


"""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 							A_      (       _a     ,	unittest.TestCase ):
  lowerCAmelCase__      			=	ShapEImgaImgPipeline
  lowerCAmelCase__      			=	['image']
  lowerCAmelCase__      			=	['image']
  lowerCAmelCase__      			=	[
      'num_images_per_prompt',
      'num_inference_steps',
      'generator',
      'latents',
      'guidance_scale',
      'frame_size',
      'output_type',
      'return_dict',
  ]
  lowerCAmelCase__      			=	False


  @property
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     return 32


  @property
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     return 32


  @property
  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     return self.time_input_dim * 4


  @property
  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     return 8


  @property
  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     torch.manual_seed(0    )
     _lowerCamelCase   :				Union[str, Any]							=     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							,)

     _lowerCamelCase   :				Any							=     CLIPVisionModel(__lowerCAmelCase    )
     return model


  @property
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     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 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     torch.manual_seed(0    )

     _lowerCamelCase   :				int							=     {
         "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,
     }

     _lowerCamelCase   :				str							=     PriorTransformer(**__lowerCAmelCase    )
     return model


  @property
  def 							_lowercase					(			self:						Any    ):







     '''simple docstring'''







     torch.manual_seed(0    )

     _lowerCamelCase   :				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,
         ),
     }
     _lowerCamelCase   :				Union[str, Any]							=     ShapERenderer(**__lowerCAmelCase    )
     return model


  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     self.dummy_prior
     _lowerCamelCase   :				List[Any]							=     self.dummy_image_encoder
     _lowerCamelCase   :				Union[str, Any]							=     self.dummy_image_processor
     _lowerCamelCase   :				Tuple							=     self.dummy_renderer

     _lowerCamelCase   :				Tuple							=     HeunDiscreteScheduler(
         beta_schedule="exp"							,num_train_timesteps=1_024							,prediction_type="sample"							,use_karras_sigmas=__lowerCAmelCase							,clip_sample=__lowerCAmelCase							,clip_sample_range=1.0							,)
     _lowerCamelCase   :				Union[str, Any]							=     {
         "prior": prior,
         "image_encoder": image_encoder,
         "image_processor": image_processor,
         "renderer": renderer,
         "scheduler": scheduler,
     }

     return components


  def 							_lowercase					(			self:						Optional[Any]							,__lowerCAmelCase:						Tuple							,__lowerCAmelCase:						Optional[Any]=0    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     floats_tensor((1, 3, 64, 64)							,rng=random.Random(__lowerCAmelCase    )    ).to(__lowerCAmelCase    )

     if str(__lowerCAmelCase    ).startswith("mps"    ):
        _lowerCamelCase   :				Tuple							=     torch.manual_seed(__lowerCAmelCase    )
     else:
        _lowerCamelCase   :				Union[str, Any]							=     torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
     _lowerCamelCase   :				Union[str, Any]							=     {
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 1,
         "frame_size": 32,
         "output_type": "np",
     }
     return inputs


  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     "cpu"

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

     _lowerCamelCase   :				int							=     self.pipeline_class(**__lowerCAmelCase    )
     _lowerCamelCase   :				Tuple							=     pipe.to(__lowerCAmelCase    )

     pipe.set_progress_bar_config(disable=__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     pipe(**self.get_dummy_inputs(__lowerCAmelCase    )    )
     _lowerCamelCase   :				List[Any]							=     output.images[0]
     _lowerCamelCase   :				Optional[int]							=     image[0, -3:, -3:, -1]

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

     _lowerCamelCase   :				Union[str, Any]							=     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 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







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


  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     torch_device == "cpu"
     _lowerCamelCase   :				List[Any]							=     True
     self._test_inference_batch_single_identical(
         batch_size=2							,test_max_difference=__lowerCAmelCase							,relax_max_difference=__lowerCAmelCase							,)






  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				int							=     self.get_dummy_components()
     _lowerCamelCase   :				Optional[int]							=     self.pipeline_class(**__lowerCAmelCase    )
     _lowerCamelCase   :				List[Any]							=     pipe.to(__lowerCAmelCase    )
     pipe.set_progress_bar_config(disable=__lowerCAmelCase    )

     _lowerCamelCase   :				Dict							=     1
     _lowerCamelCase   :				Optional[int]							=     2

     _lowerCamelCase   :				str							=     self.get_dummy_inputs(__lowerCAmelCase    )

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

     _lowerCamelCase   :				str							=     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 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







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






  def 							_lowercase					(			self:						Optional[int]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"    )
     _lowerCamelCase   :				List[Any]							=     load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/shap_e/test_shap_e_img2img_out.npy"    )
     _lowerCamelCase   :				Optional[Any]							=     ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"    )
     _lowerCamelCase   :				Any							=     pipe.to(__lowerCAmelCase    )
     pipe.set_progress_bar_config(disable=__lowerCAmelCase    )

     _lowerCamelCase   :				int							=     torch.Generator(device=__lowerCAmelCase    ).manual_seed(0    )

     _lowerCamelCase   :				Dict							=     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"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		HfArgumentParser(__snake_case   )
   _lowerCamelCase	:					int   =		parser.parse_args_into_dataclasses()[0]
   _lowerCamelCase	:					Dict   =		TensorFlowBenchmark(args=__snake_case   )
   try:
      _lowerCamelCase	:					Optional[int]   =		parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      _lowerCamelCase	:					Union[str, Any]   =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
      _lowerCamelCase	:					List[str]   =		""" """.join(str(__snake_case   ).split(""" """   )[:-1]   )
      _lowerCamelCase	:					Dict   =		""""""
      _lowerCamelCase	:					List[Any]   =		eval(str(__snake_case   ).split(""" """   )[-1]   )
      _lowerCamelCase	:					Tuple   =		[]
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]   )
         else:
            wrong_args.append(__snake_case   )
      if len(__snake_case   ) > 0:
         _lowerCamelCase	:					Tuple   =		full_error_msg + begin_error_msg + str(__snake_case   )
      raise ValueError(__snake_case   )
   benchmark.run()


if __name__ == "__main__":
 main()

