"""simple docstring"""





import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			KandinskyVaaPriorPipeline
						snake_case__			  =			["prompt"]
						snake_case__			  =			["prompt", "negative_prompt"]
						snake_case__			  =			[
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						snake_case__			  =			False






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													return 32






						@property
						def       lowerCamelCase__     (       self      :			Any    ):
													return self.time_input_dim






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return self.time_input_dim * 4






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													return 100






						@property
						def       lowerCamelCase__     (       self      :			Tuple    ):
													__lowerCamelCase					:		str		   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
													return tokenizer






						@property
						def       lowerCamelCase__     (       self      :			str    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		Optional[Any]		   =     CLIPTextConfig(
													    bos_token_id=0							,					eos_token_id=2							,					hidden_size=self.text_embedder_hidden_size							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					layer_norm_eps=1E-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1000							,					)
													return CLIPTextModelWithProjection(UpperCAmelCase    )






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													torch.manual_seed(0    )

													__lowerCamelCase					:		Optional[int]		   =     {
													    "num_attention_heads": 2,
													    "attention_head_dim": 12,
													    "embedding_dim": self.text_embedder_hidden_size,
													    "num_layers": 1,
													}

													__lowerCamelCase					:		Dict		   =     PriorTransformer(**UpperCAmelCase    )
													# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
													__lowerCamelCase					:		Dict		   =     nn.Parameter(torch.ones(model.clip_std.shape    )    )
													return model






						@property
						def       lowerCamelCase__     (       self      :			Dict    ):
													torch.manual_seed(0    )
													__lowerCamelCase					:		Tuple		   =     CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size							,					image_size=224							,					projection_dim=self.text_embedder_hidden_size							,					intermediate_size=37							,					num_attention_heads=4							,					num_channels=3							,					num_hidden_layers=5							,					patch_size=14							,					)

													__lowerCamelCase					:		List[Any]		   =     CLIPVisionModelWithProjection(UpperCAmelCase    )
													return model






						@property
						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		Union[str, Any]		   =     CLIPImageProcessor(
													    crop_size=224							,					do_center_crop=UpperCAmelCase							,					do_normalize=UpperCAmelCase							,					do_resize=UpperCAmelCase							,					image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,					image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,					resample=3							,					size=224							,					)

													return image_processor






						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Tuple		   =     self.dummy_prior
													__lowerCamelCase					:		Optional[Any]		   =     self.dummy_image_encoder
													__lowerCamelCase					:		List[Any]		   =     self.dummy_text_encoder
													__lowerCamelCase					:		Optional[int]		   =     self.dummy_tokenizer
													__lowerCamelCase					:		int		   =     self.dummy_image_processor

													__lowerCamelCase					:		Optional[Any]		   =     UnCLIPScheduler(
													    variance_type="fixed_small_log"							,					prediction_type="sample"							,					num_train_timesteps=1000							,					clip_sample=UpperCAmelCase							,					clip_sample_range=1_0.0							,					)

													__lowerCamelCase					:		Union[str, Any]		   =     {
													    "prior": prior,
													    "image_encoder": image_encoder,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "scheduler": scheduler,
													    "image_processor": image_processor,
													}

													return components






						def       lowerCamelCase__     (       self      :			Optional[Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			Optional[int]=0    ):
													if str(UpperCAmelCase    ).startswith("mps"    ):
																				__lowerCamelCase					:		Optional[Any]		   =     torch.manual_seed(UpperCAmelCase    )
													else:
																				__lowerCamelCase					:		Tuple		   =     torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
													__lowerCamelCase					:		int		   =     {
													    "prompt": "horse",
													    "generator": generator,
													    "guidance_scale": 4.0,
													    "num_inference_steps": 2,
													    "output_type": "np",
													}
													return inputs






						def       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Union[str, Any]		   =     "cpu"

													__lowerCamelCase					:		Union[str, Any]		   =     self.get_dummy_components()

													__lowerCamelCase					:		List[str]		   =     self.pipeline_class(**UpperCAmelCase    )
													__lowerCamelCase					:		Tuple		   =     pipe.to(UpperCAmelCase    )

													pipe.set_progress_bar_config(disable=UpperCAmelCase    )

													__lowerCamelCase					:		Optional[Any]		   =     pipe(**self.get_dummy_inputs(UpperCAmelCase    )    )
													__lowerCamelCase					:		int		   =     output.image_embeds

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

													__lowerCamelCase					:		str		   =     image[0, -10:]
													__lowerCamelCase					:		Dict		   =     image_from_tuple[0, -10:]

													assert image.shape == (1, 32)

													__lowerCamelCase					:		str		   =     np.array(
													    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]    )

													assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2






						@skip_mps
						def       lowerCamelCase__     (       self      :			int    ):
													__lowerCamelCase					:		Tuple		   =     torch_device == "cpu"
													__lowerCamelCase					:		Optional[int]		   =     True
													__lowerCamelCase					:		Any		   =     False

													self._test_inference_batch_single_identical(
													    test_max_difference=UpperCAmelCase							,					relax_max_difference=UpperCAmelCase							,					test_mean_pixel_difference=UpperCAmelCase							,					)







						@skip_mps
						def       lowerCamelCase__     (       self      :			List[str]    ):
													__lowerCamelCase					:		Optional[int]		   =     torch_device == "cpu"
													__lowerCamelCase					:		List[Any]		   =     False

													self._test_attention_slicing_forward_pass(
													    test_max_difference=UpperCAmelCase							,					test_mean_pixel_difference=UpperCAmelCase							,					)
"""simple docstring"""





import os


def    lowercase_   (   ) ->      List[str]:



							'''simple docstring'''

							__lowerCamelCase					:		Union[str, Any]		   =     os.path.dirname(os.path.realpath(_lowerCamelCase      )      )
							__lowerCamelCase					:		int		   =     os.path.join(_lowerCamelCase						,   "triangle.txt"      )

							with open(_lowerCamelCase      ) as f:
														__lowerCamelCase					:		List[str]		   =     f.readlines()

							__lowerCamelCase					:		List[str]		   =     []
							for line in triangle:
														__lowerCamelCase					:		Optional[int]		   =     []
														for number in line.strip().split(" "      ):
																					numbers_from_line.append(int(_lowerCamelCase      )      )
														a.append(_lowerCamelCase      )

							for i in range(1						,   len(_lowerCamelCase      )      ):
														for j in range(len(a[i]      )      ):
																					__lowerCamelCase					:		int		   =     a[i - 1][j] if j != len(a[i - 1]      ) else 0
																					__lowerCamelCase					:		Union[str, Any]		   =     a[i - 1][j - 1] if j > 0 else 0
																					a[i][j] += max(_lowerCamelCase						,   _lowerCamelCase      )
							return max(a[-1]      )


if __name__ == "__main__":
				print(solution())