

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        _SCREAMING_SNAKE_CASE	(   _UpperCAmelCase      ,  unittest.TestCase):
						_UpperCamelCase:Optional[Any]     =						KandinskyImgaImgPipeline
						_UpperCamelCase:Any     =						["prompt", "image_embeds", "negative_image_embeds", "image"]
						_UpperCamelCase:Union[str, Any]     =						[
						    "prompt",
						    "negative_prompt",
						    "image_embeds",
						    "negative_image_embeds",
						    "image",
						]
						_UpperCamelCase:Optional[Any]     =						[
						    "generator",
						    "height",
						    "width",
						    "strength",
						    "guidance_scale",
						    "negative_prompt",
						    "num_inference_steps",
						    "return_dict",
						    "guidance_scale",
						    "num_images_per_prompt",
						    "output_type",
						    "return_dict",
						]
						_UpperCamelCase:List[Any]     =						False







						@property
						def     _snake_case							(       self				)->				Dict:
													return 32







						@property
						def     _snake_case							(       self				)->				Union[str, Any]:
													return 32







						@property
						def     _snake_case							(       self				)->				List[Any]:
													return self.time_input_dim







						@property
						def     _snake_case							(       self				)->				Dict:
													return self.time_input_dim * 4







						@property
						def     _snake_case							(       self				)->				Optional[int]:
													return 100







						@property
						def     _snake_case							(       self				)->				Optional[int]:
													lowerCamelCase_				     =XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""				)
													return tokenizer







						@property
						def     _snake_case							(       self				)->				Optional[Any]:
													torch.manual_seed(0				)
													lowerCamelCase_				     =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=1005			,		)

													lowerCamelCase_				     =MultilingualCLIP(lowercase_				)
													lowerCamelCase_				     =text_encoder.eval()

													return text_encoder







						@property
						def     _snake_case							(       self				)->				Union[str, Any]:
													torch.manual_seed(0				)

													lowerCamelCase_				     ={
													    """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,
													}

													lowerCamelCase_				     =UNetaDConditionModel(**lowercase_				)
													return model







						@property
						def     _snake_case							(       self				)->				str:
													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     _snake_case							(       self				)->				Union[str, Any]:
													torch.manual_seed(0				)
													lowerCamelCase_				     =VQModel(**self.dummy_movq_kwargs				)
													return model







						def     _snake_case							(       self				)->				Tuple:
													lowerCamelCase_				     =self.dummy_text_encoder
													lowerCamelCase_				     =self.dummy_tokenizer
													lowerCamelCase_				     =self.dummy_unet
													lowerCamelCase_				     =self.dummy_movq

													lowerCamelCase_				     ={
													    """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_				     =DDIMScheduler(**lowercase_				)

													lowerCamelCase_				     ={
													    """text_encoder""": text_encoder,
													    """tokenizer""": tokenizer,
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """movq""": movq,
													}

													return components







						def     _snake_case							(       self			,		_SCREAMING_SNAKE_CASE			,		_SCREAMING_SNAKE_CASE=0				)->				Any:
													lowerCamelCase_				     =floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(lowercase_				)				).to(lowercase_				)
													lowerCamelCase_				     =floats_tensor((1, self.cross_attention_dim)			,		rng=random.Random(seed + 1				)				).to(lowercase_				)
													# create init_image
													lowerCamelCase_				     =floats_tensor((1, 3, 64, 64)			,		rng=random.Random(lowercase_				)				).to(lowercase_				)
													lowerCamelCase_				     =image.cpu().permute(0			,		2			,		3			,		1				)[0]
													lowerCamelCase_				     =Image.fromarray(np.uinta(lowercase_				)				).convert("""RGB"""				).resize((256, 256)				)

													if str(lowercase_				).startswith("""mps"""				):
																				lowerCamelCase_				     =torch.manual_seed(lowercase_				)
													else:
																				lowerCamelCase_				     =torch.Generator(device=lowercase_				).manual_seed(lowercase_				)
													lowerCamelCase_				     ={
													    """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     _snake_case							(       self				)->				Optional[int]:
													lowerCamelCase_				     ="""cpu"""

													lowerCamelCase_				     =self.get_dummy_components()

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

													pipe.set_progress_bar_config(disable=lowercase_				)

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

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

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

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

													lowerCamelCase_				     =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        _SCREAMING_SNAKE_CASE	(   unittest.TestCase):







						def     _snake_case							(       self				)->				int:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def     _snake_case							(       self				)->				str:
													lowerCamelCase_				     =load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinsky/kandinsky_img2img_frog.npy"""				)

													lowerCamelCase_				     =load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""				)
													lowerCamelCase_				     ="""A red cartoon frog, 4k"""

													lowerCamelCase_				     =KandinskyPriorPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-1-prior"""			,		torch_dtype=torch.floataa				)
													pipe_prior.to(lowercase_				)

													lowerCamelCase_				     =KandinskyImgaImgPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-1"""			,		torch_dtype=torch.floataa				)
													lowerCamelCase_				     =pipeline.to(lowercase_				)

													pipeline.set_progress_bar_config(disable=lowercase_				)

													lowerCamelCase_				     =torch.Generator(device="""cpu"""				).manual_seed(0				)
													lowerCamelCase_				     =pipe_prior(
													    lowercase_			,		generator=lowercase_			,		num_inference_steps=5			,		negative_prompt=""""""			,		).to_tuple()

													lowerCamelCase_				     =pipeline(
													    lowercase_			,		image=lowercase_			,		image_embeds=lowercase_			,		negative_image_embeds=lowercase_			,		generator=lowercase_			,		num_inference_steps=100			,		height=768			,		width=768			,		strength=0.2			,		output_type="""np"""			,		)

													lowerCamelCase_				     =output.images[0]

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

													assert_mean_pixel_difference(lowercase_			,		lowercase_				)

'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

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

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       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() = }""")





