





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        __magic_name__	(				lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  KandinskyInpaintPipeline
     __UpperCamelCase            =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     __UpperCamelCase            =  [
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     __UpperCamelCase            =  [
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __UpperCamelCase            =  False



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        return 32



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        return 32



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





        '''simple docstring'''




        return self.time_input_dim



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        return self.time_input_dim * 4



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        return 100



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"	)
        return tokenizer



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





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      MCLIPConfig(
            numDims=self.cross_attention_dim      ,   transformerDimensions=self.text_embedder_hidden_size      ,   hidden_size=self.text_embedder_hidden_size      ,   intermediate_size=37      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   vocab_size=1_005      ,   )

        A_       :					Optional[int]        =      MultilingualCLIP(snake_case	)
        A_       :					Dict        =      text_encoder.eval()

        return text_encoder



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        torch.manual_seed(0	)

        A_       :					Optional[Any]        =      {
            "in_channels": 9,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "text_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": "text_image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        A_       :					List[Any]        =      UNetaDConditionModel(**snake_case	)
        return model



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





        '''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     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					List[str]        =      VQModel(**self.dummy_movq_kwargs	)
        return model



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





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.dummy_text_encoder
        A_       :					Union[str, Any]        =      self.dummy_tokenizer
        A_       :					List[Any]        =      self.dummy_unet
        A_       :					Any        =      self.dummy_movq

        A_       :					Dict        =      DDIMScheduler(
            num_train_timesteps=1_000      ,   beta_schedule="linear"      ,   beta_start=0.00085      ,   beta_end=0.012      ,   clip_sample=snake_case      ,   set_alpha_to_one=snake_case      ,   steps_offset=1      ,   prediction_type="epsilon"      ,   thresholding=snake_case      ,   )

        A_       :					Union[str, Any]        =      {
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }

        return components



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   snake_case							:Optional[Any]      ,   snake_case							:Union[str, Any]=0	):





        '''simple docstring'''




        A_       :					Any        =      floats_tensor((1, self.cross_attention_dim)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Tuple        =      floats_tensor((1, self.cross_attention_dim)      ,   rng=random.Random(seed + 1	)	).to(snake_case	)
        # create init_image
        A_       :					Any        =      floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      image.cpu().permute(0      ,   2      ,   3      ,   1	)[0]
        A_       :					Union[str, Any]        =      Image.fromarray(np.uinta(snake_case	)	).convert("RGB"	).resize((256, 256)	)
        # create mask
        A_       :					Dict        =      np.ones((64, 64)      ,   dtype=np.floataa	)
        A_       :					str        =      0

        if str(snake_case	).startswith("mps"	):
           A_       :					Any        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Dict        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)
        A_       :					int        =      {
            "prompt": "horse",
            "image": init_image,
            "mask_image": mask,
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "generator": generator,
            "height": 64,
            "width": 64,
            "num_inference_steps": 2,
            "guidance_scale": 4.0,
            "output_type": "np",
        }
        return inputs



     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_       :					Tuple        =      "cpu"

        A_       :					Optional[Any]        =      self.get_dummy_components()

        A_       :					List[str]        =      self.pipeline_class(**snake_case	)
        A_       :					Tuple        =      pipe.to(snake_case	)

        pipe.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      pipe(**self.get_dummy_inputs(snake_case	)	)
        A_       :					Union[str, Any]        =      output.images

        A_       :					Optional[Any]        =      pipe(
            **self.get_dummy_inputs(snake_case	)      ,   return_dict=snake_case      ,   )[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Optional[int]        =      image_from_tuple[0, -3:, -3:, -1]

        print(f"image.shape {image.shape}"	)

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

        A_       :					Optional[int]        =      np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]	)

        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()}"





     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




@slow
@require_torch_gpu
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




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





     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        A_       :					int        =      load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"	)

        A_       :					Optional[Any]        =      load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
        A_       :					Union[str, Any]        =      np.ones((768, 768)      ,   dtype=np.floataa	)
        A_       :					Union[str, Any]        =      0

        A_       :					Dict        =      "a hat"

        A_       :					Optional[int]        =      KandinskyPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-1-prior"      ,   torch_dtype=torch.floataa	)
        pipe_prior.to(snake_case	)

        A_       :					Union[str, Any]        =      KandinskyInpaintPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-1-inpaint"      ,   torch_dtype=torch.floataa	)
        A_       :					Tuple        =      pipeline.to(snake_case	)
        pipeline.set_progress_bar_config(disable=snake_case	)

        A_       :					Optional[Any]        =      torch.Generator(device="cpu"	).manual_seed(0	)
        A_   ,    A_       :					Tuple        =      pipe_prior(
            snake_case      ,   generator=snake_case      ,   num_inference_steps=5      ,   negative_prompt=""      ,   ).to_tuple()

        A_       :					List[Any]        =      pipeline(
            snake_case      ,   image=snake_case      ,   mask_image=snake_case      ,   image_embeds=snake_case      ,   negative_image_embeds=snake_case      ,   generator=snake_case      ,   num_inference_steps=100      ,   height=768      ,   width=768      ,   output_type="np"      ,   )

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

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

        assert_mean_pixel_difference(snake_case      ,   snake_case	)







from __future__ import annotations

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int						)			->    list[list[int]]:
   A_       :					list[list[int]]        =      []
   A_       :					list[int]        =      []
   A_       :					Dict        =      0
   A_       :					str        =      sum(_lowerCAmelCase						)
   create_state_space_tree(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   return result

def   __snake_case    (       _lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			list[int]					,					_lowerCAmelCase			:			list[list[int]]					,					_lowerCAmelCase			:			int					,					)			->    None:
   if sum(_lowerCAmelCase						) > max_sum or (remaining_nums_sum + sum(_lowerCAmelCase						)) < max_sum:
      return
   if sum(_lowerCAmelCase						) == max_sum:
      result.append(_lowerCAmelCase						)
      return
   for index in range(_lowerCAmelCase					,					len(_lowerCAmelCase						)						):
      create_state_space_tree(
          _lowerCAmelCase					,					_lowerCAmelCase					,					index + 1					,					[*path, nums[index]]					,					_lowerCAmelCase					,					remaining_nums_sum - nums[index]					,					)
_lowerCAmelCase				:   List[Any]						       =						[3, 34, 4, 12, 5, 2]
_lowerCAmelCase				:   Dict						       =						9
_lowerCAmelCase				:   Dict						       =						generate_sum_of_subsets_soln(nums, max_sum)





print(*result)
