





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        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       int      					=KandinskyVaaImgaImgPipeline
  a_							:       int      					=["""image_embeds""", """negative_image_embeds""", """image"""]
  a_							:       List[str]      					=[
      """image_embeds""",
      """negative_image_embeds""",
      """image""",
  ]
  a_							:       Dict      					=[
      """generator""",
      """height""",
      """width""",
      """strength""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  a_							:       Optional[Any]      					=False







  @property
  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return 32







  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    return 32







  @property
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    return self.time_input_dim







  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    return self.time_input_dim * 4







  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    return 1_00







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    torch.manual_seed(0							)

    _snake_case   :				Union[str, 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,
    }

    _snake_case   :				List[str]														=	UNetaDConditionModel(**UpperCamelCase							)
    return model







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''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 UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    torch.manual_seed(0							)
    _snake_case   :				Any														=	VQModel(**self.dummy_movq_kwargs							)
    return model







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	self.dummy_unet
    _snake_case   :				Tuple														=	self.dummy_movq

    _snake_case   :				int														=	{
        'num_train_timesteps': 10_00,
        '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,
    }

    _snake_case   :				int														=	DDIMScheduler(**UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	{
        'unet': unet,
        'scheduler': scheduler,
        'movq': movq,
    }

    return components







  def UpperCamelCase_ (      self      :					str      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Dict=0							):


    '''simple docstring'''





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

    if str(UpperCamelCase							).startswith('mps'							):
      _snake_case   :				Union[str, Any]														=	torch.manual_seed(UpperCamelCase							)
    else:
      _snake_case   :				Union[str, Any]														=	torch.Generator(device=UpperCamelCase							).manual_seed(UpperCamelCase							)
    _snake_case   :				Dict														=	{
        '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 UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	'cpu'

    _snake_case   :				str														=	self.get_dummy_components()

    _snake_case   :				Union[str, Any]														=	self.pipeline_class(**UpperCamelCase							)
    _snake_case   :				Tuple														=	pipe.to(UpperCamelCase							)

    pipe.set_progress_bar_config(disable=UpperCamelCase							)

    _snake_case   :				List[str]														=	pipe(**self.get_dummy_inputs(UpperCamelCase							)							)
    _snake_case   :				Any														=	output.images

    _snake_case   :				int														=	pipe(
        **self.get_dummy_inputs(UpperCamelCase							)      ,				return_dict=UpperCamelCase      ,				)[0]

    _snake_case   :				List[str]														=	image[0, -3:, -3:, -1]
    _snake_case   :				Any														=	image_from_tuple[0, -3:, -3:, -1]

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

    _snake_case   :				Union[str, Any]														=	np.array(
        [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]							)
    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							):


  '''simple docstring'''







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	load_numpy(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/kandinskyv22/kandinskyv22_img2img_frog.npy'							)

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

    _snake_case   :				List[Any]														=	KandinskyVaaPriorPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-prior'      ,				torch_dtype=torch.floataa							)
    pipe_prior.to(UpperCamelCase							)

    _snake_case   :				Dict														=	KandinskyVaaImgaImgPipeline.from_pretrained(
        'kandinsky-community/kandinsky-2-2-decoder'      ,				torch_dtype=torch.floataa							)
    _snake_case   :				Any														=	pipeline.to(UpperCamelCase							)

    pipeline.set_progress_bar_config(disable=UpperCamelCase							)

    _snake_case   :				Optional[Any]														=	torch.Generator(device='cpu'							).manual_seed(0							)
    _snake_case   :				List[str]														=	pipe_prior(
        UpperCamelCase      ,				generator=UpperCamelCase      ,				num_inference_steps=5      ,				negative_prompt=''      ,				).to_tuple()

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

    _snake_case   :				int														=	output.images[0]

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

    assert_mean_pixel_difference(UpperCamelCase      ,				UpperCamelCase							)











import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       Optional[Union[str, Path]]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=False
  a_							:       bool      					=True
  a_							:       Optional[int]      					=None
  a_							:       int      					=1
  a_							:       Optional[Union[str, bool]]      					=None
  a_							:       bool      					=False
  a_							:       Optional[Dict]      					=None
  a_							:       Optional[str]      					=None







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    return self.__class__(**{k: copy.deepcopy(UpperCamelCase							) for k, v in self.__dict__.items()}							)




