






import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							ShapEPipeline
	__snake_case						:Optional[int] =							['prompt']
	__snake_case						:str =							['prompt']
	__snake_case						:Tuple =							[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__snake_case						:str =							False







	@property
	def 			_a (	self   :				Optional[int]      )		->			Optional[Any]:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				str      )		->			str:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		return self.time_input_dim * 4







	@property
	def 			_a (	self   :				str      )		->			Union[str, Any]:
		"""simple docstring"""







		return 8







	@property
	def 			_a (	self   :				Union[str, Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )
		return tokenizer







	@property
	def 			_a (	self   :				int      )		->			Optional[int]:
		"""simple docstring"""







		torch.manual_seed(0      )
		__lowercase   							=	CLIPTextConfig(
		    bos_token_id=0							,					eos_token_id=2							,					hidden_size=self.text_embedder_hidden_size							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1000							,					)
		return CLIPTextModelWithProjection(_lowerCAmelCase      )







	@property
	def 			_a (	self   :				Dict      )		->			List[str]:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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""",
		    """encoder_hid_proj_type""": None,
		    """added_emb_type""": None,
		}

		__lowercase   							=	PriorTransformer(**_lowerCAmelCase      )
		return model







	@property
	def 			_a (	self   :				Any      )		->			int:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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,
		    ),
		}
		__lowercase   							=	ShapERenderer(**_lowerCAmelCase      )
		return model







	def 			_a (	self   :				List[Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	self.dummy_prior
		__lowercase   							=	self.dummy_text_encoder
		__lowercase   							=	self.dummy_tokenizer
		__lowercase   							=	self.dummy_renderer

		__lowercase   							=	HeunDiscreteScheduler(
		    beta_schedule="""exp"""							,					num_train_timesteps=1024							,					prediction_type="""sample"""							,					use_karras_sigmas=_lowerCAmelCase							,					clip_sample=_lowerCAmelCase							,					clip_sample_range=1.0							,					)
		__lowercase   							=	{
		    """prior""": prior,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """renderer""": renderer,
		    """scheduler""": scheduler,
		}

		return components







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Union[str, Any]=0      )		->			Dict:
		"""simple docstring"""







		if str(_lowerCAmelCase      ).startswith("""mps"""      ):
			__lowercase   							=	torch.manual_seed(_lowerCAmelCase      )
		else:
			__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
		__lowercase   							=	{
		    """prompt""": """horse""",
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs







	def 			_a (	self   :				str      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""

		__lowercase   							=	self.get_dummy_components()

		__lowercase   							=	self.pipeline_class(**_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )

		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

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

		__lowercase   							=	np.array(
		    [
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		        0.00_039_216,
		    ]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







	def 			_a (	self   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict      )		->			int:
		"""simple docstring"""







		__lowercase   							=	torch_device == """cpu"""
		__lowercase   							=	True

		self._test_inference_batch_single_identical(
		    batch_size=2							,					test_max_difference=_lowerCAmelCase							,					relax_max_difference=_lowerCAmelCase							,					)







	def 			_a (	self   :				Any      )		->			str:
		"""simple docstring"""







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

		__lowercase   							=	1
		__lowercase   							=	2

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )

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

		__lowercase   							=	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 ):







	def 			_a (	self   :				Dict      )		->			List[str]:
		"""simple docstring"""







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







	def 			_a (	self   :				Any      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/shap_e/test_shap_e_np_out.npy"""      )
		__lowercase   							=	ShapEPipeline.from_pretrained("""openai/shap-e"""      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )

		__lowercase   							=	pipe(
		    """a shark"""							,					generator=_lowerCAmelCase							,					guidance_scale=15.0							,					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      )









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    KandinskyVaaInpaintPipeline
      __snake_case	    =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      __snake_case	    =    [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      __snake_case	    =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case	    =    False







      @property
      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->Tuple:




       """simple docstring"""


       return 32







      @property
      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Dict:




       """simple docstring"""


       return 32







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




       """simple docstring"""


       return self.time_input_dim







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[Any]:




       """simple docstring"""


       return self.time_input_dim * 4







      @property
      def  __lowerCAmelCase   ( self :		Any    )       ->Optional[int]:




       """simple docstring"""


       return 100







      @property
      def  __lowerCAmelCase   ( self :		Tuple    )       ->Optional[int]:




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       a						    = UNetaDConditionModel(**__UpperCAmelCase    )
       return model







      @property
      def  __lowerCAmelCase   ( self :		List[Any]    )       ->List[Any]:




       """simple docstring"""


       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }







      @property
      def  __lowerCAmelCase   ( self :		str    )       ->List[Any]:




       """simple docstring"""


       torch.manual_seed(0    )
       a						    = VQModel(**self.dummy_movq_kwargs    )
       return model







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = DDIMScheduler(
           num_train_timesteps=1_000					,   beta_schedule='''linear'''					,   beta_start=0.00085					,   beta_end=0.012					,   clip_sample=__UpperCAmelCase					,   set_alpha_to_one=__UpperCAmelCase					,   steps_offset=1					,   prediction_type='''epsilon'''					,   thresholding=__UpperCAmelCase					,   )

       a						    = {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int=0    )       ->Optional[int]:




       """simple docstring"""


       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1    )    ).to(
           __UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
       # create mask
       a						    = np.ones((64, 64)					,   dtype=np.floataa    )
       a						    = 0

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

       a						    = image[0, -3:, -3:, -1]
       a						    = image_from_tuple[0, -3:, -3:, -1]

       print(F"""image.shape {image.shape}"""    )

       assert image.shape == (1, 64, 64, 3)

       a						    = np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

       assert (
           np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
       ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







      def  __lowerCAmelCase   ( self :		Union[str, Any]    )       ->Optional[int]:




       """simple docstring"""


       super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Any    )       ->List[Any]:




       """simple docstring"""


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







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




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''    )

       a						    = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
       a						    = np.ones((768, 768)					,   dtype=np.floataa    )
       a						    = 0

       a						    = '''a hat'''

       a						    = KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,   torch_dtype=torch.floataa    )
       pipe_prior.to(__UpperCAmelCase    )

       a						    = KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,   torch_dtype=torch.floataa    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a    ,	a						    = pipe_prior(
           __UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=5					,   negative_prompt=''''''					,   ).to_tuple()

       a						    = pipeline(
           image=__UpperCAmelCase					,   mask_image=__UpperCAmelCase					,   image_embeds=__UpperCAmelCase					,   negative_image_embeds=__UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=100					,   height=768					,   width=768					,   output_type='''np'''					,   )

       a						    = output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )


