





'''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    UpperCAmelCase	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				KandinskyVaaControlnetImgaImgPipeline
 __lowercase   		=				["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 __lowercase   		=				["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
 __lowercase   		=				[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							List[str]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							Any:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							List[Any]:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[Any]:
     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      UpperCAmelCase_							(				self      :List[str]							)->							Dict:
     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      UpperCAmelCase_							(				self      :Optional[Any]							)->							Any:
     torch.manual_seed(0							)
     A__				=			VQModel(**self.dummy_movq_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							List[str]:
     A__				=			self.dummy_unet
     A__				=			self.dummy_movq

     A__				=			{
         "num_train_timesteps": 10_00,
         "beta_schedule": "linear",
         "beta_start": 0.0_0_0_8_5,
         "beta_end": 0.0_1_2,
         "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      UpperCAmelCase_							(				self      :int						,       lowercase_      :Any						,       lowercase_      :Optional[Any]=0							)->							Union[str, Any]:
     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, 64, 64)						,       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_56, 2_56)							)
     # create hint
     A__				=			floats_tensor((1, 3, 64, 64)						,       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": 64,
         "width": 64,
         "num_inference_steps": 10,
         "guidance_scale": 7.0,
         "strength": 0.2,
         "output_type": "np",
     }
     return inputs



 def      UpperCAmelCase_							(				self      :Optional[int]							)->							int:
     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, 64, 64, 3)

     A__				=			np.array(
         [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]							)
     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      UpperCAmelCase_							(				self      :str							)->							Dict:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :int							)->							Optional[Any]:
     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_12, 5_12)							)

     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() / 2_5_5.0
     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=0.8_5						,       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_00						,       height=5_12						,       width=5_12						,       strength=0.5						,       output_type="np"						,       )

     A__				=			output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowercase_						,       lowercase_							)










'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]       ):
    create_state_space_tree(_lowerCamelCase						,       []						,       0						,       [0 for i in range(len(_lowerCamelCase       )       )]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					list[int]						,       ):

    if index == len(_lowerCamelCase       ):
        print(_lowerCamelCase       )
        return

    for i in range(len(_lowerCamelCase       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            A__				=			True
            create_state_space_tree(_lowerCamelCase						,       _lowerCamelCase						,       index + 1						,       _lowerCamelCase       )
            current_sequence.pop()
            A__				=			False
__lowerCAmelCase							:	list[int | str]			=[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCAmelCase							:	list[int | str]			=["A", "B", "C"]



generate_all_permutations(sequence_a)



