


def 				UpperCamelCase_(      lowerCamelCase_ )    ->						float:
    _lowercase       :							Dict      		=		0
    while len(lowerCamelCase_ ) > 1:
        _lowercase       :							Dict      		=		0
        # Consider two files with minimum cost to be merged
        for _ in range(2 ):
            _lowercase       :							Union[str, Any]      		=		files.index(min(lowerCamelCase_ ) )
            temp += files[min_index]
            files.pop(lowerCamelCase_ )
        files.append(lowerCamelCase_ )
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()






import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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 _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Any     =			KandinskyImgaImgPipeline
       lowercase_ :	Union[str, Any]     =			["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
       lowercase_ :	Any     =			[
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
       ]
       lowercase_ :	List[Any]     =			[
           """generator""",
           """height""",
           """width""",
           """strength""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       lowercase_ :	Union[str, Any]     =			False
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return 32
       @property
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           return self.time_input_dim
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           return self.time_input_dim * 4
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           return 1_00
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							str      		=		XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base')
           return tokenizer
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[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=10_05,      )

           _lowercase       :							Optional[int]      		=		MultilingualCLIP(lowerCamelCase)
           _lowercase       :							List[str]      		=		text_encoder.eval()

           return text_encoder
       @property
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           torch.manual_seed(0)

           _lowercase       :							Union[str, Any]      		=		{
               'in_channels': 4,
               # 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,
           }

           _lowercase       :							Optional[Any]      		=		UNetaDConditionModel(**lowerCamelCase)
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """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)
           _lowercase       :							Dict      		=		VQModel(**self.dummy_movq_kwargs)
           return model
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Any      		=		self.dummy_text_encoder
           _lowercase       :							List[Any]      		=		self.dummy_tokenizer
           _lowercase       :							int      		=		self.dummy_unet
           _lowercase       :							int      		=		self.dummy_movq

           _lowercase       :							Optional[int]      		=		{
               '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,
           }

           _lowercase       :							List[Any]      		=		DDIMScheduler(**lowerCamelCase)

           _lowercase       :							List[Any]      		=		{
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'unet': unet,
               'scheduler': scheduler,
               'movq': movq,
           }

           return components
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=0)       ->   Dict:




           """simple docstring"""

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

           if str(lowerCamelCase).startswith('mps'):
               _lowercase       :							List[str]      		=		torch.manual_seed(lowerCamelCase)
           else:
               _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(lowerCamelCase)
           _lowercase       :							Tuple      		=		{
               'prompt': 'horse',
               '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)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Dict      		=		'cpu'

           _lowercase       :							Tuple      		=		self.get_dummy_components()

           _lowercase       :							str      		=		self.pipeline_class(**lowerCamelCase)
           _lowercase       :							str      		=		pipe.to(lowerCamelCase)

           pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							List[str]      		=		pipe(**self.get_dummy_inputs(lowerCamelCase))
           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							List[Any]      		=		pipe(
               **self.get_dummy_inputs(lowerCamelCase),      return_dict=lowerCamelCase,      )[0]

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

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

           _lowercase       :							Tuple      		=		np.array(
               [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
           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   ):
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           _lowercase       :							int      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/kandinsky/kandinsky_img2img_frog.npy')

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

           _lowercase       :							Union[str, Any]      		=		KandinskyPriorPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1-prior',      torch_dtype=torch.floataa)
           pipe_prior.to(lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		KandinskyImgaImgPipeline.from_pretrained(
               'kandinsky-community/kandinsky-2-1',      torch_dtype=torch.floataa)
           _lowercase       :							List[Any]      		=		pipeline.to(lowerCamelCase)

           pipeline.set_progress_bar_config(disable=lowerCamelCase)

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

           _lowercase       :							Union[str, Any]      		=		pipeline(
               lowerCamelCase,      image=lowerCamelCase,      image_embeds=lowerCamelCase,      negative_image_embeds=lowerCamelCase,      generator=lowerCamelCase,      num_inference_steps=1_00,      height=7_68,      width=7_68,      strength=0.2,      output_type='np',      )

           _lowercase       :							Dict      		=		output.images[0]

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

           assert_mean_pixel_difference(lowerCamelCase,      lowerCamelCase)


