






'''simple docstring'''
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
  )
  from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import DeiTImageProcessor






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Optional[int]					,      __lowercase	:							int					,      __lowercase	:							Any=13					,      __lowercase	:							str=30					,      __lowercase	:							str=2					,      __lowercase	:							Optional[int]=3					,      __lowercase	:							List[Any]=True					,      __lowercase	:							Optional[int]=True					,      __lowercase	:							Optional[int]=32					,      __lowercase	:							int=5					,      __lowercase	:							Optional[int]=4					,      __lowercase	:							Any=37					,      __lowercase	:							int="gelu"					,      __lowercase	:							Optional[int]=0.1					,      __lowercase	:							Optional[int]=0.1					,      __lowercase	:							Tuple=10					,      __lowercase	:							Any=0.02					,      __lowercase	:							Optional[Any]=3					,      __lowercase	:							Dict=None					,      __lowercase	:							Dict=2					,      )	->						List[Any]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Tuple									=parent
       SCREAMING_SNAKE_CASE__     :				Tuple									=batch_size
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_size
       SCREAMING_SNAKE_CASE__     :				List[str]									=patch_size
       SCREAMING_SNAKE_CASE__     :				Dict									=num_channels
       SCREAMING_SNAKE_CASE__     :				Dict									=is_training
       SCREAMING_SNAKE_CASE__     :				List[Any]									=use_labels
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Any									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				str									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_act
       SCREAMING_SNAKE_CASE__     :				List[Any]									=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Tuple									=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Any									=type_sequence_label_size
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Tuple									=scope
       SCREAMING_SNAKE_CASE__     :				List[Any]									=encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__     :				Tuple									=num_patches + 2




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Any:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE__     :				Any									=None
       if self.use_labels:
             SCREAMING_SNAKE_CASE__     :				int									=ids_tensor([self.batch_size]					,      self.type_sequence_label_size      )

       SCREAMING_SNAKE_CASE__     :				str									=self.get_config()

       return config, pixel_values, labels




 def      __magic_name__	(      self	:							Any      )	->						Dict:

       '''simple docstring'''





       return DeiTConfig(
           image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      hidden_act=self.hidden_act					,      hidden_dropout_prob=self.hidden_dropout_prob					,      attention_probs_dropout_prob=self.attention_probs_dropout_prob					,      is_decoder=lowerCamelCase__					,      initializer_range=self.initializer_range					,      encoder_stride=self.encoder_stride					,      )




 def      __magic_name__	(      self	:							Dict					,      __lowercase	:							str					,      __lowercase	:							List[str]					,      __lowercase	:							List[str]      )	->						str:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DeiTModel(config=lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				List[str]									=model(lowerCamelCase__      )
       self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def      __magic_name__	(      self	:							int					,      __lowercase	:							List[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							Union[str, Any]      )	->						Optional[int]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				int									=DeiTForMaskedImageModeling(config=lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Dict									=model(lowerCamelCase__      )
       self.parent.assertEqual(
           result.reconstruction.shape					,      (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       SCREAMING_SNAKE_CASE__     :				int									=1
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=DeiTForMaskedImageModeling(lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()

       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       SCREAMING_SNAKE_CASE__     :				Any									=model(lowerCamelCase__      )
       self.parent.assertEqual(result.reconstruction.shape					,      (self.batch_size, 1, self.image_size, self.image_size)      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							str					,      __lowercase	:							Any					,      __lowercase	:							int      )	->						Tuple:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Dict									=self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DeiTForImageClassification(lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Any									=model(lowerCamelCase__					,      labels=lowerCamelCase__      )
       self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       SCREAMING_SNAKE_CASE__     :				List[str]									=1
       SCREAMING_SNAKE_CASE__     :				Tuple									=DeiTForImageClassification(lowerCamelCase__      )
       model.to(lowerCamelCase__      )
       model.eval()

       SCREAMING_SNAKE_CASE__     :				str									=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=model(lowerCamelCase__					,      labels=lowerCamelCase__      )
       self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[Any]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE__    
       )     :				Optional[int]									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				List[Any]									={"pixel_values": pixel_values}
       return config, inputs_dict






@require_torch
class      __SCREAMING_SNAKE_CASE      (			UpperCAmelCase__	,				UpperCAmelCase__	,				unittest.TestCase				):
 snake_case_  							=							(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 snake_case_  							=							(
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=DeiTModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=ConfigTester(self					,      config_class=lowerCamelCase__					,      has_text_modality=lowerCamelCase__					,      hidden_size=37      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Union[str, Any]:

       '''simple docstring'''





       self.config_tester.run_common_tests()




 @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
 def      __magic_name__	(      self	:							int      )	->						List[str]:

       '''simple docstring'''





       pass




 def      __magic_name__	(      self	:							str      )	->						Dict:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model_class(lowerCamelCase__      )
             self.assertIsInstance(model.get_input_embeddings()					,      (nn.Module)      )
             SCREAMING_SNAKE_CASE__     :				Dict									=model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(lowerCamelCase__					,      nn.Linear      )      )




 def      __magic_name__	(      self	:							int      )	->						Dict:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE__     :				Tuple									=["pixel_values"]
             self.assertListEqual(arg_names[:1]					,      lowerCamelCase__      )




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

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				str									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase__      )




 def      __magic_name__	(      self	:							List[str]      )	->						Any:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase__      )




 def      __magic_name__	(      self	:							int      )	->						Optional[int]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				List[str]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__      )




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							List[str]=False      )	->						Optional[int]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				int									=super()._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__					,      return_labels=lowerCamelCase__      )

       if return_labels:
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict




 def      __magic_name__	(      self	:							Tuple      )	->						str:

       '''simple docstring'''





       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=True

       for model_class in self.all_model_classes:
             # DeiTForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(lowerCamelCase__      )
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue
             SCREAMING_SNAKE_CASE__     :				List[str]									=model_class(lowerCamelCase__      )
             model.to(lowerCamelCase__      )
             model.train()
             SCREAMING_SNAKE_CASE__     :				List[str]									=self._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__					,      return_labels=lowerCamelCase__      )
             SCREAMING_SNAKE_CASE__     :				Any									=model(**lowerCamelCase__      ).loss
             loss.backward()




 def      __magic_name__	(      self	:							Dict      )	->						str:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE__     :				int									=False
       SCREAMING_SNAKE_CASE__     :				Tuple									=True

       for model_class in self.all_model_classes:
             if model_class in get_values(lowerCamelCase__      ) or not model_class.supports_gradient_checkpointing:
                   continue
             # DeiTForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   continue
             SCREAMING_SNAKE_CASE__     :				str									=model_class(lowerCamelCase__      )
             model.gradient_checkpointing_enable()
             model.to(lowerCamelCase__      )
             model.train()
             SCREAMING_SNAKE_CASE__     :				str									=self._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__					,      return_labels=lowerCamelCase__      )
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(**lowerCamelCase__      ).loss
             loss.backward()




 def      __magic_name__	(      self	:							List[str]      )	->						List[Any]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[
           {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
           {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
           {"title": "regression", "num_labels": 1, "dtype": torch.float},
       ]

       for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(lowerCamelCase__      ),
                     *get_values(lowerCamelCase__      ),
                 ]
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue

             for problem_type in problem_types:
                   with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"      ):
                         SCREAMING_SNAKE_CASE__     :				Any									=problem_type["title"]
                         SCREAMING_SNAKE_CASE__     :				Dict									=problem_type["num_labels"]

                         SCREAMING_SNAKE_CASE__     :				str									=model_class(lowerCamelCase__      )
                         model.to(lowerCamelCase__      )
                         model.train()

                         SCREAMING_SNAKE_CASE__     :				Tuple									=self._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__					,      return_labels=lowerCamelCase__      )

                         if problem_type["num_labels"] > 1:
                               SCREAMING_SNAKE_CASE__     :				List[Any]									=inputs["labels"].unsqueeze(1      ).repeat(1					,      problem_type['''num_labels''']      )

                         SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=inputs["labels"].to(problem_type['''dtype''']      )

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=lowerCamelCase__      ) as warning_list:
                               SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(**lowerCamelCase__      ).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message      ):
                                     raise ValueError(
                                         F"Something is going wrong in the regression problem: intercepted {w.message}"      )

                         loss.backward()




 @slow
 def      __magic_name__	(      self	:							str      )	->						Optional[int]:

       '''simple docstring'''





       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__     :				int									=DeiTModel.from_pretrained(lowerCamelCase__      )
             self.assertIsNotNone(lowerCamelCase__      )





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[Any]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







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




 @cached_property
 def      __magic_name__	(      self	:							Any      )	->						str:

       '''simple docstring'''





       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
           if is_vision_available()
           else None
       )




 @slow
 def      __magic_name__	(      self	:							str      )	->						List[str]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      ).to(
           lowerCamelCase__      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.default_image_processor
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=prepare_img()
       SCREAMING_SNAKE_CASE__     :				Dict									=image_processor(images=lowerCamelCase__					,      return_tensors='''pt'''      ).to(lowerCamelCase__      )

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**lowerCamelCase__      )

       # verify the logits
       SCREAMING_SNAKE_CASE__     :				int									=torch.Size((1, 10_00)      )
       self.assertEqual(outputs.logits.shape					,      lowerCamelCase__      )

       SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([-1.0266, 0.1912, -1.2861]      ).to(lowerCamelCase__      )

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




 @slow
 @require_accelerate
 @require_torch_gpu
 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:

       '''simple docstring'''





       SCREAMING_SNAKE_CASE__     :				Dict									=DeiTModel.from_pretrained(
           '''facebook/deit-base-distilled-patch16-224'''					,      torch_dtype=torch.floataa					,      device_map='''auto'''      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.default_image_processor

       SCREAMING_SNAKE_CASE__     :				int									=prepare_img()
       SCREAMING_SNAKE_CASE__     :				Dict									=image_processor(images=lowerCamelCase__					,      return_tensors='''pt'''      )
       SCREAMING_SNAKE_CASE__     :				Any									=inputs.pixel_values.to(lowerCamelCase__      )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=model(lowerCamelCase__      )







'''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      )