





import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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    lowerCAmelCase							(					__UpperCamelCase,  unittest.TestCase			):
 UpperCAmelCase__													=		KandinskyVaaControlnetPipeline
 UpperCAmelCase__													=		["""image_embeds""", """negative_image_embeds""", """hint"""]
 UpperCAmelCase__													=		["""image_embeds""", """negative_image_embeds""", """hint"""]
 UpperCAmelCase__													=		[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase__													=		False
 @property
 def      A_	(      self  :      Union[str, Any]	)			->	List[Any]:
     return 32
 @property
 def      A_	(      self  :      Optional[int]	)			->	str:
     return 32
 @property
 def      A_	(      self  :      str	)			->	Tuple:
     return self.time_input_dim
 @property
 def      A_	(      self  :      int	)			->	Any:
     return self.time_input_dim * 4
 @property
 def      A_	(      self  :      List[str]	)			->	int:
     return 100
 @property
 def      A_	(      self  :      int	)			->	Union[str, Any]:
     torch.manual_seed(0	)

     lowerCamelCase__    :      List[str]					 =		{
         'in_channels': 8,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'image_hint',
         '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,
     }

     lowerCamelCase__    :      Dict					 =		UNetaDConditionModel(**UpperCAmelCase	)
     return model
 @property
 def      A_	(      self  :      Any	)			->	Optional[int]:
     return {
         "block_out_channels": [32, 32, 64, 64],
         "down_block_types": [
             "DownEncoderBlock2D",
             "DownEncoderBlock2D",
             "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
         "vq_embed_dim": 4,
     }
 @property
 def      A_	(      self  :      Union[str, Any]	)			->	List[str]:
     torch.manual_seed(0	)
     lowerCamelCase__    :      Optional[Any]					 =		VQModel(**self.dummy_movq_kwargs	)
     return model
 def      A_	(      self  :      Tuple	)			->	Dict:
     lowerCamelCase__    :      Optional[Any]					 =		self.dummy_unet
     lowerCamelCase__    :      str					 =		self.dummy_movq

     lowerCamelCase__    :      Optional[Any]					 =		DDIMScheduler(
         num_train_timesteps=1000			,     beta_schedule='linear'			,     beta_start=0.0_0_0_8_5			,     beta_end=0.0_1_2			,     clip_sample=UpperCAmelCase			,     set_alpha_to_one=UpperCAmelCase			,     steps_offset=1			,     prediction_type='epsilon'			,     thresholding=UpperCAmelCase			,     )

     lowerCamelCase__    :      Optional[Any]					 =		{
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components
 def      A_	(      self  :      str			,     UpperCAmelCase  :      Tuple			,     UpperCAmelCase  :      str=0	)			->	List[Any]:
     lowerCamelCase__    :      Any					 =		floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(UpperCAmelCase	)	).to(UpperCAmelCase	)
     lowerCamelCase__    :      Dict					 =		floats_tensor((1, self.text_embedder_hidden_size)			,     rng=random.Random(seed + 1	)	).to(
         UpperCAmelCase	)

     # create hint
     lowerCamelCase__    :      Any					 =		floats_tensor((1, 3, 64, 64)			,     rng=random.Random(UpperCAmelCase	)	).to(UpperCAmelCase	)

     if str(UpperCAmelCase	).startswith('mps'	):
         lowerCamelCase__    :      Dict					 =		torch.manual_seed(UpperCAmelCase	)
     else:
         lowerCamelCase__    :      List[str]					 =		torch.Generator(device=UpperCAmelCase	).manual_seed(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		{
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'hint': hint,
         'generator': generator,
         'height': 64,
         'width': 64,
         'guidance_scale': 4.0,
         'num_inference_steps': 2,
         'output_type': 'np',
     }
     return inputs







 def      A_	(      self  :      List[Any]	)			->	Any:
     lowerCamelCase__    :      int					 =		'cpu'

     lowerCamelCase__    :      List[str]					 =		self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      Dict					 =		pipe(**self.get_dummy_inputs(UpperCAmelCase	)	)
     lowerCamelCase__    :      Optional[int]					 =		output.images

     lowerCamelCase__    :      Union[str, Any]					 =		pipe(
         **self.get_dummy_inputs(UpperCAmelCase	)			,     return_dict=UpperCAmelCase			,     )[0]

     lowerCamelCase__    :      List[Any]					 =		image[0, -3:, -3:, -1]
     lowerCamelCase__    :      Dict					 =		image_from_tuple[0, -3:, -3:, -1]

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

     lowerCamelCase__    :      Dict					 =		np.array(
         [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]	)

     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()}"""


@slow
@require_torch_gpu
class    lowerCAmelCase							(					unittest.TestCase			):
 def      A_	(      self  :      Optional[int]	)			->	Optional[int]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







 def      A_	(      self  :      Optional[int]	)			->	Optional[Any]:
     lowerCamelCase__    :      int					 =		load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'	)

     lowerCamelCase__    :      Tuple					 =		load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/hint_image_cat.png'	)
     lowerCamelCase__    :      str					 =		torch.from_numpy(np.array(UpperCAmelCase	)	).float() / 2_5_5.0
     lowerCamelCase__    :      Optional[Any]					 =		hint.permute(2			,     0			,     1	).unsqueeze(0	)

     lowerCamelCase__    :      Optional[Any]					 =		KandinskyVaaPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-prior'			,     torch_dtype=torch.floataa	)
     pipe_prior.to(UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		KandinskyVaaControlnetPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-controlnet-depth'			,     torch_dtype=torch.floataa	)
     lowerCamelCase__    :      str					 =		pipeline.to(UpperCAmelCase	)
     pipeline.set_progress_bar_config(disable=UpperCAmelCase	)

     lowerCamelCase__    :      Union[str, Any]					 =		'A robot, 4k photo'

     lowerCamelCase__    :      int					 =		torch.Generator(device='cuda'	).manual_seed(0	)
     lowerCamelCase__ ,							lowerCamelCase__    :      Tuple					 =		pipe_prior(
         UpperCAmelCase			,     generator=UpperCAmelCase			,     num_inference_steps=5			,     negative_prompt=''			,     ).to_tuple()

     lowerCamelCase__    :      Union[str, Any]					 =		torch.Generator(device='cuda'	).manual_seed(0	)
     lowerCamelCase__    :      List[Any]					 =		pipeline(
         image_embeds=UpperCAmelCase			,     negative_image_embeds=UpperCAmelCase			,     hint=UpperCAmelCase			,     generator=UpperCAmelCase			,     num_inference_steps=100			,     output_type='np'			,     )

     lowerCamelCase__    :      List[str]					 =		output.images[0]

     assert image.shape == (512, 512, 3)

     assert_mean_pixel_difference(UpperCAmelCase			,     UpperCAmelCase	)








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowercase_       		=  False


@skip_mps
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							StableDiffusionAttendAndExcitePipeline
    lowerCamelCase			=							False
    lowerCamelCase			=							TEXT_TO_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS
    lowerCamelCase			=							TEXT_TO_IMAGE_IMAGE_PARAMS


    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[Any]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


    def 						snake_case__     (      self :       List[str]		)->    int:





        '''simple docstring'''







        torch.manual_seed(0		)
        A__          =   UNetaDConditionModel(
            block_out_channels=(3_2, 6_4),layers_per_block=1,sample_size=3_2,in_channels=4,out_channels=4,down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D'),up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D'),cross_attention_dim=3_2,attention_head_dim=(2, 4),use_linear_projection=lowercase_,)
        A__          =   DDIMScheduler(
            beta_start=0.00_085,beta_end=0.012,beta_schedule='scaled_linear',clip_sample=lowercase_,set_alpha_to_one=lowercase_,)
        torch.manual_seed(0		)
        A__          =   AutoencoderKL(
            block_out_channels=[3_2, 6_4],in_channels=3,out_channels=3,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],latent_channels=4,sample_size=1_2_8,)
        torch.manual_seed(0		)
        A__          =   CLIPTextConfig(
            bos_token_id=0,eos_token_id=2,hidden_size=3_2,intermediate_size=3_7,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1_0_0_0,hidden_act='gelu',projection_dim=5_1_2,)
        A__          =   CLIPTextModel(lowercase_		)
        A__          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

        A__          =   {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }

        return components


    def 						snake_case__     (      self :       Tuple,lowercase_ :       str,lowercase_ :       List[Any]=0		)->    int:





        '''simple docstring'''







        if str(lowercase_		).startswith('mps'		):
            A__          =   torch.manual_seed(lowercase_		)
        else:
            A__          =   torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__          =   A__          =   {
            'prompt': 'a cat and a frog',
            'token_indices': [2, 5],
            'generator': generator,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
            'max_iter_to_alter': 2,
            'thresholds': {0: 0.7},
        }
        return inputs


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





        '''simple docstring'''







        A__          =   'cpu'

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

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

        self.assertEqual(image.shape,(1, 6_4, 6_4, 3)		)
        A__          =   np.array(
            [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]		)
        A__          =   np.abs(image_slice.flatten() - expected_slice		).max()
        self.assertLessEqual(lowercase_,1E-3		)


    def 						snake_case__     (      self :       str		)->    Optional[Any]:





        '''simple docstring'''







        super().test_cpu_offload_forward_pass(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       str		)->    Optional[int]:





        '''simple docstring'''







        self._test_inference_batch_single_identical(batch_size=2,expected_max_diff=7E-4		)


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





        '''simple docstring'''







        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)


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





        '''simple docstring'''







        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    Any:





        '''simple docstring'''







        super().test_save_load_local(expected_max_difference=5E-4		)


    def 						snake_case__     (      self :       Dict		)->    List[str]:





        '''simple docstring'''







        super().test_save_load_optional_components(expected_max_difference=4E-4		)




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







    """simple docstring"""





    @classmethod
    def 						snake_case__     (      cls :       Any		)->    Optional[int]:





        '''simple docstring'''







        super().setUpClass()
        torch.use_deterministic_algorithms(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       int		)->    List[Any]:





        '''simple docstring'''







        super().tearDownClass()
        torch.use_deterministic_algorithms(lowercase_		)


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





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__          =   torch.manual_seed(5_1		)

        A__          =   StableDiffusionAttendAndExcitePipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4',safety_checker=lowercase_,torch_dtype=torch.floataa		)
        pipe.to('cuda'		)

        A__          =   'a painting of an elephant with glasses'
        A__          =   [5, 7]

        A__          =   pipe(
            prompt=lowercase_,token_indices=lowercase_,guidance_scale=7.5,generator=lowercase_,num_inference_steps=5,max_iter_to_alter=5,output_type='numpy',).images[0]

        A__          =   load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'		)
        assert np.abs((expected_image - image).max()		) < 5E-1

