






'''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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
	snake_case_  							=							ShapEImgaImgPipeline
	snake_case_  							=							["""image"""]
	snake_case_  							=							["""image"""]
	snake_case_  							=							[
	    """num_images_per_prompt""",
	    """num_inference_steps""",
	    """generator""",
	    """latents""",
	    """guidance_scale""",
	    """frame_size""",
	    """output_type""",
	    """return_dict""",
	]
	snake_case_  							=							False




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




	@property
	def      __magic_name__	(      self	:							List[str]      )	->						Optional[int]:
							return 32




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




	@property
	def      __magic_name__	(      self	:							Dict      )	->						Union[str, Any]:
							return 8




	@property
	def      __magic_name__	(      self	:							Optional[int]      )	->						Union[str, Any]:
							torch.manual_seed(0      )
							SCREAMING_SNAKE_CASE__     :				Dict									=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__     :				str									=CLIPVisionModel(__lowercase      )
							return model




	@property
	def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
							SCREAMING_SNAKE_CASE__     :				int									=CLIPImageProcessor(
							    crop_size=2_24					,      do_center_crop=__lowercase					,      do_normalize=__lowercase					,      do_resize=__lowercase					,      image_mean=[0.48145466, 0.4578275, 0.40821073]					,      image_std=[0.26862954, 0.26130258, 0.27577711]					,      resample=3					,      size=2_24					,      )

							return image_processor




	@property
	def      __magic_name__	(      self	:							List[str]      )	->						Dict:
							torch.manual_seed(0      )

							SCREAMING_SNAKE_CASE__     :				str									={
							    '''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__     :				str									=PriorTransformer(**__lowercase      )
							return model




	@property
	def      __magic_name__	(      self	:							Tuple      )	->						List[str]:
							torch.manual_seed(0      )

							SCREAMING_SNAKE_CASE__     :				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,
							    ),
							}
							SCREAMING_SNAKE_CASE__     :				str									=ShapERenderer(**__lowercase      )
							return model




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

							SCREAMING_SNAKE_CASE__     :				int									=HeunDiscreteScheduler(
							    beta_schedule='''exp'''					,      num_train_timesteps=10_24					,      prediction_type='''sample'''					,      use_karras_sigmas=__lowercase					,      clip_sample=__lowercase					,      clip_sample_range=1.0					,      )
							SCREAMING_SNAKE_CASE__     :				Any									={
							    '''prior''': prior,
							    '''image_encoder''': image_encoder,
							    '''image_processor''': image_processor,
							    '''renderer''': renderer,
							    '''scheduler''': scheduler,
							}

							return components




	def      __magic_name__	(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Any=0      )	->						Any:
							SCREAMING_SNAKE_CASE__     :				int									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )

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




	def      __magic_name__	(      self	:							List[str]      )	->						str:
							SCREAMING_SNAKE_CASE__     :				int									='''cpu'''

							SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()

							SCREAMING_SNAKE_CASE__     :				str									=self.pipeline_class(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Any									=pipe.to(__lowercase      )

							pipe.set_progress_bar_config(disable=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
							SCREAMING_SNAKE_CASE__     :				Tuple									=output.images[0]
							SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]

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

							SCREAMING_SNAKE_CASE__     :				List[Any]									=np.array(
							    [
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							        0.00039216,
							    ]      )

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




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




	def      __magic_name__	(      self	:							Optional[int]      )	->						str:
							SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch_device == '''cpu'''
							SCREAMING_SNAKE_CASE__     :				Optional[Any]									=True
							self._test_inference_batch_single_identical(
							    batch_size=2					,      test_max_difference=__lowercase					,      relax_max_difference=__lowercase					,      )




	def      __magic_name__	(      self	:							Dict      )	->						List[str]:
							SCREAMING_SNAKE_CASE__     :				Any									=self.get_dummy_components()
							SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
							SCREAMING_SNAKE_CASE__     :				Optional[int]									=pipe.to(__lowercase      )
							pipe.set_progress_bar_config(disable=__lowercase      )

							SCREAMING_SNAKE_CASE__     :				Optional[int]									=1
							SCREAMING_SNAKE_CASE__     :				List[str]									=2

							SCREAMING_SNAKE_CASE__     :				Dict									=self.get_dummy_inputs(__lowercase      )

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

							SCREAMING_SNAKE_CASE__     :				List[Any]									=pipe(**__lowercase					,      num_images_per_prompt=__lowercase      )[0]

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






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




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




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

							SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(0      )

							SCREAMING_SNAKE_CASE__     :				Tuple									=pipe(
							    __lowercase					,      generator=__lowercase					,      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(__lowercase					,      __lowercase      )







'''simple docstring'''
import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu






class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=inspect.getfile(accelerate.test_utils      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''test_script.py''']      )
       SCREAMING_SNAKE_CASE__     :				Dict									=os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




 @require_tpu
 def      __magic_name__	(      self	:							int      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Any									=F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
       SCREAMING_SNAKE_CASE__     :				List[str]									=[sys.executable] + distributed_args
       execute_subprocess_async(__lowercase					,      env=os.environ.copy()      )