'''simple docstring'''




import functools






def 							snake_case__				(     _A:   list[int] ,  _A:   list[int]					)       ->      int:

		'''simple docstring'''


		if not isinstance(_A ,  _A					) or not all(isinstance(_A ,  _A					) for day in days					):
				raise ValueError("""The parameter days should be a list of integers"""					)

		if len(_A					) != 3 or not all(isinstance(_A ,  _A					) for cost in costs					):
				raise ValueError("""The parameter costs should be a list of three integers"""					)

		if len(_A					) == 0:
				return 0

		if min(_A					) <= 0:
				raise ValueError("""All days elements should be greater than 0"""					)

		if max(_A					) >= 366:
				raise ValueError("""All days elements should be less than 366"""					)

		lowerCAmelCase     =    set(_A					)

		@functools.cache
		def dynamic_programming(_A:   int					) -> int:
				if index > 365:
						return 0

				if index not in days_set:
						return dynamic_programming(index + 1					)

				return min(
				    costs[0] + dynamic_programming(index + 1					) ,  costs[1] + dynamic_programming(index + 7					) ,  costs[2] + dynamic_programming(index + 30					) ,  )

		return dynamic_programming(1					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





'''simple docstring'''




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 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	"DiagonalGaussianDistribution"







class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			True



			@register_to_config
			def __init__(  self			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = ("DownEncoderBlock2D",)			,       __lowerCAmelCase = ("UpDecoderBlock2D",)			,       __lowerCAmelCase = (64,)			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = "silu"			,       __lowerCAmelCase = 4			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 0.18215			,       ):




					"""simple docstring"""


					super().__init__()

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

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

					lowerCAmelCase     =    nn.Convad(2 * latent_channels			,       2 * latent_channels			,       1)
					lowerCAmelCase     =    nn.Convad(__lowerCAmelCase			,       __lowerCAmelCase			,       1)

					lowerCAmelCase     =    False
					lowerCAmelCase     =    False

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=False):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       (Encoder, Decoder)):
							lowerCAmelCase     =    value



			def 				a_			(  self			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    use_tiling



			def 				a_			(  self):




					"""simple docstring"""


					self.enable_tiling(__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    True



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    False



			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {}

					def fn_recursive_add_processors(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									lowerCAmelCase     =    module.processor

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

							return processors

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

					return processors



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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

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

					def fn_recursive_attn_processor(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									if not isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
											module.set_processor(__lowerCAmelCase)
									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}"			,       __lowerCAmelCase			,       __lowerCAmelCase)

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



			def 				a_			(  self):




					"""simple docstring"""


					self.set_attn_processor(AttnProcessor())



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""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(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

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

					lowerCAmelCase     =    self.quant_conv(__lowerCAmelCase)
					lowerCAmelCase     =    DiagonalGaussianDistribution(__lowerCAmelCase)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""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(__lowerCAmelCase			,       return_dict=__lowerCAmelCase)

					lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
					lowerCAmelCase     =    self.decoder(__lowerCAmelCase)

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


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

					if not return_dict:
							return (decoded,)

					return DecoderOutput(sample=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


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

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

					lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=2)
					lowerCAmelCase     =    DiagonalGaussianDistribution(__lowerCAmelCase)

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
					lowerCAmelCase     =    int(self.tile_sample_min_size * self.tile_overlap_factor)
					lowerCAmelCase     =    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.
					lowerCAmelCase     =    []
					for i in range(0			,       z.shape[2]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       z.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
									lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
									lowerCAmelCase     =    self.decoder(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							for j, tile in enumerate(__lowerCAmelCase):
									# blend the above tile and the left tile
									# to the current tile and add the current tile to the result row
									if i > 0:
											lowerCAmelCase     =    self.blend_v(rows[i - 1][j]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

					lowerCAmelCase     =    torch.cat(__lowerCAmelCase			,       dim=2)
					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = False			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


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

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)




