






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 					_UpperCAmelCase       ( _lowerCamelCase   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   KandinskyVaaImgaImgPipeline
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   ["image_embeds", "negative_image_embeds", "image"]
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   [
					    "image_embeds",
					    "negative_image_embeds",
					    "image",
					]
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   [
					    "generator",
					    "height",
					    "width",
					    "strength",
					    "guidance_scale",
					    "num_inference_steps",
					    "return_dict",
					    "guidance_scale",
					    "num_images_per_prompt",
					    "output_type",
					    "return_dict",
					]
					SCREAMING_SNAKE_CASE_		:				str							    =   False




					@property
					def   A  (				self     :						Optional[Any]   )  ->  Tuple:
							return 32




					@property
					def   A  (				self     :						Optional[int]   )  ->  Tuple:
							return 32




					@property
					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							return self.time_input_dim




					@property
					def   A  (				self     :						Tuple   )  ->  List[Any]:
							return self.time_input_dim * 4




					@property
					def   A  (				self     :						int   )  ->  Tuple:
							return 1_00




					@property
					def   A  (				self     :						Any   )  ->  Optional[Any]:
							torch.manual_seed(0   )

							lowercase_ : List[Any]							       =   {
							    """in_channels""": 4,
							    # 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,
							}

							lowercase_ : Tuple							       =   UNetaDConditionModel(**a_   )
							return model




					@property
					def   A  (				self     :						Union[str, Any]   )  ->  Tuple:
							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   A  (				self     :						List[str]   )  ->  Any:
							torch.manual_seed(0   )
							lowercase_ : Optional[Any]							       =   VQModel(**self.dummy_movq_kwargs   )
							return model




					def   A  (				self     :						str   )  ->  Optional[Any]:
							lowercase_ : Optional[int]							       =   self.dummy_unet
							lowercase_ : List[Any]							       =   self.dummy_movq

							lowercase_ : Optional[int]							       =   {
							    """num_train_timesteps""": 10_00,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.00085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowercase_ : Dict							       =   DDIMScheduler(**a_   )

							lowercase_ : int							       =   {
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




					def   A  (				self     :						Tuple    ,						A     :						List[Any]    ,						A     :						Dict=0   )  ->  Dict:
							lowercase_ : Optional[int]							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(a_   )   ).to(a_   )
							lowercase_ : Optional[int]							       =   floats_tensor((1, self.text_embedder_hidden_size)    ,						rng=random.Random(seed + 1   )   ).to(
							    a_   )
							# create init_image
							lowercase_ : Optional[Any]							       =   floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(a_   )   ).to(a_   )
							lowercase_ : str							       =   image.cpu().permute(0    ,						2    ,						3    ,						1   )[0]
							lowercase_ : Tuple							       =   Image.fromarray(np.uinta(a_   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )

							if str(a_   ).startswith('''mps'''   ):
									lowercase_ : Optional[int]							       =   torch.manual_seed(a_   )
							else:
									lowercase_ : Tuple							       =   torch.Generator(device=a_   ).manual_seed(a_   )
							lowercase_ : Optional[int]							       =   {
							    """image""": init_image,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 10,
							    """guidance_scale""": 7.0,
							    """strength""": 0.2,
							    """output_type""": """np""",
							}
							return inputs






					def   A  (				self     :						Optional[Any]   )  ->  Dict:
							lowercase_ : Dict							       =   """cpu"""

							lowercase_ : int							       =   self.get_dummy_components()

							lowercase_ : Optional[Any]							       =   self.pipeline_class(**a_   )
							lowercase_ : List[Any]							       =   pipe.to(a_   )

							pipe.set_progress_bar_config(disable=a_   )

							lowercase_ : List[str]							       =   pipe(**self.get_dummy_inputs(a_   )   )
							lowercase_ : Optional[int]							       =   output.images

							lowercase_ : Tuple							       =   pipe(
							    **self.get_dummy_inputs(a_   )    ,						return_dict=a_    ,						)[0]

							lowercase_ : Tuple							       =   image[0, -3:, -3:, -1]
							lowercase_ : Optional[int]							       =   image_from_tuple[0, -3:, -3:, -1]

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

							lowercase_ : Optional[int]							       =   np.array(
							    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]   )
							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 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						Any   )  ->  List[str]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






					def   A  (				self     :						int   )  ->  Dict:
							lowercase_ : Union[str, Any]							       =   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''   )

							lowercase_ : Optional[Any]							       =   load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
							lowercase_ : Optional[int]							       =   """A red cartoon frog, 4k"""

							lowercase_ : Dict							       =   KandinskyVaaPriorPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''    ,						torch_dtype=torch.floataa   )
							pipe_prior.to(a_   )

							lowercase_ : Optional[Any]							       =   KandinskyVaaImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-decoder'''    ,						torch_dtype=torch.floataa   )
							lowercase_ : Any							       =   pipeline.to(a_   )

							pipeline.set_progress_bar_config(disable=a_   )

							lowercase_ : Dict							       =   torch.Generator(device='''cpu'''   ).manual_seed(0   )
							lowercase_ : Any							       =   pipe_prior(
							    a_    ,						generator=a_    ,						num_inference_steps=5    ,						negative_prompt=''''''    ,						).to_tuple()

							lowercase_ : List[str]							       =   pipeline(
							    image=a_    ,						image_embeds=a_    ,						negative_image_embeds=a_    ,						generator=a_    ,						num_inference_steps=1_00    ,						height=7_68    ,						width=7_68    ,						strength=0.2    ,						output_type='''np'''    ,						)

							lowercase_ : Optional[int]							       =   output.images[0]

							assert image.shape == (7_68, 7_68, 3)

							assert_mean_pixel_difference(a_    ,						a_   )








'''simple docstring'''






from numpy import exp, pi, sqrt




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = 0.0       ,   lowerCAmelCase_ = 1.0		)->		int:





       '''simple docstring'''


       return 1 / sqrt(2 * pi * sigma**2		) * exp(-((x - mu) ** 2) / (2 * sigma**2)		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



