


"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Tuple													=KandinskyVaaControlnetImgaImgPipeline
     snake_case__	:					List[Any]													=['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     snake_case__	:					Dict													=['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
     snake_case__	:					Optional[Any]													=[
         '''generator''',
         '''height''',
         '''width''',
         '''strength''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     snake_case__	:					List[Any]													=False

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

     @property
     def 		a__						(	self:						int	)->    List[Any]:
           return 32

     @property
     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           return self.time_input_dim

     @property
     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           return self.time_input_dim * 4

     @property
     def 		a__						(	self:						Tuple	)->    Any:
           return 100

     @property
     def 		a__						(	self:						Dict	)->    int:
           torch.manual_seed(0	)

           lowerCamelCase					:					Tuple        =	{
               """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					:					Optional[Any]        =	UNetaDConditionModel(**__a	)
           return model

     @property
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           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:						str	)->    str:
           torch.manual_seed(0	)
           lowerCamelCase					:					Union[str, Any]        =	VQModel(**self.dummy_movq_kwargs	)
           return model

     def 		a__						(	self:						List[str]	)->    int:
           lowerCamelCase					:					str        =	self.dummy_unet
           lowerCamelCase					:					Tuple        =	self.dummy_movq

           lowerCamelCase					:					List[Any]        =	{
               """num_train_timesteps""": 1_000,
               """beta_schedule""": """linear""",
               """beta_start""": 0.0_00_85,
               """beta_end""": 0.0_12,
               """clip_sample""": False,
               """set_alpha_to_one""": False,
               """steps_offset""": 0,
               """prediction_type""": """epsilon""",
               """thresholding""": False,
           }

           lowerCamelCase					:					str        =	DDIMScheduler(**__a	)

           lowerCamelCase					:					str        =	{
               """unet""": unet,
               """scheduler""": scheduler,
               """movq""": movq,
           }

           return components

     def 		a__						(	self:						Tuple     ,		__a:						List[str]     ,		__a:						Optional[Any]=0	)->    int:
           lowerCamelCase					:					Tuple        =	floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					Dict        =	floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(seed + 1	)	).to(
               __a	)
           # create init_image
           lowerCamelCase					:					Optional[Any]        =	floats_tensor((1, 3, 64, 64)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					Any        =	image.cpu().permute(0     ,		2     ,		3     ,		1	)[0]
           lowerCamelCase					:					Tuple        =	Image.fromarray(np.uinta(__a	)	).convert("""RGB"""	).resize((256, 256)	)
           # create hint
           lowerCamelCase					:					Tuple        =	floats_tensor((1, 3, 64, 64)     ,		rng=random.Random(__a	)	).to(__a	)

           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Optional[int]        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					Optional[int]        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Any        =	{
               """image""": init_image,
               """image_embeds""": image_embeds,
               """negative_image_embeds""": negative_image_embeds,
               """hint""": hint,
               """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:						Dict	)->    Optional[int]:
           lowerCamelCase					:					str        =	"""cpu"""

           lowerCamelCase					:					Optional[int]        =	self.get_dummy_components()

           lowerCamelCase					:					Union[str, Any]        =	self.pipeline_class(**__a	)
           lowerCamelCase					:					Optional[Any]        =	pipe.to(__a	)

           pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Union[str, Any]        =	pipe(**self.get_dummy_inputs(__a	)	)
           lowerCamelCase					:					Tuple        =	output.images

           lowerCamelCase					:					List[Any]        =	pipe(
               **self.get_dummy_inputs(__a	)     ,		return_dict=__a     ,		)[0]

           lowerCamelCase					:					List[str]        =	image[0, -3:, -3:, -1]
           lowerCamelCase					:					int        =	image_from_tuple[0, -3:, -3:, -1]

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

           lowerCamelCase					:					List[str]        =	np.array(
               [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]	)
           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 			A__	(						unittest.TestCase):


     """simple docstring"""

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







     def 		a__						(	self:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Dict        =	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""	)

           lowerCamelCase					:					Any        =	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
           lowerCamelCase					:					int        =	init_image.resize((512, 512)	)

           lowerCamelCase					:					Optional[int]        =	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/kandinskyv22/hint_image_cat.png"""	)
           lowerCamelCase					:					Optional[Any]        =	torch.from_numpy(np.array(__a	)	).float() / 2_55.0
           lowerCamelCase					:					Optional[Any]        =	hint.permute(2     ,		0     ,		1	).unsqueeze(0	)

           lowerCamelCase					:					List[Any]        =	"""A robot, 4k photo"""

           lowerCamelCase					:					Dict        =	KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-prior"""     ,		torch_dtype=torch.floataa	)
           pipe_prior.to(__a	)

           lowerCamelCase					:					Dict        =	KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
               """kandinsky-community/kandinsky-2-2-controlnet-depth"""     ,		torch_dtype=torch.floataa	)
           lowerCamelCase					:					Any        =	pipeline.to(__a	)

           pipeline.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[int]        =	torch.Generator(device="""cpu"""	).manual_seed(0	)

           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	pipe_prior(
               __a     ,		image=__a     ,		strength=0.85     ,		generator=__a     ,		negative_prompt=""""""     ,		).to_tuple()

           lowerCamelCase					:					List[Any]        =	pipeline(
               image=__a     ,		image_embeds=__a     ,		negative_image_embeds=__a     ,		hint=__a     ,		generator=__a     ,		num_inference_steps=100     ,		height=512     ,		width=512     ,		strength=0.5     ,		output_type="""np"""     ,		)

           lowerCamelCase					:					List[Any]        =	output.images[0]

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

           assert_mean_pixel_difference(__a     ,		__a	)








"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp


class 			A__	(						unittest.TestCase):


     """simple docstring"""

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	[10, 20, 30, 40, 50, 60]
           lowerCamelCase					:					Union[str, Any]        =	[2, 4, 6, 8, 10, 12]
           lowerCamelCase					:					Union[str, Any]        =	100
           self.assertEqual(kp.calc_profit(__a     ,		__a     ,		__a	)     ,		210	)

     def 		a__						(	self:						str	)->    str:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)

     def 		a__						(	self:						str	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""Weight can not be negative."""	)

     def 		a__						(	self:						Any	)->    Dict:
           self.assertRaisesRegex(__a     ,		"""Profit can not be negative."""	)

     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           self.assertRaisesRegex(__a     ,		"""max_weight must greater than zero."""	)







     def 		a__						(	self:						Optional[Any]	)->    Tuple:
           self.assertRaisesRegex(
               __a     ,		"""The length of profit and weight must be same."""	)


if __name__ == "__main__":
    unittest.main()




