder,
			    'image_processor': image_processor,
			    'renderer': renderer,
			    'scheduler': scheduler,
			}

			return components







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Union[str, Any]  ,					_A		:						Dict=0      )  ->			Union[str, Any]:
			__magic_name__	:   Dict					     =					floats_tensor((1, 3, 64, 64)  ,					rng=random.Random(__UpperCAmelCase      )      ).to(__UpperCAmelCase      )

			if str(__UpperCAmelCase      ).startswith('mps'      ):
				__magic_name__	:   Tuple					     =					torch.manual_seed(__UpperCAmelCase      )
			else:
				__magic_name__	:   str					     =					torch.Generator(device=__UpperCAmelCase      ).manual_seed(__UpperCAmelCase      )
			__magic_name__	:   List[str]					     =					{
			    'image': input_image,
			    'generator': generator,
			    'num_inference_steps': 1,
			    'frame_size': 32,
			    'output_type': 'np',
			}
			return inputs







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					'cpu'

			__magic_name__	:   List[str]					     =					self.get_dummy_components()

			__magic_name__	:   str					     =					self.pipeline_class(**__UpperCAmelCase      )
			__magic_name__	:   Optional[int]					     =					pipe.to(__UpperCAmelCase      )

			pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

			__magic_name__	:   Dict					     =					pipe(**self.get_dummy_inputs(__UpperCAmelCase      )      )
			__magic_name__	:   Dict					     =					output.images[0]
			__magic_name__	:   Any					     =					image[0, -3:, -3:, -1]

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

			__magic_name__	:   Tuple					     =					np.array(
			    [
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			        0.0003_9216,
			    ]      )

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







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			List[str]:
			self._test_inference_batch_consistent(batch_sizes=[1, 2]      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Tuple:
			__magic_name__	:   int					     =					torch_device == 'cpu'
			__magic_name__	:   Any					     =					True
			self._test_inference_batch_single_identical(
			    batch_size=2  ,					test_max_difference=__UpperCAmelCase  ,					relax_max_difference=__UpperCAmelCase  ,					)







		def        __lowerCAmelCase		( self		:						Any      )  ->			List[Any]:
			__magic_name__	:   List[Any]					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					self.pipeline_class(**__UpperCAmelCase      )
			__magic_name__	:   Any					     =					pipe.to(__UpperCAmelCase      )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

			__magic_name__	:   int					     =					1
			__magic_name__	:   Optional[int]					     =					2

			__magic_name__	:   Dict					     =					self.get_dummy_inputs(__UpperCAmelCase      )

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

			__magic_name__	:   int					     =					pipe(**__UpperCAmelCase  ,					num_images_per_prompt=__UpperCAmelCase      )[0]

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







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



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Tuple:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Union[str, Any]:
			__magic_name__	:   Tuple					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'      )
			__magic_name__	:   int					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/shap_e/test_shap_e_img2img_out.npy'      )
			__magic_name__	:   Dict					     =					ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'      )
			__magic_name__	:   Tuple					     =					pipe.to(__UpperCAmelCase      )
			pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

			__magic_name__	:   Optional[Any]					     =					torch.Generator(device=__UpperCAmelCase      ).manual_seed(0      )

			__magic_name__	:   Dict					     =					pipe(
			    __UpperCAmelCase  ,					generator=__UpperCAmelCase  ,					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(__UpperCAmelCase  ,					__UpperCAmelCase      )



'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Any  ,					*_A		:						List[Any]  ,					**_A		:						str      )  ->			None:
			warnings.warn(
			    'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
			    ' use BeitImageProcessor instead.'  ,					_A  ,					)
			super().__init__(*_A  ,					**_A      )