

'''simple docstring'''



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MobileNetVaForImageClassification, MobileNetVaModel
  from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""tf_padding"""     )     )
      self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE   ,				"""depth_multiplier"""     )     )







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			int=3   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=32   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.25   ,				__SCREAMING_SNAKE_CASE   :			List[str]=8   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			int=1_024   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=32   ,				__SCREAMING_SNAKE_CASE   :			List[Any]="relu6"   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]=True   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=10   ,				__SCREAMING_SNAKE_CASE   :			int=None   ,				)    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = num_channels
      __SCREAMING_SNAKE_CASE      = image_size
      __SCREAMING_SNAKE_CASE      = depth_multiplier
      __SCREAMING_SNAKE_CASE      = min_depth
      __SCREAMING_SNAKE_CASE      = tf_padding
      __SCREAMING_SNAKE_CASE      = int(last_hidden_size * depth_multiplier     )
      __SCREAMING_SNAKE_CASE      = output_stride
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = classifier_dropout_prob
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = num_labels
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = scope
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = None
      if self.use_labels:
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size]   ,				self.num_labels     )
           __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels     )

      __SCREAMING_SNAKE_CASE      = self.get_config()

      return config, pixel_values, labels, pixel_labels
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Tuple:


      """simple docstring"""




      return MobileNetVaConfig(
          num_channels=self.num_channels   ,				image_size=self.image_size   ,				depth_multiplier=self.depth_multiplier   ,				min_depth=self.min_depth   ,				tf_padding=self.tf_padding   ,				hidden_act=self.hidden_act   ,				classifier_dropout_prob=self.classifier_dropout_prob   ,				initializer_range=self.initializer_range   ,				)
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			int   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			int     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModel(config=__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(
          result.last_hidden_state.shape   ,				(
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )   ,				)
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.num_labels
      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE     )
      model.to(__SCREAMING_SNAKE_CASE     )
      model.eval()
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				labels=__SCREAMING_SNAKE_CASE     )
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)     )


 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
 lowerCAmelCase__       = (
     {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
     if is_torch_available()
     else {}
 )

 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 lowerCAmelCase__       = False
 def  UpperCAmelCase__						(						self   :			str     )    ->					Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaModelTester(self     )
      __SCREAMING_SNAKE_CASE      = MobileNetVaConfigTester(self   ,				config_class=__SCREAMING_SNAKE_CASE   ,				has_text_modality=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Any:


      """simple docstring"""




      self.config_tester.run_common_tests()
 @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					List[Any]:


      """simple docstring"""




      pass
 @unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Optional[int]:


      """simple docstring"""




      pass
 def  UpperCAmelCase__						(						self   :			int     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE      = model_class(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = inspect.signature(model.forward     )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __SCREAMING_SNAKE_CASE      = [*signature.parameters.keys()]

           __SCREAMING_SNAKE_CASE      = ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[Any]:


      """simple docstring"""




      def check_hidden_states_output(__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			str     ):
           __SCREAMING_SNAKE_CASE      = model_class(__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()

           with torch.no_grad():
                __SCREAMING_SNAKE_CASE      = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )     )

           __SCREAMING_SNAKE_CASE      = outputs.hidden_states

           __SCREAMING_SNAKE_CASE      = 26
           self.assertEqual(len(__SCREAMING_SNAKE_CASE     )   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           __SCREAMING_SNAKE_CASE      = True
           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __SCREAMING_SNAKE_CASE      = True

           check_hidden_states_output(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE     )


 @slow
 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __SCREAMING_SNAKE_CASE      = MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     return image







@require_torch
@require_vision
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @cached_property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					List[str]:


      """simple docstring"""




      return (
          MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""     ) if is_vision_available() else None
      )


 @slow
 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.default_image_processor
      __SCREAMING_SNAKE_CASE      = prepare_img()
      __SCREAMING_SNAKE_CASE      = image_processor(images=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""     ).to(__SCREAMING_SNAKE_CASE     )

      # forward pass
      with torch.no_grad():
           __SCREAMING_SNAKE_CASE      = model(**__SCREAMING_SNAKE_CASE     )

      # verify the logits
      __SCREAMING_SNAKE_CASE      = torch.Size((1, 1_001)     )
      self.assertEqual(outputs.logits.shape   ,				__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(__SCREAMING_SNAKE_CASE     )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-4     )     )









'''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__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 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  UpperCAmelCase__						(						self   :			Tuple     )    ->					Tuple:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					int:


      """simple docstring"""




      return 32
 @property
 def  UpperCAmelCase__						(						self   :			List[str]     )    ->					Union[str, Any]:


      """simple docstring"""




      return self.time_input_dim * 4
 @property
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




      return 8
 @property
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Optional[Any]:


      """simple docstring"""




      torch.manual_seed(0     )
      __SCREAMING_SNAKE_CASE      = 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      = CLIPVisionModel(__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = CLIPImageProcessor(
          crop_size=224   ,				do_center_crop=__SCREAMING_SNAKE_CASE   ,				do_normalize=__SCREAMING_SNAKE_CASE   ,				do_resize=__SCREAMING_SNAKE_CASE   ,				image_mean=[0.48145466, 0.4578275, 0.40821073]   ,				image_std=[0.26862954, 0.26130258, 0.27577711]   ,				resample=3   ,				size=224   ,				)

      return image_processor
 @property
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Tuple:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """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      = PriorTransformer(**__SCREAMING_SNAKE_CASE     )
      return model
 @property
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      torch.manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = {
          """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      = ShapERenderer(**__SCREAMING_SNAKE_CASE     )
      return model
 def  UpperCAmelCase__						(						self   :			str     )    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.dummy_prior
      __SCREAMING_SNAKE_CASE      = self.dummy_image_encoder
      __SCREAMING_SNAKE_CASE      = self.dummy_image_processor
      __SCREAMING_SNAKE_CASE      = self.dummy_renderer

      __SCREAMING_SNAKE_CASE      = HeunDiscreteScheduler(
          beta_schedule="""exp"""   ,				num_train_timesteps=1_024   ,				prediction_type="""sample"""   ,				use_karras_sigmas=__SCREAMING_SNAKE_CASE   ,				clip_sample=__SCREAMING_SNAKE_CASE   ,				clip_sample_range=1.0   ,				)
      __SCREAMING_SNAKE_CASE      = {
          """prior""": prior,
          """image_encoder""": image_encoder,
          """image_processor""": image_processor,
          """renderer""": renderer,
          """scheduler""": scheduler,
      }

      return components
 def  UpperCAmelCase__						(						self   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			str=0     )    ->					Tuple:


      """simple docstring"""




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

      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = {
          """image""": input_image,
          """generator""": generator,
          """num_inference_steps""": 1,
          """frame_size""": 32,
          """output_type""": """np""",
      }
      return inputs
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = """cpu"""

      __SCREAMING_SNAKE_CASE      = self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      = self.pipeline_class(**__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = pipe.to(__SCREAMING_SNAKE_CASE     )

      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE     )

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

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

      __SCREAMING_SNAKE_CASE      = 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  UpperCAmelCase__						(						self   :			str     )    ->					Dict:


      """simple docstring"""




      self._test_inference_batch_consistent(batch_sizes=[1, 2]     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					str:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Tuple     )    ->					Optional[Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 1
      __SCREAMING_SNAKE_CASE      = 2

      __SCREAMING_SNAKE_CASE      = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE     )

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

      __SCREAMING_SNAKE_CASE      = pipe(**__SCREAMING_SNAKE_CASE   ,				num_images_per_prompt=__SCREAMING_SNAKE_CASE     )[0]

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







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



 """simple docstring"""



 def  UpperCAmelCase__						(						self   :			int     )    ->					Dict:


      """simple docstring"""




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


 def  UpperCAmelCase__						(						self   :			Optional[Any]     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(0     )

      __SCREAMING_SNAKE_CASE      = pipe(
          __SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				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(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )






