






import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
 from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image







class 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=64    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=37    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=10    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=[1, 16, 4, 4]    ,	_lowerCAmelCase=None    ,	):



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

          # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          # the number of patches is based on the feature map of the backbone, which by default uses an output stride
          # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
          lowerCAmelCase__   :str      							=				(self.image_size // 32) ** 2
          lowerCAmelCase__   :Any      							=				num_patches + 1
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

          lowerCAmelCase__   :int      							=				self.get_config()

          return config, pixel_values, labels
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				{
              "global_padding": "same",
              "layer_type": "bottleneck",
              "depths": [3, 4, 9],
              "out_features": ["stage1", "stage2", "stage3"],
              "embedding_dynamic_padding": True,
              "hidden_sizes": [4, 8, 16, 32],
              "num_groups": 2,
          }

          return ViTHybridConfig(
              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    ,	backbone_featmap_shape=self.backbone_featmap_shape    ,	backbone_config=_lowerCAmelCase    ,	)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				ViTHybridModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :int      							=				model(_lowerCAmelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.type_sequence_label_size
          lowerCAmelCase__   :List[str]      							=				ViTHybridForImageClassification(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :Dict      							=				model(_lowerCAmelCase    ,	labels=_lowerCAmelCase		)
          self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.type_sequence_label_size)		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.prepare_config_and_inputs()
          lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__   :int      							=				config_and_inputs
          lowerCAmelCase__   :Optional[int]      							=				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_torch
class 	_UpperCAmelCase  (  _A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      A          =    (
          {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      A          =    False
      A          =    False
      A          =    False
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				ViTHybridModelTester(self		)
          lowerCAmelCase__   :Optional[Any]      							=				ConfigTester(self    ,	config_class=_lowerCAmelCase    ,	has_text_modality=_lowerCAmelCase    ,	hidden_size=37		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.config_tester.run_common_tests()
      @unittest.skip(reason="ViT does not use inputs_embeds"		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          pass
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__							,lowerCAmelCase__   :List[Any]      							=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              lowerCAmelCase__   :Tuple      							=				model_class(_lowerCAmelCase		)
              self.assertIsInstance(model.get_input_embeddings()    ,	(nn.Module)		)
              lowerCAmelCase__   :Dict      							=				model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_lowerCAmelCase    ,	nn.Linear		)		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__							,lowerCAmelCase__   :str      							=				self.model_tester.prepare_config_and_inputs_for_common()

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

              lowerCAmelCase__   :List[str]      							=				["pixel_values"]
              self.assertListEqual(arg_names[:1]    ,	_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__							,lowerCAmelCase__   :Union[str, Any]      							=				self.model_tester.prepare_config_and_inputs_for_common()

          lowerCAmelCase__   :Tuple      							=				_config_zero_init(_lowerCAmelCase		)
          for model_class in self.all_model_classes:
              lowerCAmelCase__   :Any      							=				model_class(config=_lowerCAmelCase		)
              # Skip the check for the backbone
              for name, module in model.named_modules():
                  if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                      lowerCAmelCase__   :List[str]      							=				[f'''{name}.{key}''' for key in module.state_dict().keys()]
                      break

              for name, param in model.named_parameters():
                  if param.requires_grad:
                      if name in backbone_params:
                          continue
                      self.assertIn(
                          ((param.data.mean() * 1e9).round() / 1e9).item()    ,	[0.0, 1.0]    ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,	)



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__   :Optional[int]      							=				ViTHybridModel.from_pretrained(_lowerCAmelCase		)
              self.assertIsNotNone(_lowerCAmelCase		)





def 				snake_case__							(							):
    lowerCAmelCase__   :Optional[Any]      							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image







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




      """simple docstring"""



      @cached_property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return (
              ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
              if is_vision_available()
              else None
          )
      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
              _lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				self.default_image_processor
          lowerCAmelCase__   :List[Any]      							=				prepare_img()
          lowerCAmelCase__   :Any      							=				image_processor(images=_lowerCAmelCase    ,	return_tensors="pt"		).to(_lowerCAmelCase		)

          # forward pass
          with torch.no_grad():
              lowerCAmelCase__   :Tuple      							=				model(**_lowerCAmelCase		)

          # verify the logits
          lowerCAmelCase__   :Any      							=				torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape    ,	_lowerCAmelCase		)

          lowerCAmelCase__   :List[Any]      							=				torch.tensor([-1.9090, -0.4993, -0.2389]		).to(_lowerCAmelCase		)

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



      @slow
      @require_accelerate
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"		)
          lowerCAmelCase__   :Any      							=				ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"    ,	device_map="auto"		)

          lowerCAmelCase__   :int      							=				prepare_img()

          lowerCAmelCase__   :Tuple      							=				image_processor(images=_lowerCAmelCase    ,	return_tensors="pt"		)
          lowerCAmelCase__   :List[str]      							=				model(**_lowerCAmelCase		)
          lowerCAmelCase__   :int      							=				outputs.logits
          # model predicts one of the 1000 ImageNet classes
          lowerCAmelCase__   :str      							=				logits.argmax(-1		).item()

          self.assertTrue(model.config.idalabel[predicted_class_idx]    ,	"tabby, tabby cat"		)











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 	_UpperCAmelCase  (  _A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    ShapEImgaImgPipeline
      A          =    ['''image''']
      A          =    ['''image''']
      A          =    [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      A          =    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		)
          lowerCAmelCase__   :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__   :int      							=				CLIPVisionModel(_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				CLIPImageProcessor(
              crop_size=224    ,	do_center_crop=_lowerCAmelCase    ,	do_normalize=_lowerCAmelCase    ,	do_resize=_lowerCAmelCase    ,	image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]    ,	image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]    ,	resample=3    ,	size=224    ,	)

          return image_processor
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :Any      							=				{
              "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__   :int      							=				PriorTransformer(**_lowerCAmelCase		)
          return model
      @property
      def 			snake_case_      (   self		):



          '''simple docstring'''
          torch.manual_seed(0		)

          lowerCAmelCase__   :List[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__   :str      							=				ShapERenderer(**_lowerCAmelCase		)
          return model
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.dummy_prior
          lowerCAmelCase__   :str      							=				self.dummy_image_encoder
          lowerCAmelCase__   :Optional[Any]      							=				self.dummy_image_processor
          lowerCAmelCase__   :Union[str, Any]      							=				self.dummy_renderer

          lowerCAmelCase__   :str      							=				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__   :Any      							=				{
              "prior": prior,
              "image_encoder": image_encoder,
              "image_processor": image_processor,
              "renderer": renderer,
              "scheduler": scheduler,
          }

          return components
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=0		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)

          if str(_lowerCAmelCase		).startswith("mps"		):
              lowerCAmelCase__   :Dict      							=				torch.manual_seed(_lowerCAmelCase		)
          else:
              lowerCAmelCase__   :str      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
          lowerCAmelCase__   :Tuple      							=				{
              "image": input_image,
              "generator": generator,
              "num_inference_steps": 1,
              "frame_size": 32,
              "output_type": "np",
          }
          return inputs
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				"cpu"

          lowerCAmelCase__   :Union[str, Any]      							=				self.get_dummy_components()

          lowerCAmelCase__   :Union[str, Any]      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[int]      							=				pipe.to(_lowerCAmelCase		)

          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

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

          lowerCAmelCase__   :str      							=				np.array(
              [
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
                  0.0003_9216,
              ]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
          self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				torch_device == "cpu"
          lowerCAmelCase__   :Tuple      							=				True
          self._test_inference_batch_single_identical(
              batch_size=2    ,	test_max_difference=_lowerCAmelCase    ,	relax_max_difference=_lowerCAmelCase    ,	)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.get_dummy_components()
          lowerCAmelCase__   :Tuple      							=				self.pipeline_class(**_lowerCAmelCase		)
          lowerCAmelCase__   :Optional[Any]      							=				pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				1
          lowerCAmelCase__   :List[Any]      							=				2

          lowerCAmelCase__   :List[str]      							=				self.get_dummy_inputs(_lowerCAmelCase		)

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

          lowerCAmelCase__   :Optional[Any]      							=				pipe(**_lowerCAmelCase    ,	num_images_per_prompt=_lowerCAmelCase		)[0]

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







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




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''simple docstring'''
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"		)
          lowerCAmelCase__   :int      							=				load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/shap_e/test_shap_e_img2img_out.npy"		)
          lowerCAmelCase__   :Optional[int]      							=				ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"		)
          lowerCAmelCase__   :Tuple      							=				pipe.to(_lowerCAmelCase		)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

          lowerCAmelCase__   :Any      							=				torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)

          lowerCAmelCase__   :List[Any]      							=				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		)



