


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


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 32


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 32


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return self.time_input_dim * 4


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return 8


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)
  __A  :		Tuple       =			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      ,      )

  __A  :		str       =			CLIPVisionModel(__lowercase						)
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			CLIPImageProcessor(
      crop_size=224      ,      do_center_crop=__lowercase      ,      do_normalize=__lowercase      ,      do_resize=__lowercase      ,      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=224      ,      )

  return image_processor


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)

  __A  :		Tuple       =			{
      "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,
  }

  __A  :		Union[str, Any]       =			PriorTransformer(**__lowercase						)
  return model


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  torch.manual_seed(0						)

  __A  :		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,
      ),
  }
  __A  :		Any       =			ShapERenderer(**__lowercase						)
  return model


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.dummy_prior
  __A  :		Tuple       =			self.dummy_image_encoder
  __A  :		List[str]       =			self.dummy_image_processor
  __A  :		Optional[Any]       =			self.dummy_renderer

  __A  :		str       =			HeunDiscreteScheduler(
      beta_schedule='exp'      ,      num_train_timesteps=1_024      ,      prediction_type='sample'      ,      use_karras_sigmas=__lowercase      ,      clip_sample=__lowercase      ,      clip_sample_range=1.0      ,      )
  __A  :		List[Any]       =			{
      "prior": prior,
      "image_encoder": image_encoder,
      "image_processor": image_processor,
      "renderer": renderer,
      "scheduler": scheduler,
  }

  return components


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0						):

  """simple docstring"""







  __A  :		Any       =			floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(__lowercase						)						).to(__lowercase						)

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


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			"cpu"

  __A  :		Optional[Any]       =			self.get_dummy_components()

  __A  :		Union[str, Any]       =			self.pipeline_class(**__lowercase						)
  __A  :		List[Any]       =			pipe.to(__lowercase						)

  pipe.set_progress_bar_config(disable=__lowercase						)

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

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

  __A  :		Optional[int]       =			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						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Dict       =			self.get_dummy_components()
  __A  :		Optional[Any]       =			self.pipeline_class(**__lowercase						)
  __A  :		Any       =			pipe.to(__lowercase						)
  pipe.set_progress_bar_config(disable=__lowercase						)

  __A  :		Union[str, Any]       =			1
  __A  :		Optional[Any]       =			2

  __A  :		List[Any]       =			self.get_dummy_inputs(__lowercase						)

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

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

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


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




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







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







 def  snake_case__   (      self						):

  """simple docstring"""







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

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

  __A  :		Union[str, Any]       =			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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     Optional[int]								=      {
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''
 __lowercase				:		Tuple    							=						'''vit_msn'''


 def __init__(      self      ,      __lowercase=768      ,      __lowercase=12      ,      __lowercase=12      ,      __lowercase=3_072      ,      __lowercase="gelu"      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=1E-06      ,      __lowercase=224      ,      __lowercase=16      ,      __lowercase=3      ,      __lowercase=True      ,      **__lowercase      ,      ):

  """simple docstring"""







  super().__init__(**__lowercase						)

  __A  :		Tuple       =			hidden_size
  __A  :		Optional[int]       =			num_hidden_layers
  __A  :		str       =			num_attention_heads
  __A  :		Dict       =			intermediate_size
  __A  :		Optional[Any]       =			hidden_act
  __A  :		Union[str, Any]       =			hidden_dropout_prob
  __A  :		Optional[Any]       =			attention_probs_dropout_prob
  __A  :		List[str]       =			initializer_range
  __A  :		Dict       =			layer_norm_eps
  __A  :		List[Any]       =			image_size
  __A  :		Optional[int]       =			patch_size
  __A  :		List[Any]       =			num_channels
  __A  :		Optional[Any]       =			qkv_bias






