

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						(							_UpperCAmelCase   ,							unittest.TestCase			):







    """simple docstring"""





    lowerCamelCase		     =							KandinskyVaaControlnetImgaImgPipeline
    lowerCamelCase		     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowerCamelCase		     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowerCamelCase		     =							[
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowerCamelCase		     =							False


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





        '''simple docstring'''







        return 3_2


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





        '''simple docstring'''







        return 3_2


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





        '''simple docstring'''







        return self.time_input_dim


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





        '''simple docstring'''







        return self.time_input_dim * 4


    @property
    def 						snake_case__					(				self			:	Tuple		)->   Optional[int]:





        '''simple docstring'''







        return 1_0_0


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





        '''simple docstring'''







        torch.manual_seed(0		)

        A__   				=       {
            '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,
        }

        A__   				=       UNetaDConditionModel(**lowercase_		)
        return model


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





        '''simple docstring'''







        return {
            "block_out_channels": [3_2, 3_2, 6_4, 6_4],
            "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": 1_2,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }


    @property
    def 						snake_case__					(				self			:	str		)->   int:





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__   				=       self.dummy_unet
        A__   				=       self.dummy_movq

        A__   				=       {
            'num_train_timesteps': 1_0_0_0,
            'beta_schedule': 'linear',
            'beta_start': 0.00_085,
            'beta_end': 0.012,
            'clip_sample': False,
            'set_alpha_to_one': False,
            'steps_offset': 0,
            'prediction_type': 'epsilon',
            'thresholding': False,
        }

        A__   				=       DDIMScheduler(**lowercase_		)

        A__   				=       {
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components


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





        '''simple docstring'''







        A__   				=       floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(lowercase_		)		).to(lowercase_		)
        A__   				=       floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(seed + 1		)		).to(
            lowercase_		)
        # create init_image
        A__   				=       floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(lowercase_		)		).to(lowercase_		)
        A__   				=       image.cpu().permute(0,2,3,1		)[0]
        A__   				=       Image.fromarray(np.uinta(lowercase_		)		).convert('RGB'		).resize((2_5_6, 2_5_6)		)
        # create hint
        A__   				=       floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(lowercase_		)		).to(lowercase_		)

        if str(lowercase_		).startswith('mps'		):
            A__   				=       torch.manual_seed(lowercase_		)
        else:
            A__   				=       torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
        A__   				=       {
            'image': init_image,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 6_4,
            'width': 6_4,
            'num_inference_steps': 1_0,
            'guidance_scale': 7.0,
            'strength': 0.2,
            'output_type': 'np',
        }
        return inputs


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





        '''simple docstring'''







        A__   				=       'cpu'

        A__   				=       self.get_dummy_components()

        A__   				=       self.pipeline_class(**lowercase_		)
        A__   				=       pipe.to(lowercase_		)

        pipe.set_progress_bar_config(disable=lowercase_		)

        A__   				=       pipe(**self.get_dummy_inputs(lowercase_		)		)
        A__   				=       output.images

        A__   				=       pipe(
            **self.get_dummy_inputs(lowercase_		),return_dict=lowercase_,)[0]

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

        assert image.shape == (1, 6_4, 6_4, 3)

        A__   				=       np.array(
            [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]		)
        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 						snake_case__					(				self			:	str		)->   Optional[int]:





        '''simple docstring'''







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


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





        '''simple docstring'''







        A__   				=       load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'		)

        A__   				=       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'		)
        A__   				=       init_image.resize((5_1_2, 5_1_2)		)

        A__   				=       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png'		)
        A__   				=       torch.from_numpy(np.array(lowercase_		)		).float() / 255.0
        A__   				=       hint.permute(2,0,1		).unsqueeze(0		)

        A__   				=       'A robot, 4k photo'

        A__   				=       KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior',torch_dtype=torch.floataa		)
        pipe_prior.to(lowercase_		)

        A__   				=       KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth',torch_dtype=torch.floataa		)
        A__   				=       pipeline.to(lowercase_		)

        pipeline.set_progress_bar_config(disable=lowercase_		)

        A__   				=       torch.Generator(device='cpu'		).manual_seed(0		)

        A__				,						A__   				=       pipe_prior(
            lowercase_,image=lowercase_,strength=0.85,generator=lowercase_,negative_prompt='',).to_tuple()

        A__   				=       pipeline(
            image=lowercase_,image_embeds=lowercase_,negative_image_embeds=lowercase_,hint=lowercase_,generator=lowercase_,num_inference_steps=1_0_0,height=5_1_2,width=5_1_2,strength=0.5,output_type='np',)

        A__   				=       output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        assert_mean_pixel_difference(lowercase_,lowercase_		)




from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

lowercase_			=  (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_			=  (4, 6, 2, 0, 8, 10, 3, -2)


@dataclass
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42




class 			A						:







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Iterable[int]		)->   None:





        '''simple docstring'''







        A__   				=       None
        for i in sorted(lowercase_,reverse=lowercase_		):
            A__   				=       Node(lowercase_,self.head		)


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





        '''simple docstring'''







        A__   				=       self.head
        while node:
            yield node.data
            A__   				=       node.next_node


    def __len__(				self			:	str		)->   int:





        '''simple docstring'''







        return sum(1 for _ in self		)


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





        '''simple docstring'''







        return " -> ".join([str(lowercase_		) for node in self]		)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     SortedLinkedList   ,      SCREAMING_SNAKE_CASE__      :     SortedLinkedList				)							->				SortedLinkedList:





    '''simple docstring'''






    return SortedLinkedList(list(SCREAMING_SNAKE_CASE__				) + list(SCREAMING_SNAKE_CASE__				)				)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     lowercase_			=  SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

