'''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 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Optional[Any]      =       KandinskyVaaImgaImgPipeline
			UpperCAmelCase_			:     Any      =       ['''image_embeds''', '''negative_image_embeds''', '''image''']
			UpperCAmelCase_			:     Optional[int]      =       [
			    '''image_embeds''',
			    '''negative_image_embeds''',
			    '''image''',
			]
			UpperCAmelCase_			:     Optional[Any]      =       [
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''strength''',
			    '''guidance_scale''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			UpperCAmelCase_			:     Union[str, Any]      =       False



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 32



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 32



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.time_input_dim



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self.time_input_dim * 4



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return 100



			@property
			def 							a_       (   self):




					"""simple docstring"""


					torch.manual_seed(0)

					lowerCAmelCase   					=  {
					    """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,
					}

					lowerCAmelCase   					=  UNetaDConditionModel(**__lowerCAmelCase)
					return model



			@property
			def 							a_       (   self):




					"""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 							a_       (   self):




					"""simple docstring"""


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



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.dummy_unet
					lowerCAmelCase   					=  self.dummy_movq

					lowerCAmelCase   					=  {
					    """num_train_timesteps""": 1000,
					    """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,
					}

					lowerCAmelCase   					=  DDIMScheduler(**__lowerCAmelCase)

					lowerCAmelCase   					=  {
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """movq""": movq,
					}

					return components



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase=0):




					"""simple docstring"""


					lowerCAmelCase   					=  floats_tensor((1, self.text_embedder_hidden_size)       ,    rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase   					=  floats_tensor((1, self.text_embedder_hidden_size)       ,    rng=random.Random(seed + 1)).to(
					    __lowerCAmelCase)
					# create init_image
					lowerCAmelCase   					=  floats_tensor((1, 3, 64, 64)       ,    rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
					lowerCAmelCase   					=  image.cpu().permute(0       ,    2       ,    3       ,    1)[0]
					lowerCAmelCase   					=  Image.fromarray(np.uinta(__lowerCAmelCase)).convert("""RGB""").resize((256, 256))

					if str(__lowerCAmelCase).startswith("""mps"""):
							lowerCAmelCase   					=  torch.manual_seed(__lowerCAmelCase)
					else:
							lowerCAmelCase   					=  torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
					lowerCAmelCase   					=  {
					    """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):




					"""simple docstring"""


					lowerCAmelCase   					=  """cpu"""

					lowerCAmelCase   					=  self.get_dummy_components()

					lowerCAmelCase   					=  self.pipeline_class(**__lowerCAmelCase)
					lowerCAmelCase   					=  pipe.to(__lowerCAmelCase)

					pipe.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  pipe(**self.get_dummy_inputs(__lowerCAmelCase))
					lowerCAmelCase   					=  output.images

					lowerCAmelCase   					=  pipe(
					    **self.get_dummy_inputs(__lowerCAmelCase)       ,    return_dict=__lowerCAmelCase       ,    )[0]

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

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

					lowerCAmelCase   					=  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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


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







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  load_numpy(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/kandinskyv22/kandinskyv22_img2img_frog.npy""")

					lowerCAmelCase   					=  load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
					lowerCAmelCase   					=  """A red cartoon frog, 4k"""

					lowerCAmelCase   					=  KandinskyVaaPriorPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-prior"""       ,    torch_dtype=torch.floataa)
					pipe_prior.to(__lowerCAmelCase)

					lowerCAmelCase   					=  KandinskyVaaImgaImgPipeline.from_pretrained(
					    """kandinsky-community/kandinsky-2-2-decoder"""       ,    torch_dtype=torch.floataa)
					lowerCAmelCase   					=  pipeline.to(__lowerCAmelCase)

					pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

					lowerCAmelCase   					=  torch.Generator(device="""cpu""").manual_seed(0)
					lowerCAmelCase					,     lowerCAmelCase   					=  pipe_prior(
					    __lowerCAmelCase       ,    generator=__lowerCAmelCase       ,    num_inference_steps=5       ,    negative_prompt=""""""       ,    ).to_tuple()

					lowerCAmelCase   					=  pipeline(
					    image=__lowerCAmelCase       ,    image_embeds=__lowerCAmelCase       ,    negative_image_embeds=__lowerCAmelCase       ,    generator=__lowerCAmelCase       ,    num_inference_steps=100       ,    height=768       ,    width=768       ,    strength=0.2       ,    output_type="""np"""       ,    )

					lowerCAmelCase   					=  output.images[0]

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

					assert_mean_pixel_difference(__lowerCAmelCase       ,    __lowerCAmelCase)





'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




