


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     _lowercase				(							lowercase__							,						unittest.TestCase):


							"""simple docstring"""




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





							@property
							def 		lowerCAmelCase  (     self							:    str ):


													'''simple docstring'''



													return 32





							@property
							def 		lowerCAmelCase  (     self							:    int ):


													'''simple docstring'''



													return 32





							@property
							def 		lowerCAmelCase  (     self							:    Tuple ):


													'''simple docstring'''



													return self.time_input_dim





							@property
							def 		lowerCAmelCase  (     self							:    List[str] ):


													'''simple docstring'''



													return self.time_input_dim * 4





							@property
							def 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													return 100





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


													'''simple docstring'''



													torch.manual_seed(0 )

													lowerCamelCase__	:       Any					=						{
													    "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,
													}

													lowerCamelCase__	:       str					=						UNetaDConditionModel(**__lowerCamelCase )
													return model





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


													'''simple docstring'''



													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 		lowerCAmelCase  (     self							:    Optional[Any] ):


													'''simple docstring'''



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





							def 		lowerCAmelCase  (     self							:    List[str] ):


													'''simple docstring'''



													lowerCamelCase__	:       Dict					=						self.dummy_unet
													lowerCamelCase__	:       Union[str, Any]					=						self.dummy_movq

													lowerCamelCase__	:       Optional[Any]					=						{
													    "num_train_timesteps": 1000,
													    "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,
													}

													lowerCamelCase__	:       List[Any]					=						DDIMScheduler(**__lowerCamelCase )

													lowerCamelCase__	:       Dict					=						{
													    "unet": unet,
													    "scheduler": scheduler,
													    "movq": movq,
													}

													return components





							def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Union[str, Any]=0 ):


													'''simple docstring'''



													lowerCamelCase__	:       List[Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
													lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1 ) ).to(
													    __lowerCamelCase )
													# create init_image
													lowerCamelCase__	:       List[str]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
													lowerCamelCase__	:       str					=						image.cpu().permute(0      ,   2      ,   3      ,   1 )[0]
													lowerCamelCase__	:       Optional[Any]					=						Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )
													# create hint
													lowerCamelCase__	:       Union[str, Any]					=						floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )

													if str(__lowerCamelCase ).startswith("mps" ):
																			lowerCamelCase__	:       List[str]					=						torch.manual_seed(__lowerCamelCase )
													else:
																			lowerCamelCase__	:       str					=						torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
													lowerCamelCase__	:       str					=						{
													    "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 		lowerCAmelCase  (     self							:    Any ):


													'''simple docstring'''



													lowerCamelCase__	:       Any					=						"cpu"

													lowerCamelCase__	:       Any					=						self.get_dummy_components()

													lowerCamelCase__	:       Union[str, Any]					=						self.pipeline_class(**__lowerCamelCase )
													lowerCamelCase__	:       str					=						pipe.to(__lowerCamelCase )

													pipe.set_progress_bar_config(disable=__lowerCamelCase )

													lowerCamelCase__	:       str					=						pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
													lowerCamelCase__	:       List[str]					=						output.images

													lowerCamelCase__	:       int					=						pipe(
													    **self.get_dummy_inputs(__lowerCamelCase )      ,   return_dict=__lowerCamelCase      ,   )[0]

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

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

													lowerCamelCase__	:       Any					=						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     _lowercase				(							unittest.TestCase):


							"""simple docstring"""




							def 		lowerCAmelCase  (     self							:    Dict ):


													'''simple docstring'''



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





							def 		lowerCAmelCase  (     self							:    int ):


													'''simple docstring'''



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

													lowerCamelCase__	:       Tuple					=						load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
													lowerCamelCase__	:       List[str]					=						init_image.resize((512, 512) )

													lowerCamelCase__	:       Optional[Any]					=						load_image(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/kandinskyv22/hint_image_cat.png" )
													lowerCamelCase__	:       Optional[Any]					=						torch.from_numpy(np.array(__lowerCamelCase ) ).float() / 255.0
													lowerCamelCase__	:       Optional[Any]					=						hint.permute(2      ,   0      ,   1 ).unsqueeze(0 )

													lowerCamelCase__	:       Optional[Any]					=						"A robot, 4k photo"

													lowerCamelCase__	:       str					=						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-2-prior"      ,   torch_dtype=torch.floataa )
													pipe_prior.to(__lowerCamelCase )

													lowerCamelCase__	:       int					=						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-2-controlnet-depth"      ,   torch_dtype=torch.floataa )
													lowerCamelCase__	:       List[Any]					=						pipeline.to(__lowerCamelCase )

													pipeline.set_progress_bar_config(disable=__lowerCamelCase )

													lowerCamelCase__	:       str					=						torch.Generator(device="cpu" ).manual_seed(0 )

													lowerCamelCase__	:       List[str]					=						pipe_prior(
													    __lowerCamelCase      ,   image=__lowerCamelCase      ,   strength=0.8_5      ,   generator=__lowerCamelCase      ,   negative_prompt=""      ,   ).to_tuple()

													lowerCamelCase__	:       Dict					=						pipeline(
													    image=__lowerCamelCase      ,   image_embeds=__lowerCamelCase      ,   negative_image_embeds=__lowerCamelCase      ,   hint=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   num_inference_steps=100      ,   height=512      ,   width=512      ,   strength=0.5      ,   output_type="np"      ,   )

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

													assert image.shape == (512, 512, 3)

													assert_mean_pixel_difference(__lowerCamelCase      ,   __lowerCamelCase )






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


