
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					"DiagonalGaussianDistribution"





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):
	UpperCamelCase_  :					Optional[Any]    		=	True







	@register_to_config
	def __init__(     self      :Tuple   ,     lowercase      :int = 3   ,     lowercase      :int = 3   ,     lowercase      :Tuple[str] = ("DownEncoderBlock2D",)   ,     lowercase      :Tuple[str] = ("UpDecoderBlock2D",)   ,     lowercase      :Tuple[int] = (6_4,)   ,     lowercase      :int = 1   ,     lowercase      :str = "silu"   ,     lowercase      :int = 4   ,     lowercase      :int = 3_2   ,     lowercase      :int = 3_2   ,     lowercase      :float = 0.1_82_15   ,     )		->      Optional[Any]:







				"""simple docstring"""






				super().__init__()

				# pass init params to Encoder
				SCREAMING_SNAKE_CASE     						= Encoder(
				    in_channels=lowercase   ,     out_channels=lowercase   ,     down_block_types=lowercase   ,     block_out_channels=lowercase   ,     layers_per_block=lowercase   ,     act_fn=lowercase   ,     norm_num_groups=lowercase   ,     double_z=lowercase   ,     )

				# pass init params to Decoder
				SCREAMING_SNAKE_CASE     						= Decoder(
				    in_channels=lowercase   ,     out_channels=lowercase   ,     up_block_types=lowercase   ,     block_out_channels=lowercase   ,     layers_per_block=lowercase   ,     norm_num_groups=lowercase   ,     act_fn=lowercase   ,     )

				SCREAMING_SNAKE_CASE     						= nn.Convad(2 * latent_channels   ,     2 * latent_channels   ,     1			)
				SCREAMING_SNAKE_CASE     						= nn.Convad(lowercase   ,     lowercase   ,     1			)

				SCREAMING_SNAKE_CASE     						= False
				SCREAMING_SNAKE_CASE     						= False

				# only relevant if vae tiling is enabled
				SCREAMING_SNAKE_CASE     						= self.config.sample_size
				SCREAMING_SNAKE_CASE     						= (
				    self.config.sample_size[0]
				    if isinstance(self.config.sample_size   ,     (list, tuple)			)
				    else self.config.sample_size
				)
				SCREAMING_SNAKE_CASE     						= int(sample_size / (2 ** (len(self.config.block_out_channels			) - 1))			)
				SCREAMING_SNAKE_CASE     						= 0.25







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Any   ,     lowercase      :Union[str, Any]=False			)		->      List[Any]:







				"""simple docstring"""






				if isinstance(lowercase   ,     (Encoder, Decoder)			):
							SCREAMING_SNAKE_CASE     						= value







	def  snake_case__     (     self      :List[str]   ,     lowercase      :bool = True			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= use_tiling







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







				"""simple docstring"""






				self.enable_tiling(lowercase			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= True







	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= False







	@property
	# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
	def  snake_case__     (     self      :Optional[Any]			)		->      Dict[str, AttentionProcessor]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {}

				def fn_recursive_add_processors(lowercase      :str   ,     lowercase      :torch.nn.Module   ,     lowercase      :Dict[str, AttentionProcessor]			):
							if hasattr(lowercase   ,     '''set_processor'''			):
										SCREAMING_SNAKE_CASE     						= module.processor

							for sub_name, child in module.named_children():
										fn_recursive_add_processors(f"""{name}.{sub_name}"""   ,     lowercase   ,     lowercase			)

							return processors

				for name, module in self.named_children():
							fn_recursive_add_processors(lowercase   ,     lowercase   ,     lowercase			)

				return processors







	def  snake_case__     (     self      :Any   ,     lowercase      :Union[AttentionProcessor, Dict[str, AttentionProcessor]]			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= len(self.attn_processors.keys()			)

				if isinstance(lowercase   ,     lowercase			) and len(lowercase			) != count:
							raise ValueError(
							    f"""A dict of processors was passed, but the number of processors {len(lowercase			)} does not match the"""
							    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""			)

				def fn_recursive_attn_processor(lowercase      :str   ,     lowercase      :torch.nn.Module   ,     lowercase      :List[str]			):
							if hasattr(lowercase   ,     '''set_processor'''			):
										if not isinstance(lowercase   ,     lowercase			):
													module.set_processor(lowercase			)
										else:
													module.set_processor(processor.pop(f"""{name}.processor"""			)			)

							for sub_name, child in module.named_children():
										fn_recursive_attn_processor(f"""{name}.{sub_name}"""   ,     lowercase   ,     lowercase			)

				for name, module in self.named_children():
							fn_recursive_attn_processor(lowercase   ,     lowercase   ,     lowercase			)







	def  snake_case__     (     self      :int			)		->      List[Any]:







				"""simple docstring"""






				self.set_attn_processor(AttnProcessor()			)







	@apply_forward_hook
	def  snake_case__     (     self      :str   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = True			)		->      AutoencoderKLOutput:







				"""simple docstring"""






				if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
							return self.tiled_encode(lowercase   ,     return_dict=lowercase			)

				if self.use_slicing and x.shape[0] > 1:
							SCREAMING_SNAKE_CASE     						= [self.encoder(lowercase			) for x_slice in x.split(1			)]
							SCREAMING_SNAKE_CASE     						= torch.cat(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self.encoder(lowercase			)

				SCREAMING_SNAKE_CASE     						= self.quant_conv(lowercase			)
				SCREAMING_SNAKE_CASE     						= DiagonalGaussianDistribution(lowercase			)

				if not return_dict:
							return (posterior,)

				return AutoencoderKLOutput(latent_dist=lowercase			)







	def  snake_case__     (     self      :int   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = True			)		->      Union[DecoderOutput, torch.FloatTensor]:







				"""simple docstring"""






				if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
							return self.tiled_decode(lowercase   ,     return_dict=lowercase			)

				SCREAMING_SNAKE_CASE     						= self.post_quant_conv(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.decoder(lowercase			)

				if not return_dict:
							return (dec,)

				return DecoderOutput(sample=lowercase			)







	@apply_forward_hook
	def  snake_case__     (     self      :List[Any]   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = True			)		->      Union[DecoderOutput, torch.FloatTensor]:







				"""simple docstring"""






				if self.use_slicing and z.shape[0] > 1:
							SCREAMING_SNAKE_CASE     						= [self._decode(lowercase			).sample for z_slice in z.split(1			)]
							SCREAMING_SNAKE_CASE     						= torch.cat(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self._decode(lowercase			).sample

				if not return_dict:
							return (decoded,)

				return DecoderOutput(sample=lowercase			)







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :List[Any]   ,     lowercase      :Optional[Any]   ,     lowercase      :int			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= min(a.shape[2]   ,     b.shape[2]   ,     lowercase			)
				for y in range(lowercase			):
							SCREAMING_SNAKE_CASE     						= a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
				return b







	def  snake_case__     (     self      :Tuple   ,     lowercase      :Tuple   ,     lowercase      :Tuple   ,     lowercase      :str			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= min(a.shape[3]   ,     b.shape[3]   ,     lowercase			)
				for x in range(lowercase			):
							SCREAMING_SNAKE_CASE     						= a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
				return b







	def  snake_case__     (     self      :Any   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = True			)		->      AutoencoderKLOutput:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)			)
				SCREAMING_SNAKE_CASE     						= int(self.tile_latent_min_size * self.tile_overlap_factor			)
				SCREAMING_SNAKE_CASE     						= self.tile_latent_min_size - blend_extent

				# Split the image into 512x512 tiles and encode them separately.
				SCREAMING_SNAKE_CASE     						= []
				for i in range(0   ,     x.shape[2]   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= []
							for j in range(0   ,     x.shape[3]   ,     lowercase			):
										SCREAMING_SNAKE_CASE     						= x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
										SCREAMING_SNAKE_CASE     						= self.encoder(lowercase			)
										SCREAMING_SNAKE_CASE     						= self.quant_conv(lowercase			)
										row.append(lowercase			)
							rows.append(lowercase			)
				SCREAMING_SNAKE_CASE     						= []
				for i, row in enumerate(lowercase			):
							SCREAMING_SNAKE_CASE     						= []
							for j, tile in enumerate(lowercase			):
										# blend the above tile and the left tile
										# to the current tile and add the current tile to the result row
										if i > 0:
													SCREAMING_SNAKE_CASE     						= self.blend_v(rows[i - 1][j]   ,     lowercase   ,     lowercase			)
										if j > 0:
													SCREAMING_SNAKE_CASE     						= self.blend_h(row[j - 1]   ,     lowercase   ,     lowercase			)
										result_row.append(tile[:, :, :row_limit, :row_limit]			)
							result_rows.append(torch.cat(lowercase   ,     dim=3			)			)

				SCREAMING_SNAKE_CASE     						= torch.cat(lowercase   ,     dim=2			)
				SCREAMING_SNAKE_CASE     						= DiagonalGaussianDistribution(lowercase			)

				if not return_dict:
							return (posterior,)

				return AutoencoderKLOutput(latent_dist=lowercase			)







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = True			)		->      Union[DecoderOutput, torch.FloatTensor]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)			)
				SCREAMING_SNAKE_CASE     						= int(self.tile_sample_min_size * self.tile_overlap_factor			)
				SCREAMING_SNAKE_CASE     						= self.tile_sample_min_size - blend_extent

				# Split z into overlapping 64x64 tiles and decode them separately.
				# The tiles have an overlap to avoid seams between tiles.
				SCREAMING_SNAKE_CASE     						= []
				for i in range(0   ,     z.shape[2]   ,     lowercase			):
							SCREAMING_SNAKE_CASE     						= []
							for j in range(0   ,     z.shape[3]   ,     lowercase			):
										SCREAMING_SNAKE_CASE     						= z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
										SCREAMING_SNAKE_CASE     						= self.post_quant_conv(lowercase			)
										SCREAMING_SNAKE_CASE     						= self.decoder(lowercase			)
										row.append(lowercase			)
							rows.append(lowercase			)
				SCREAMING_SNAKE_CASE     						= []
				for i, row in enumerate(lowercase			):
							SCREAMING_SNAKE_CASE     						= []
							for j, tile in enumerate(lowercase			):
										# blend the above tile and the left tile
										# to the current tile and add the current tile to the result row
										if i > 0:
													SCREAMING_SNAKE_CASE     						= self.blend_v(rows[i - 1][j]   ,     lowercase   ,     lowercase			)
										if j > 0:
													SCREAMING_SNAKE_CASE     						= self.blend_h(row[j - 1]   ,     lowercase   ,     lowercase			)
										result_row.append(tile[:, :, :row_limit, :row_limit]			)
							result_rows.append(torch.cat(lowercase   ,     dim=3			)			)

				SCREAMING_SNAKE_CASE     						= torch.cat(lowercase   ,     dim=2			)
				if not return_dict:
							return (dec,)

				return DecoderOutput(sample=lowercase			)







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :torch.FloatTensor   ,     lowercase      :bool = False   ,     lowercase      :bool = True   ,     lowercase      :Optional[torch.Generator] = None   ,     )		->      Union[DecoderOutput, torch.FloatTensor]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= sample
				SCREAMING_SNAKE_CASE     						= self.encode(lowercase			).latent_dist
				if sample_posterior:
							SCREAMING_SNAKE_CASE     						= posterior.sample(generator=lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= posterior.mode()
				SCREAMING_SNAKE_CASE     						= self.decode(lowercase			).sample

				if not return_dict:
							return (dec,)

				return DecoderOutput(sample=lowercase			)

import os


def    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


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